AI4Peopleâan ethical framework for a good AI society: opportunities, risks, principles, and recommendations

Philosophy

Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 1 Forthcoming in Minds and Machines, December 2018 AI4People – An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations Luciano Floridi1,2,*, Josh Cowls1,2, Monica Beltrametti3, Raja Chatila4,5, Patrice Chazerand6, Virginia Dignum7, 8, Christoph Luetge9, Robert Madelin10, Ugo Pagallo11, Francesca Rossi12,13, Burkhard Schafer14, Peggy Valcke15,16, and Effy Vayena17. 1Oxford Internet Institute, University of Oxford, Oxford, United Kingdom. 2The Alan Turing Institute, London, United Kingdom. 3Naver Corporation, Grenoble, France. 4French National Center of Scientific Research, France. 5Institute of Intelligent Systems and Robotics at Pierre and Marie Curie University, Paris, France. 6Digital Europe, Brussels, Belgium. 7University of Umeå, Umeå, Sweden. 8Delft Design for Values Institute, Delft University of Technology, Delft, the Netherlands. 9TUM School of Governance, Technical University of Munich, Munich, Germany. 10Centre for Technology and Global Affairs, University of Oxford, Oxford, United Kingdom. 11Department of Law, University of Turin, Turin, Italy. 12IBM Research, United States. 13University of Padova, Padova, Italy. 14University of Edinburgh Law School, Edinburgh, United Kingdom. 15Centre for IT & IP Law, Catholic University of Leuven, Flanders, Belgium. 16Bocconi University, Milan, Italy. 17Bioethics, Health Ethics and Policy Lab, ETH Zurich, Zurich, Switzerland. *Corresponding author: email@example.com Abstract This article reports the findings of AI4People, an Atomium – EISMD initiative designed to lay the foundations for a "Good AI Society". We introduce the core opportunities and risks of AI for society; present a synthesis of five ethical principles that should undergird its development and adoption; and offer 20 concrete recommendations – to assess, to develop, to incentivise, and to support good AI – which in some cases may be undertaken directly by national or supranational policy makers, while in others may be led by other stakeholders. If adopted, these recommendations would serve as a firm foundation for the establishment of a Good AI Society. Keywords Artificial intelligence; AI4People; Data Governance; Digital Ethics; Governance; Ethics of AI. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 2 1. Introduction AI is not another utility that needs to be regulated once it is mature. It is a powerful force, a new form of smart agency, which is already reshaping our lives, our interactions, and our environments. AI4People was set up to help steer this powerful force towards the good of society, everyone in it, and the environments we share. This article is the outcome of the collaborative effort by the AI4People Scientific Committee-comprising 12 experts and chaired by Luciano Floridi1-to propose a series of recommendations for the development of a Good AI Society. The article synthesises three things: the opportunities and associated risks that AI technologies offer for fostering human dignity and promoting human flourishing; the principles that should undergird the adoption of AI; and twenty specific recommendations that, if adopted, will enable all stakeholders to seize the opportunities, to avoid or at least minimise and counterbalance the risks, to respect the principles, and hence to develop a Good AI Society. The article is structured around four more sections after this introduction. Section 2 states the core opportunities for promoting human dignity and human flourishing offered by AI, together with their corresponding risks.2 Section 3 offers a brief, high-level view of the advantages for organisations of taking an ethical approach to the development and use of AI. Section 4 formulates 5 ethical principles for AI, building on existing analyses, which should undergird the ethical adoption of AI in society at large. Finally, Section 5 offers 20 recommendations for the purpose of developing a Good AI Society in Europe. Since the launch of AI4People in February 2018, the Scientific Committee has acted collaboratively to develop the recommendations in the final section of this paper. Through this work, we hope to have contributed to the foundation of a Good AI Society we can all share. 2. The Opportunities and Risks of AI for Society That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and 1 Besides Luciano Floridi, the members of the Scientific Committee are: Monica Beltrametti, Raja Chatila, Patrice Chazerand, Virginia Dignum, Christoph Luetge, Robert Madelin, Ugo Pagallo, Francesca Rossi, Burkhard Schafer, Peggy Valcke, and Effy Vayena. Josh Cowls is the rapporteur. Thomas Burri contributed to an earlier draft. 2 The analysis in this and the following two sections is also available in Cowls and Floridi (2018). Further analysis and more information on the methodology employed will be presented in Cowls and Floridi (Forthcoming). Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 3 on what timescale. Put another way, we can safely dispense with the question of whether AI will have an impact; the pertinent questions now are by whom, how, where, and when this positive or negative impact will be felt. In order to frame these questions in a more substantive and practical way, we introduce here what we consider the four chief opportunities for society that AI offers. They are four because they address the four fundamental points in the understanding of human dignity and flourishing: who we can become (autonomous self-realisation); what we can do (human agency); what we can achieve (individual and societal capabilities); and how we can interact with each other and the world (societal cohesion). In each case, AI can be used to foster human nature and its potentialities, thus creating opportunities; underused, thus creating opportunity costs; or overused and misused, thus creating risks. As the terminology indicates, the assumption is that the use of AI is synonymous with good innovation and positive applications of this technology. However, fear, ignorance, misplaced concerns or excessive reaction may lead a society to underuse AI technologies below their full potential, for what might be broadly described as the wrong reasons. This may cause significant opportunity costs. It might include, for example, heavy-handed or misconceived regulation, underinvestment, or a public backlash akin to that faced by genetically modified crops (Imperial College, 2017). As a result, the benefits offered by AI technologies may not be fully realised by society. These dangers arise largely from unintended consequences and relate typically to good intentions gone awry. However, we must also consider the risks associated with inadvertent overuse or wilful misuse of AI technologies, grounded, for example, in misaligned incentives, greed, adversarial geopolitics, or malicious intent. Everything from email scams to full-scale cyber-warfare may be accelerated or intensified by the malicious use of AI technologies (Taddeo, 2017). And new evils may be made possible (King et. al, 2018). The possibility of social progress represented by the aforementioned opportunities above must be weighed against the risk that malicious manipulation will be enabled or enhanced by AI. Yet a broad risk is that AI may be underused out of fear of overuse or misuse. We summarise these risks in Figure A below, and offer a more detailed explanation in the text that follows. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 4 Figure A: Overview of the four core opportunities offered by AI, four corresponding risks, and the opportunity cost of underusing AI. 2.1 Who we can become: enabling human self-realisation, without devaluing human abilities AI may enable self-realisation, by which we mean the ability for people to flourish in terms of their own characteristics, interests, potential abilities or skills, aspirations, and life projects. Much as inventions, such as the washing machine, liberated people – particularly women – from the drudgery of domestic work, the "smart" automation of other mundane aspects of life may free up yet more time for cultural, intellectual and social pursuits, and more interesting and rewarding work. More AI may easily mean more human life spent more intelligently. The risk in this case is not the obsolescence of some old skills and the emergence of new ones per se, but the pace at which this is happening and the unequal distributions of the costs and benefits that result. A very fast devaluation of old skills and hence a quick disruption of the job market and the nature of employment can be seen at the level of both the individual and society. At the level of the individual, jobs are often intimately linked to personal identity, self-esteem, and social role or standing, all factors that may be adversely affected by redundancy, even putting to one side the potential for severe economic harm. Furthermore, at the level of society, the deskilling in sensitive, skill-intensive domains, such as health care diagnosis or aviation, may create dangerous vulnerabilities in the event of AI malfunction or an adversarial attack. Fostering the development of AI in support of new abilities and skills, while anticipating and mitigating its impact on old ones Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 5 will require both close study and potentially radical ideas, such as the proposal for some form of "universal basic income", which is growing in popularity and experimental use. In the end, we need some intergenerational solidarity between those disadvantaged today and those advantaged tomorrow, to ensure that the disruptive transition between the present and the future will be as fair as possible, for everyone. 2.2 What we can do: enhancing human agency, without removing human responsibility AI is providing a growing reservoir of "smart agency". Put at the service of human intelligence, such a resource can hugely enhance human agency. We can do more, better, and faster, thanks to the support provided by AI. In this sense of "Augmented Intelligence", AI could be compared to the impact that engines have had on our lives. The larger the number of people who will enjoy the opportunities and benefits of such a reservoir of smart agency "on tap", the better our societies will be. Responsibility is therefore essential, in view of what sort of AI we develop, how we use it, and whether we share with everyone its advantages and benefits. Obviously, the corresponding risk is the absence of such responsibility. This may happen not just because we have the wrong socio-political framework, but also because of a "black box" mentality, according to which AI systems for decision-making are seen as being beyond human understanding, and hence control. These concerns apply not only to high-profile cases, such as deaths caused by autonomous vehicles, but also to more commonplace but still significant uses, such as in automated decisions about parole or creditworthiness. Yet the relationship between the degree and quality of agency that people enjoy and how much agency we delegate to autonomous systems is not zero-sum, either pragmatically or ethically. In fact, if developed thoughtfully, AI offers the opportunity of improving and multiplying the possibilities for human agency. Consider examples of "distributed morality" in human-to-human systems such as peer-to-peer lending (Floridi, 2013). Human agency may be ultimately supported, refined and expanded by the embedding of "facilitating frameworks", designed to improve the likelihood of morally good outcomes, in the set of functions that we delegate to AI systems. AI systems could, if designed effectively, amplify and strengthen shared moral systems. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 6 2.3 What we can achieve: increasing societal capabilities, without reducing human control Artificial intelligence offers myriad opportunities for improving and augmenting the capabilities of individuals and society at large. Whether by preventing and curing diseases or optimising transportation and logistics, the use of AI technologies presents countless possibilities for reinventing society by radically enhancing what humans are collectively capable of. More AI may support better coordination, and hence more ambitious goals. Human intelligence augmented by AI could find new solutions to old and new problems, from a fairer or more efficient distribution of resources to a more sustainable approach to consumption. Precisely because such technologies have the potential to be so powerful and disruptive, they also introduce proportionate risks. Increasingly, we may not need to be either 'in or on the loop' (that is, as part of the process or at least in control of it), if we can delegate our tasks to AI. However, if we rely on the use of AI technologies to augment our own abilities in the wrong way, we may delegate important tasks and above all decisions to autonomous systems that should remain at least partly subject to human supervision and choice. This in turn may reduce our ability to monitor the performance of these systems (by no longer being 'on the loop' either) or preventing or redressing errors or harms that arise ('post loop'). It is also possible that these potential harms may accumulate and become entrenched, as more and more functions are delegated to artificial systems. It is therefore imperative to strike a balance between pursuing the ambitious opportunities offered by AI to improve human life and what we can achieve, on the one hand, and, on the other hand, ensuring that we remain in control of these major developments and their effects. 2.4 How we can interact: cultivating societal cohesion, without eroding human self-determination From climate change and antimicrobial resistance to nuclear proliferation and fundamentalism, global problems increasingly have high degrees of coordination complexity, meaning that they can be tackled successfully only if all stakeholders co-design and co-own the solutions and cooperate to bring them about. AI, with its data-intensive, algorithmic-driven solutions, can hugely help to deal with such coordination complexity, supporting more societal cohesion and collaboration. For example, efforts to tackle climate change have exposed the challenge of creating a cohesive Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 7 response, both within societies and between them. The scale of this challenge is such that we may soon need to decide between engineering the climate directly and designing societal frameworks to encourage a drastic cut in harmful emissions. This latter option might be undergirded by an algorithmic system to cultivate societal cohesion. Such a system would not be imposed from the outside; it would be the result of a self-imposed choice, not unlike our choice of not buying chocolate if we had earlier chosen to be on a diet, or setting up an alarm clock to wake up. "Selfnudging" to behave in socially preferable ways is the best form of nudging, and the only one that preserves autonomy. It is the outcome of human decisions and choices, but it can rely on AI solutions to be implemented and facilitated. Yet the risk is that AI systems may erode human selfdetermination, as they may lead to unplanned and unwelcome changes in human behaviours to accommodate the routines that make automation work and people's lives easier. AI's predictive power and relentless nudging, even if unintentional, should be at the service of human selfdetermination and foster societal cohesion, not undermining of human dignity or human flourishing. Taken together, these four opportunities, and their corresponding challenges, paint a mixed picture about the impact of AI on society and the people in it. Accepting the presence of trade-offs, seizing the opportunities while working to anticipate, avoid, or minimise the risks head-on will improve the prospect for AI technologies to promote human dignity and flourishing. Having outlined the potential benefits to individuals and society at large of an ethically engaged approach to AI, in the next section we highlight the "dual advantage" to organisations of taking such an approach. 3. The Dual Advantage of an Ethical Approach to AI Ensuring socially preferable outcomes of AI relies on resolving the tension between incorporating the benefits and mitigating the potential harms of AI, in short, simultaneously avoiding the misuse and underuse of these technologies. In this context, the value of an ethical approach to AI technologies comes into starker relief. Compliance with the law is merely necessary (the leas that is required), but significantly insufficient (not the most than can be done) (Floridi, 2018). With an analogy, it is the difference between playing according to the rules, and playing well, so that one may win the game. Adopting an ethical approach to AI confers what we define here as a "dual Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 8 advantage". On one side, ethics enables organisations to take advantage of the social value that AI enables. This is the advantage of being able to identify and leverage new opportunities that are socially acceptable or preferable. On the other side, ethics enables organisations to anticipate and avoid or at least minimise costly mistakes. This is the advantage of prevention and mitigation of courses of action that turn out to be socially unacceptable and hence rejected, even when legally unquestionable. This also lowers the opportunity costs of choices not made or options not grabbed for fear of mistakes. Ethics' dual advantage can only function in an environment of public trust and clear responsibilities more broadly. Public acceptance and adoption of AI technologies will occur only if the benefits are seen as meaningful and risks as potential, yet preventable, minimisable, or at least something against which one can be protected, through risk management (e.g. insurance) or redressing. These attitudes will depend in turn on public engagement with the development of AI technologies, openness about how they operate, and understandable, widely accessible mechanisms of regulation and redress. In this way, an ethical approach to AI can also be seen as an early warning system against risks which might endanger entire organisations. The clear value to any organisation of the dual advantage of an ethical approach to AI amply justifies the expense of engagement, openness, and contestability that such an approach requires. 4. A Unified Framework of Principles for AI in Society AI4People is not the first initiative to consider the ethical implications of AI. Many organisations have already produced statements of the values or principles that should guide the development and deployment of AI in society. Rather than conduct a similar, potentially redundant exercise here, we strive to move the dialogue forward, constructively, from principles to proposed policies, best practices, and concrete recommendations for new strategies. Such recommendations are not offered in a vacuum. But rather than generating yet another series of principles to serve as an ethical foundation for our recommendations, we offer a synthesis of existing sets of principles produced by various reputable, multi-stakeholder organisations and initiatives. A fuller explanation of the scope, selection and method of assessing these sets of principles is available in Cowls and Floridi (Forthcoming). Here, we focus on the commonalities and noteworthy differences Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 9 observable across these sets of principles, in view of the 20 recommendations offered in the rest of the paper. The documents we assessed are: 1. the Asilomar AI Principles, developed under the auspices of the Future of Life Institute, in collaboration with attendees of the high-level Asilomar conference of January 2017 (hereafter "Asilomar"; Asilomar AI Principles, 2017); 2. the Montreal Declaration for Responsible AI, developed under the auspices of the University of Montreal, following the Forum on the Socially Responsible Development of AI of November 2017 (hereafter "Montreal"; Montreal Declaration, 2017);3 3. the General Principles offered in the second version of Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems. This crowd-sourced global treatise received contributions from 250 global thought leaders to develop principles and recommendations for the ethical development and design of autonomous and intelligent systems, and was published in December 2017 (hereafter "IEEE"; IEEE, 2017);4 4. the Ethical Principles offered in the Statement on Artificial Intelligence, Robotics and 'Autonomous' Systems, published by the European Commission's European Group on Ethics in Science and New Technologies, in March 2018 (hereafter "EGE"; EGE, 2018); 5. the "five overarching principles for an AI code" offered in paragraph 417 of the UK House of Lords Artificial Intelligence Committee's report, AI in the UK: ready, willing and able?, published in April 2018 (hereafter "AIUK"; House of Lords, 2018); and 6. the Tenets of the Partnership on AI, a multistakeholder organisation consisting of academics, researchers, civil society organisations, companies building and utilising AI technology, and other groups (hereafter "the Partnership"; Partnership on AI, 2018). Taken together, they yield 47 principles.5 Overall, we find an impressive and reassuring degree of coherence and overlap between the six sets of principles. This can most clearly be shown by 3 The Montreal Declaration is currently open for comments as part of a redrafting exercise. The principles we refer to here are those which were publicly announced as of 1st May, 2018. 4 The third version of Ethically Aligned Design will be released in 2019 following wider public consultation. 5 Of the six documents, the Asilomar Principles offer the largest number of principles with arguably the broadest scope. The 23 principles are organised under three headings, "research issues", "ethics and values", and "longer-term issues". We have omitted consideration of the five "research issues" here as they are related specifically to the practicalities of AI development, particularly in the narrower context of academia and industry. Similarly, the Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 10 comparing the sets of principles with the set of four core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. The comparison should not be surprising. Of all areas of applied ethics, bioethics is the one that most closely resembles digital ethics in dealing ecologically with new forms of agents, patients, and environments (Floridi, 2013). The four bioethical principles adapt surprisingly well to the fresh ethical challenges posed by artificial intelligence. But they are not exhaustive. On the basis of the following comparative analysis, we argue that one more, new principle is needed in addition: explicability, understood as incorporating both intelligibility and accountability. 4.1 Beneficence: promoting well-being, preserving dignity, and sustaining the planet Of the four core bioethics principles, beneficence is perhaps the easiest to observe across the six sets of principles we synthesise here. The principle of creating AI technology that is beneficial to humanity is expressed in different ways, but it typically features at the top of each list of principles. Montreal and IEEE principles both use the term "well-being": for Montreal, "the development of AI should ultimately promote the well-being of all sentient creatures"; while IEEE states the need to "prioritize human well-being as an outcome in all system designs". AIUK and Asilomar both characterise this principle as the "common good": AI should "be developed for the common good and the benefit of humanity", according to AIUK. The Partnership describes the intention to "ensure that AI technologies benefit and empower as many people as possible"; while the EGE emphasises the principle of both "human dignity" and "sustainability". Its principle of "sustainability" represents perhaps the widest of all interpretations of beneficence, arguing that "AI technology must be in line with ... ensur[ing] the basic preconditions for life on our planet, continued prospering for mankind and the preservation of a good environment for future generations". Taken together, the prominence of these principles of beneficence firmly underlines the central importance of promoting the well-being of people and the planet. Partnership's eight Tenets consist of both intra-organisational objectives and wider principles for the development and use of AI. We include only the wider principles (the first, sixth, and seventh tenets). Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 11 4.2 Non-maleficence: privacy, security and "capability caution" Though "do only good" (beneficence) and "do no harm" (non-maleficence) seem logically equivalent, in both the context of bioethics and of the ethics of AI they represent distinct principles, each requiring explication. While they encourage well-being, the sharing of benefits and the advancement of the public good, each of the six sets of principles also cautions against the many potentially negative consequences of overusing or misusing AI technologies. Of particular concern is the prevention of infringements on personal privacy, which is listed as a principle in five of the six sets, and as part of the "human rights" principles in the IEEE document. In each case, privacy is characterised as being intimately linked to individuals' access to, and control over, how personal data is used. Yet the infringement of privacy is not the only danger to be avoided in the adoption of AI. Several of the documents also emphasise the importance of avoiding the misuse of AI technologies in other ways. The Asilomar Principles are quite specific on this point, citing the threats of an AI arms race and of the recursive self-improvement of AI, as well as the need for "caution" around "upper limits on future AI capabilities". The Partnership similarly asserts the importance of AI operating "within secure constraints". The IEEE document meanwhile cites the need to "avoid misuse", while the Montreal Declaration argues that those developing AI "should assume their responsibility by working against the risks arising from their technological innovations", echoed by the EGE's similar need for responsibility. From these various warnings, it is not entirely clear whether it is the people developing AI, or the technology itself, which should be encouraged not to do harm – in other words, whether it is Frankenstein or his monster against whose maleficence we should be guarding. Confused also is the question of intent: promoting non-maleficence can be seen to incorporate the prevention of both accidental (what we above call "overuse") and deliberate (what we call "misuse") harms arising. In terms of the principle of non-maleficence, this need not be an either/or question: the point is simply to prevent harms arising, whether from the intent of humans or the unpredicted behaviour of machines (including the unintentional nudging of human behaviour in undesirable ways). Yet these underlying questions of agency, intent and control become knottier when we consider the next principle. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 12 4.3 Autonomy: the power to decide (whether to decide) Another classic tenet of bioethics is the principle of autonomy: the idea that individuals have a right to make decisions for themselves about the treatment they do or not receive. In a medical context, this principle of autonomy is most often impaired when patients lack the mental capacity to make decisions in their own best interests; autonomy is thus surrendered involuntarily. With AI, the situation becomes rather more complex: when we adopt AI and its smart agency, we willingly cede some of our decision-making power to machines. Thus, affirming the principle of autonomy in the context of AI means striking a balance between the decision-making power we retain for ourselves and that which we delegate to artificial agents. The principle of autonomy is explicitly stated in four of the six documents. The Montreal Declaration articulates the need for a balance between humanand machine-led decision-making, stating that "the development of AI should promote the autonomy of all human beings and control ... the autonomy of computer systems" (italics added). The EGE argues that autonomous systems "must not impair [the] freedom of human beings to set their own standards and norms and be able to live according to them", while AIUK adopts the narrower stance that "the autonomous power to hurt, destroy or deceive human beings should never be vested in AI". The Asilomar document similarly supports the principle of autonomy, insofar as "humans should choose how and whether to delegate decisions to AI systems, to accomplish human-chosen objectives". These documents express a similar sentiment in slightly different ways, echoing the distinction drawn above between beneficence and non-maleficence: not only should the autonomy of humans be promoted, but also the autonomy of machines should be restricted and made intrinsically reversible, should human autonomy need to be re-established (consider the case of a pilot able to turn off the automatic pilot and regain full control of the airplane). Taken together, the central point is to protect the intrinsic value of human choice – at least for significant decisions – and, as a corollary, to contain the risk of delegating too much to machines. Therefore, what seems most important here is what we might call "meta-autonomy", or a "decide-to-delegate" model: humans should always retain the power to decide which decisions to take, exercising the freedom to choose where necessary, and ceding it in cases where overriding reasons, such as efficacy, may outweigh the loss of control over decision-making. As anticipated, any delegation should remain overridable in principle (deciding to decide again). Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 13 The decision to make or delegate decisions does not take place in a vacuum. Nor is this capacity to decide (to decide, and to decide again) distributed equally across society. The consequences of this potential disparity in autonomy are addressed in the final of the four principles inspired by bioethics. 4.4 Justice: promoting prosperity and preserving solidarity The last of the four classic bioethics principles is justice, which is typically invoked in relation to the distribution of resources, such as new and experimental treatment options or simply the general availability of conventional healthcare. Again, this bioethics principle finds clear echoes across the principles for AI that we analyse. The importance of "justice" is explicitly cited in the Montreal Declaration, which argues that "the development of AI should promote justice and seek to eliminate all types of discrimination", while the Asilomar Principles include the need for both "shared benefit" and "shared prosperity" from AI. Under its principle named "Justice, equity and solidarity", the EGE argues that AI should "contribute to global justice and equal access to the benefits" of AI technologies. It also warns against the risk of bias in datasets used to train AI systems, and – unique among the documents – argues for the need to defend against threats to "solidarity", including "systems of mutual assistance such as in social insurance and healthcare". The emphasis on the protection of social support systems may reflect geopolitics, insofar as the EGE is a European body. The AIUK report argues that citizens should be able to "flourish mentally, emotionally and economically alongside artificial intelligence". The Partnership, meanwhile, adopts a more cautious framing, pledging to "respect the interests of all parties that may be impacted by AI advances". As with the other principles already discussed, these interpretations of what justice means as an ethical principle in the context of AI are broadly similar, yet contain subtle distinctions. Across the documents, justice variously relates to a) using AI to correct past wrongs such as eliminating unfair discrimination; b) ensuring that the use of AI creates benefits that are shared (or at least shareable); and c) preventing the creation of new harms, such as the undermining of existing social structures. Notable also are the different ways in which the position of AI, vis-à-vis people, is characterised in relation to justice. In Asilomar and EGE respectively, it is AI technologies themselves that "should Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 14 benefit and empower as many people as possible" and "contribute to global justice", whereas in Montreal, it is "the development of AI" that "should promote justice" (italics added). In AIUK, meanwhile, people should flourish merely "alongside" AI. Our purpose here is not to split semantic hairs. The diverse ways in which the relationship between people and AI is described in these documents hints at broader confusion over AI as a man-made reservoir of "smart agency". Put simply, and to resume our bioethics analogy, are we (humans) the patient, receiving the "treatment" of AI, the doctor prescribing it? Or both? It seems that we must resolve this question before seeking to answer the next question of whether the treatment will even work. This is the core justification for our identification within these documents of a new principle, one that is not drawn from bioethics. 4.5 Explicability: enabling the other principles through intelligibility and accountability The short answer to the question of whether "we" are the patient or the doctor is that actually we could be either – depending on the circumstances and on who "we" are in our everyday life. The situation is inherently unequal: a small fraction of humanity is currently engaged in the design and development of a set of technologies that are already transforming the everyday lives of just about everyone else. This stark reality is not lost on the authors whose documents we analyse. In all, reference is made to the need to understand and hold to account the decision-making processes of AI. This principle is expressed using different terms: "transparency" in Asilomar; "accountability" in EGE; both "transparency" and "accountability" in IEEE; "intelligibility" in AIUK; and as "understandable and interpretable" for the Partnership. Though described in different ways, each of these principles captures something seemingly novel about AI: that its workings are often invisible or unintelligible to all but (at best) the most expert observers. The addition of this principle, which we synthesise as "explicability" both in the epistemological sense of "intelligibility" (as an answer to the question "how does it work?") and in the ethical sense of "accountability" (as an answer to the question: "who is responsible for the way it works?"), is therefore the crucial missing piece of the jigsaw when we seek to apply the framework of bioethics to the ethics of AI. It complements the other four principles: for AI to be beneficent and non-maleficent, we must be able to understand the good or harm it is actually doing to society, and in which ways; for AI to promote and not constrain human autonomy, our Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 15 "decision about who should decide" must be informed by knowledge of how AI would act instead of us; and for AI to be just, we must ensure that the technology – or, more accurately, the people and organisations developing and deploying it – are held accountable in the event of a negative outcome, which would require in turn some understanding of why this outcome arose. More broadly, we must negotiate the terms of the relationship between ourselves and this transformative technology, on grounds that are readily understandable to the proverbial person "on the street". Taken together, we argue that these five principles capture the meaning of each of the 47 principles contained in the six high-profile, expert-driven documents, forming an ethical framework within which we offer our recommendations below. This framework of principles is shown in Figure B. Figure B: an ethical framework for AI, formed of four traditional principles and a new one 5. Recommendations for a Good AI Society This section introduces the Recommendations for a Good AI Society. It consists of two parts: a Preamble, and 20 Action Points. There are four kinds of Action Points: to assess, to develop, to incentivise and to support. Some recommendations may be undertaken directly, by national or European policy makers, in collaboration with stakeholders where appropriate. For others, policy makers may play an enabling role for efforts undertaken or led by third parties. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 16 5.1 Preamble We believe that, in order to create a Good AI Society, the ethical principles identified in the previous section should be embedded in the default practices of AI. In particular, AI should be designed and developed in ways that decrease inequality and further social empowerment, with respect for human autonomy, and increase benefits that are shared by all, equitably. It is especially important that AI be explicable, as explicability is a critical tool to build public trust in, and understanding of, the technology. We also believe that creating a Good AI Society requires a multistakeholder approach, which is the most effective way to ensure that AI will serve the needs of society, by enabling developers, users and rule-makers to all be on board and collaborating from the outset. Different cultural frameworks inform attitudes to new technology. This document represents a European approach, which is meant to be complementary to other approaches. We are committed to the development of AI technology in a way that secures people's trust, serves the public interest, and strengthens shared social responsibility. Finally, this set of recommendations should be seen as a "living document". The Action Points are designed to be dynamic, requiring not simply single policies or one-off investments, but rather, continuous, ongoing efforts for their effects to be sustained. 5.2 Action Points 5.2.1 Assessment 1. Assess the capacity of existing institutions, such as national civil courts, to redress the mistakes made or harms inflicted by AI systems. This assessment should evaluate the presence of sustainable, majority-agreed foundations for liability from the design stage onwards in order to reduce negligence and conflicts (see also Recommendation 5).6 2. Assess which tasks and decision-making functionalities should not be delegated to AI systems, through the use of participatory mechanisms to ensure alignment with 6 Determining accountability and responsibility may usefully borrow from lawyers in Ancient Rome who would go by this formula 'cuius commoda eius et incommoda' ('the person who derives an advantage from a situation must also bear the inconvenience'). A good 2,200 years old principle that has a well-established tradition and elaboration could properly set the starting level of abstraction in this field. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 17 societal values and understanding of public opinion. This assessment should take into account existing legislation and be supported by ongoing dialogue between all stakeholders (including government, industry, and civil society) to debate how AI will impact society opinion (in concert with Recommendation 17). 3. Assess whether current regulations are sufficiently grounded in ethics to provide a legislative framework that can keep pace with technological developments. This may include a framework of key principles that would be applicable to urgent and/or unanticipated problems. 5.2.2 Development 4. Develop a framework to enhance the explicability of AI systems which make socially significant decisions. Central to this framework is the ability for individuals to obtain a factual, direct, and clear explanation of the decision-making process, especially in the event of unwanted consequences. This is likely to require the development of frameworks specific to different industries, and professional associations should be involved in this process, alongside experts in science, business, law, and ethics. 5. Develop appropriate legal procedures and improve the IT infrastructure of the justice system to permit the scrutiny of algorithmic decisions in court. This is likely to include the creation of a framework for AI explainability as indicated in Recommendation 4, specific to the legal system. Examples of appropriate procedures may include the applicable disclosure of sensitive commercial information in IP litigation, and – where disclosure poses unacceptable risks, for instance to national security – the configuration of AI systems to adopt technical solutions by default, such as zeroknowledge proofs in order to evaluate their trustworthiness. 6. Develop auditing mechanisms for AI systems to identify unwanted consequences, such as unfair bias, and (for instance, in cooperation with the insurance sector) a solidarity mechanism to deal with severe risks in AI-intensive sectors. Those risks could be mitigated by multistakeholder mechanisms upstream. Pre-digital experience indicates that, in some cases, it may take a couple of decades before society catches up with technology by way of rebalancing rights and protection adequately to restore trust. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 18 The earlier that users and governments become involved – as made possible by ICT – the shorter this lag will be. 7. Develop a redress process or mechanism to remedy or compensate for a wrong or grievance caused by AI. To foster public trust in AI, society needs a widely accessible and reliable mechanism of redress for harms inflicted, costs incurred, or other grievances caused by the technology. Such a mechanism will necessarily involve a clear and comprehensive allocation of accountability to humans and/or organisations. Lessons could be learnt from the aerospace industry, for example, which has a proven system of handling unwanted consequences thoroughly and seriously. The development of this process must follow from the assessment of existing capacity outlined in Recommendation 1. If a lack of capacity is identified, additional institutional solutions should be developed at national and/or EU levels, to enable people to seek redress. Such solutions may include: o an "AI ombudsperson" to ensure the auditing of allegedly unfair or inequitable uses of AI; o a guided process for registering a complaint akin to making a Freedom of Information request; and o the development of liability insurance mechanisms, which would be required as an obligatory accompaniment of specific classes of AI offerings in EU and other markets. This would ensure that the relative reliability of AI-powered artefacts, especially in robotics, is mirrored in insurance pricing and therefore in the market prices of competing products.7 Whichever solutions are developed, these are likely to rely on the framework for intelligibility proposed in Recommendation 4. 8. Develop agreed-upon metrics for the trustworthiness of AI products and services, to be undertaken either by a new organisation, or by a suitable existing organisation. These metrics would serve as the basis for a system that enables the user-driven benchmarking of all marketed AI offerings. In this way, an index for 7 Of course, to the extent that AI systems are 'products', general tort law still applies in the same way to AI as it applies in any instance involving defective products or services that injure users or do not perform as claimed or expected. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 19 trustworthy AI can be developed and signalled, in addition to a product's price. This "trust comparison index" for AI would improve public understanding and engender competitiveness around the development of safer, more socially beneficial AI (e.g., "IwantgreatAI.org"). In the longer term, such a system could form the basis for a broader system of certification for deserving products and services, administered by the organisation noted here, and/or by the oversight agency proposed in Recommendation 9. The organisation could also support the development of codes of conduct (see Recommendation 18). Furthermore, those who own or operate inputs to AI systems and profit from it could be tasked with funding and/or helping to develop AI literacy programs for consumers, in their own best interest. 9. Develop a new EU oversight agency responsible for the protection of public welfare through the scientific evaluation and supervision of AI products, software, systems or services. This may be similar, for example, to the European Medicines Agency. Relatedly, a "post-release" monitoring system for AIs similar to, for example, the one available for drugs should be developed, with reporting duties for some stakeholders and easy reporting mechanisms for other users. 10. Develop a European observatory for AI. The mission of the observatory would be to watch developments, provide a forum to nurture debate and consensus, provide a repository for AI literature and software (including concepts and links to available literature), and issue step-by-step recommendation and guidelines for action. 11. Develop legal instruments and contractual templates to lay the foundation for a smooth and rewarding human-machine collaboration in the work environment. Shaping the narrative on the 'Future of Work' is instrumental to winning "hearts and minds". In keeping with 'A Europe that protects', the idea of "inclusive innovation" and to smooth the transition to new kinds of jobs, a European AI Adjustment Fund could be set up along the lines of the European Globalisation Adjustment Fund. 5.2.3 Incentivisation 12. Incentivise financially, at the EU level, the development and use of AI technologies within the EU that are socially preferable (not merely acceptable) and Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 20 environmentally friendly (not merely sustainable but favourable to the environment). This will include the elaboration of methodologies that can help assess whether AI projects are socially preferable and environmentally friendly. In this vein, adopting a 'challenge approach' (see DARPA challenges) may encourage creativity and promote competition in the development of specific AI solutions that are ethically sound and in the interest of the common good. 13. Incentivise financially a sustained, increased and coherent European research effort, tailored to the specific features of AI as a scientific field of investigation. This should involve a clear mission to advance AI for social good, to serve as a unique counterbalance to AI trends with less focus on social opportunities. 14. Incentivise financially cross-disciplinary and cross-sectoral cooperation and debate concerning the intersections between technology, social issues, legal studies, and ethics. Debates about technological challenges may lag behind the actual technical progress, but if they are strategically informed by a diverse, multistakeholder group, they may steer and support technological innovation in the right direction. Ethics should help seize opportunities and cope with challenges, not only describe them. It is essential in this respect that diversity infuses the design and development of AI, in terms of gender, class, ethnicity, discipline and other pertinent dimensions, in order to increase inclusivity, toleration, and the richness of ideas and perspectives. 15. Incentivise financially the inclusion of ethical, legal and social considerations in AI research projects. In parallel, incentivise regular reviews of legislation to test the extent to which it fosters socially positive innovation. Taken together, these two measures will help ensure that AI technology has ethics at its heart and that policy is oriented towards innovation. 16. Incentivise financially the development and use of lawfully de-regulated special zones within the EU for the empirical testing and development of AI systems. These zones may take the form of a "living lab" (or Tokku), building on the experience of existing "test highways" (or Teststrecken). In addition to aligning innovation more closely with society's preferred level of risk, sandbox experiments such as these contribute to handsElectronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 21 on education and the promotion of accountability and acceptability at an early stage. "Protection by design" is intrinsic to this kind of framework. 17. Incentivise financially research about public perception and understanding of AI and its applications, and the implementation of structured public consultation mechanisms to design policies and rules related to AI. This may include the direct elicitation of public opinion via traditional research methods, such as opinion polls and focus groups, as well as more experimental approaches, such as providing simulated examples of the ethical dilemmas introduced by AI systems, or experiments in social science labs. This research agenda should not serve merely to measure public opinion, but should also lead to the co-creation of policies, standards, best practices, and rules as a result. 5.2.4 Support 18. Support the development of self-regulatory codes of conduct for data and AI related professions, with specific ethical duties. This would be along the lines of other socially sensitive professions, such as medical doctors or lawyers, i.e., with the attendant certification of 'ethical AI' through trust-labels to make sure that people understand the merits of ethical AI and will therefore demand it from providers. Current attention manipulation techniques may be constrained through these self-regulating instruments. 19. Support the capacity of corporate boards of directors to take responsibility for the ethical implications of companies' AI technologies. For example, this may include improved training for existing boards and the potential development of an ethics committee with internal auditing powers. This could be developed within the existing structure of both one-tier and two-tier board systems, and/or in conjunction with the development of a mandatory form of "corporate ethical review board" to be adopted by organisations developing or using AI systems, to evaluate initial projects and their deployment with respect to fundamental principles. 20. Support the creation of educational curricula and public awareness activities around the societal, legal, and ethical impact of Artificial Intelligence. This may include: Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 email@example.com | www.eismd.eu | www.AI4People.org 22 o curricula for schools, supporting the inclusion of computer science among the basic disciplines to be taught; o initiatives and qualification programmes in businesses dealing with AI technology, to educate employees on the societal, legal, and ethical impact of working alongside AI; o a European-level recommendation to include ethics and human rights in the degrees of data and AI scientists and other scientific and engineering curricula dealing with computational and AI systems; o the development of similar programmes for the public at large, with a special focus on those involved at each stage of management of the technology, including civil servants, politicians and journalists; o engagement with wider initiatives such as the ITU AI for Good events and NGOs working on the UN Sustainable Development Goals. Conclusion Europe, and the world at large, face the emergence of a technology that holds much exciting promise for many aspects of human life, and yet seems to pose major threats as well. This article – and especially the Recommendations in the previous section – seek to nudge the tiller in the direction of ethically and socially preferable outcomes from the development, design and deployment of AI technologies. Building on our identification of both the core opportunities and the risks of AI for society as well as the set of five ethical principles we synthesised to guide its adoption, we formulated 20 Action Points in the spirit of collaboration and in the interest of creating concrete and constructive responses to the most pressing social challenges posed by AI. With the rapid pace of technological change, it can be tempting to view the political process in the liberal democracies of today as old-fashioned, out-of-step, and no longer up to the task of preserving the values and promoting the interests of society and everyone in it. We disagree. With the Recommendations we offer here, including the creation of centres, agencies, curricula, and other infrastructure, we have made the case for an ambitious, inclusive, equitable programme of policy making and technological innovation, which we believe will contribute to securing the benefits and mitigating the risks of AI, for all people, and for the world we share. Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 23 Acknowledgements This publication would not have been possible without the generous support of Atomium – European Institute for Science, Media and Democracy. We are particularly grateful to Michelangelo Baracchi Bonvicini, Atomium's President, to Guido Romeo, its Editor in Chief, the staff of Atomium for their help, and to all the partners of the AI4People project and members of its Forum (http://www.eismd.eu/ai4people) for their feedback. The authors of this article are the only persons responsible for its contents and any remaining mistakes. References Asilomar AI Principles (2017). Principles developed in conjunction with the 2017 Asilomar conference [Benevolent AI 2017]. Retrieved September 18, 2018, from https://futureoflife.org/aiprinciples Cowls, J. and Floridi, L. (2018). Prolegomena to a White Paper on Recommendations for the Ethics of AI (June 19, 2018). Available at SSRN: https://ssrn.com/abstract=3198732. Cowls, J. and Floridi, L. (Forthcoming). The Utility of a Principled Approach to AI Ethics. European Group on Ethics in Science and New Technologies (2018, March). Statement on Artificial Intelligence, Robotics and 'Autonomous' Systems. Retrieved September 18, 2018, from https://ec.europa.eu/info/news/ethics-artificial-intelligence-statement-ege-released-2018-apr24_en. Imperial College London (2017, Oct, 11). Written Submission to House of Lords Select Committee on Artificial Intelligence [AIC0214]. Retrieved September 18, 2018, from http://bit.ly/2yleuET The IEEE Initiative on Ethics of Autonomous and Intelligent Systems (2017). Ethically Aligned Design, v2. Retrieved September 18, 2018, from https://ethicsinaction.ieee.org Floridi, L. (2018). Soft Ethics and the Governance of the Digital. Philos. Technol. 2018, 1-8. Floridi, L. (2013). The Ethics of Information. Oxford, Oxford University Press. House of Lords Artificial Intelligence Committee (2018, April, 16). AI in the UK: ready, willing and able? Retrieved September 18, 2018, from https://publications.parliament.uk/pa/ld201719/ldselect/ldai/100/10002.htm Electronic copy available at: https://ssrn.com/abstract=3284141 Atomium European Institute for Science, Media and Democracy 24, Boulevard Louis Schmidt, Brussels, Belgium | T. +32 (0)2 8887010 | F. +32 (0)2 8887011 firstname.lastname@example.com | www.eismd.eu | www.AI4People.org 24 King, T., Aggarwal, N., Taddeo, M., and Floridi, L (2018, May, 22), Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions. Available at SSRN: https://ssrn.com/abstract=3183238 Montreal Declaration for a Responsible Development of Artificial Intelligence (2017, November, 3). Announced at the conclusion of the Forum on the Socially Responsible Development of AI. Retrieved September 18, 2018, from https://www.montrealdeclaration-responsibleai.com/thedeclaration. Partnership on AI (2018). Tenets. Retrieved September 18, 2018, from https://www.partnershiponai.org/tenets/ Taddeo, M. (2017). The limits of deterrence theory in cyberspace. Philos. Technol. 2017, 1–17. Electronic copy available at: https://ssrn.com/abstract=


Gary Ostertag (Ed.), Meanings and Other Things: Themes from the Work of  Stephen Schiffer

Philosophy

Penultimate version – published in Notre Dame Philosophical Reviews, 2018 "Review of Gary Ostertag's (ed.) Meanings and Other Things: Themes from the Work of Stephen Schiffer" Indrek Reiland Over the course of the past half century, Stephen Schiffer has written a highly influential book in philosophy of language every fifteen years, plus countless influential papers in-between. His first book, Meaning (1972), set out the ambitious Gricean program with the aim to analyze linguistic meaning in terms of speaker meaning and speaker meaning in terms of intention, ultimately hoping for a reduction of intention and other propositional attitudes in non-intentional terms. This was followed by Remnants of Meaning (1987) which criticized and ultimately dismantled the previous program. His last effort, The Things We Mean (2003), tries to reconcile the two previous time-slices by taking on board the pessimistic lessons of the second book, while still offering an account of linguistic meaning. This Festschrift dedicated to Schiffer's work consists of an editor's introduction, 14 papers, and his replies. Most of the contributions respond to Things or papers published around that time and they can be grouped into four. First, there are four papers on easy ontology and pleonastic entities by Amie Thomasson, Thomas Hofweber, Ian Rumfitt, and Michael Smith. Second, there are three papers on propositional attitude reports by Gary Ostertag, Ray Buchanan, and Nathan Salmon (a reprint of Salmon 2006). Next, there are four papers on vagueness and paradoxes by Dorothy Edgington, Hartry Field, Crispin Wright, and Paul Horwich (also printed in Horwich 2010). Finally, there are three papers on the Gricean program and issues concerning reference and definite descriptions by Stephen Neale, Anita Avramides, and Kent Bach. Frequently the most useful part of an edited volume is the introduction, and Ostertag's is essential reading for anyone who wants to get a handle on Schiffer's views and their development over time. I want to start by saying something general about Schiffer's approach to linguistic meaning that comes up in the introduction and informs several of his replies. 2 What does an account of linguistic meaning consist of? Schiffer seems to conceive of meaning on what we might call the Object Model. Having a meaning is like having a cow (or the money you can buy it with). There's the thing, the meaning, and then there's the having of it, which consists in standing in the "meaning relation" to it. Thus, there seem to be two questions to be answered. First, what are meanings? Second, what is it for an expression to have one in a language. I have something to say about both parts, starting with the second. Ostertag portrays Schiffer as posing the second question as follows: "In virtue of what does an expression have the meaning that it does among a group of speakers G?". This, in turn, gets transformed, following David Lewis, as follows. First, one stipulates that a language or "Lewis-language" like French as currently spoken is an abstract object, a set of sentences with their meanings such that the sentences have their meanings essentially. Second, one asks: "What determines that the members of G speak this language rather than another one?". Schiffer's answer takes the following form: A sentence x has meaning M among G just when, for some language L, (i) x has meaning M in L, and (ii) L is used by G. Ostertag says that to fill this out, one doesn't need to analyze 'x has meaning M in L'. One only needs to say when L is used by G, to specify the so-called actual-language relation. As he puts it, once this is done: "...there is no further question that needs to be addressed." (p. 7) Schiffer later says the same (p. 507). I've always found the Lewis-like setup at best a distraction. Instead of asking directly what is it for an expression to have a meaning in an actual language like French as currently spoken, one defines what amounts intuitively a mere potential language, and then one asks what is it for a group to speak one of these potential languages as their language. In any case, care is needed here in how we state the question. Consider, for a moment, truth and truth-making. It's one thing to ask, what is it for something to be true, seeking an analysis. It's another thing to ask in virtue of what are truths true, what make truths or grounds truth-facts. Now, similarly, it's one thing to ask what is it for an expression to have a meaning, seeking an analysis. It's another thing to ask in virtue of what do expressions have the meanings that they actually do, what makes meanings or grounds meaning-facts. That 3 these are separate question is highlighted by the fact that one could think that the first question lacks a substantive answer because truth or meaningfulness are primitive and unanalyzable while still trying to answer the second question. Now let's come back to how Ostertag portrays Schiffer as setting things up. It uses an 'In virtue of', form and this makes it a bit unclear whether we're asking for a reductive analysis or a completely different story about meaning-making. But let's assume that it's the former. This makes one wonder why it is that we don't need to analyze, for LewisLanguages, 'x has meaning M in L'. (There is no question about meaning-making for LewisLanguages since that is settled by fiat). The answer seems to be that 'meaning' in the above locution is not to be taken in an ordinary, but a stipulated sense (Schiffer 2017). But then it becomes all the more puzzling why the roundabout way of setting things up should be helpful at all. What about Schiffer's view of meanings? In Things he argues that the linguistic meaning of a sentence can't be a mere propositional blueprint to be filled in by resolving context-sensitivity (a la Kaplan's character) but must further include information contributed by sentence mood. He calls meanings thus conceived characters* and represents them as a template consisting of the propositional blueprint + an intended speech act. For example, the character* of 'Finish your coffee!' is <commanding, the proposition that i finishes her coffee at t> (where i is the addressee of the context and t the time). However, this raises a problem. Given that meaning is what knowledge of meaning is knowledge of, we should know expressions' characters*. However, Schiffer thinks, and Ostertag seems to agree, it's implausible that ordinary speakers have such knowledge since speakers wouldn't recognize these things as meanings. This leads him to the claim that either knowledge of meaning has to be understood in some novel way or that expressions don't really have meanings at all but only characters*. We can find a way out of this dilemma if we focus on the fact that like Kaplan's characters, Schiffer's characters* are mere formal representations of whatever meanings really are. One reason why Schiffer hasn't been able to tell us more about what meanings really are might be his implicit acceptance of the Object Model which could lead one to conceive of meanings as things that need to be characterized independently of answering the question about having them. In fact, this is how he proceeds in Things. But suppose one rejects this assumption and asks first, what is it for an expression to have a meaning in a 4 language. One interesting idea is that it is for there to be a rule for the use of the expression that determines the conditions in which it may be used. On this view, meanings are useconditions. The character* of 'Finish your coffee!', <commanding, the proposition that i finishes her coffee at t> could be just a formal representation of a rule which tells us that a speaker may use the sentence iff she intends to command a person she's addressing to finish her coffee at the time of speaking. And perhaps it wouldn't be implausible to think that ordinary speakers' knowledge of meaning consists in knowing such use-conditions. Moving on from the introduction and general issues to the contributions, one way in which such Festschrifts can interestingly go beyond usual edited collections is by containing the honoree's replies. Many of the exchanges here are fascinating and most of them resist short summaries. For example, I would highly recommend Amie Thomasson's "Easy Ontology and Its Consequences" together with Schiffer's reply to anyone interested in the metaphysics of propositions and properties and metametaphysics more generally. However, here I will look more closely at two other exchanges which for me were the highlights of the volume. Ray Buchanan's "Schiffer's Puzzle: A Kind of Fregean Response" reacts to one of Schiffer's less known papers "What Reference Has to Tell Us About Meaning" (Schiffer 2005). In that paper Schiffer discusses beliefs like my belief that I'm hungry. Most philosophers agree that having this belief involves thinking of myself in a special, firstperson way. Yet, when I say to you that I'm hungry, understanding what I say doesn't require you to think of me in the first-person way. Now, on the face of it, I say what I believe. But then it seems that what I believe and say and which you grasp when understanding me must be such that for different people to have an attitude with it as its content, they need to think of the relevant object in different ways or under different modes of presentation (MOPs). In Schiffer's terms, the thing I believe and say, the proposition, must have what he calls the relativity feature. Standard Russellian and Fregean views don't allow any propositions to have the relativity feature and thus they must explain away how things seem. The Russellians who start from what's public and tie their notion of propositional content primarily to propositional attitude reports try to account for this by claiming that it's our states of belief and understanding that somehow involve the different MOPs whereas the believed and grasped content is the same Russellian proposition <Indrek, being hungry>. The Fregeans 5 who start from what's private and tie their notion of propositional content primarily to the individual's perspective try to account for this by claiming that what I believe does indeed involve a different MOP than what you grasp when you understand me, but insisting that there is no problem since communication doesn't require transfer of content, but only mutual knowledge of suitable correspondence between the contents (Heck 2002, McDowell 1998). Schiffer argues that neither of these strategies works and that this provides further support for his theory of pleonastic propositions which can allow for propositions to have the relativity feature. Buchanan develops the Fregean view and defends it against Schiffer's objections. On his picture we believe Fregean propositions like <IndrekDE SE, being hungry>. However, and this is where he goes beyond McDowell and Heck, on his view we never say, we don't even try to communicate, exactly what we believe. Rather, in the above case I say a Fregean proposition-type Φ which abstracts away from the relevant MOP and is basically equivalent to the Russellian proposition <Indrek, being hungry>. Schiffer doesn't like such views because he thinks that they can't allow for what I say to be what I believe. Consider examples like the following: 1) I'm hungry. 2) By uttering 1), I said that I'm hungry. 3) I believe that I'm hungry. On Buchanan's account, the that-clause in 2) doesn't refer to the Fregean proposition, but then it also can't refer to it in 3). But then, when are reports such as 3) true? Buchanan's solution is to start from Kent Bach's claim that belief reports never report what we believe, but only indirectly and partially characterize it. First, consider the following report: 4) Alex said that I'm hungry On Buchanan's view this has something like the following logical form (where Φ is the contextually relevant proposition-type): 6 4 ) Says (Alex, Φ) Here, the that-clause refers to the proposition-type. Now consider the following report: 5) Alex believes that I'm hungry On Buchanan's view this has a different logical form (where, again, Φ is the contextually relevant proposition-type and p is a proposition of that type) 5 ) p (Φp & Believes (Alex, p)) The that-clause still refers not to the proposition, but to the proposition-type. But 'believes' triggers an existential reading much like 'owns' in 'Mary owns that kind of dog'. Thus, the report tells us that there is some Fregean proposition from the relevant type such that Alex believes that proposition. This is an interesting view. Unsurprisingly, Schiffer doesn't like it. One of the reasons he gives in his reply is the following worry. Schiffer claims that 'Logicism' is the name of a proposition, namely that mathematics reduces to logic. Now, consider: 6) Jill believes logicism. 7) Jill believes the proposition that mathematics reduces to logic. 8) Jill believes that mathematics reduces to logic. Schiffer claims that in 6) 'Logicism' refers to the proposition that mathematics reduces to logic. He then claims it follows that in 7) the NP 'the proposition that mathematics reduces to logic' also refers to that proposition. Finally, he claims that since 7) is a pleonastic equivalent of 8), the that-clause should also refer to the proposition. But Buchanan can't allow for this, so he is in trouble. All of the steps in the above argument are dubious. First, names like 'Logicism' do not refer to propositions, but views which are more coarse-grained than propositions in being expressible by different sentences expressing different propositions (e.g. 'Mathematics is reducible to logic' vs. 'Mathematics is not irreducible to logic') (Salmon 7 1992). Second, even if one would agree that names like 'Logicism' refer to propositions it's unclear why it would follow that NPs like 'the proposition that mathematics reduces to logic' refer to propositions rather than somehow describing them. Finally, and perhaps most importantly, the NP and the that-clause are clearly not pleonastic equivalents. Consider: 9) Scott likes the proposition that mathematics reduces to logic. 10) Scott likes that mathematics reduces to logic. 9) attributes to Scott a completely kosher attitude of liking a particular proposition, perhaps as a pet example. In contrast, 10) attributes to him a problematic attitude of liking that something is the case that in fact isn't. Thus, it's hard to see why Buchanan should be moved by Schiffer's first worry. Nevertheless, Schiffer is correct that the above view does get in trouble explaining the validity of inferences like the following: 11) Jill said that mathematics reduces to logic. 12) Jack believes that mathematics reduces to logic. 13) There is something that Jill said, and Jack believes. All in all, Schiffer's original paper and Buchanan's paper should be of immense interest to anyone who's puzzled about the tricky relations of mental state and speech act content. The second paper I want to comment on is Stephen Neale's massive, 112(!)-page contribution "Silent Reference" which takes up more than a 5th of the volume. This paper takes off from Schiffer's "Indexicals and the Theory of Reference" (1981) and "Belief Ascription" (1992). In the former, Schiffer proposed roughly the following analysis of speaker reference in terms of speaker meaning: S refers to o in uttering x just in case, in uttering x, S means a singular, o-dependent proposition. He then went on to define a notion of speaker reference with an expression and the notion of implicit reference. In the latter paper Schiffer criticized theories of belief reports which relied on implicit reference to MOPs. Later, he extended the criticism to theories of 8 knowledge, incomplete definite descriptions, and other phenomena which relied on implicit reference. The relevant objection is the meaning-intention problem. If to refer is to mean, and to mean is to have audience-directed intentions, then speakers who implicitly refer to something must have intentions about what they're implicitly referring to. For example, if I say 'It's raining' then it's plausible that I'm intending to refer to a particular location. If asked 'Where?', I can at least provide an answer. However, Schiffer argues, in the case of supposed implicit reference to MOPs, epistemic standards, completing properties etc., such intentions seem to be lacking and speakers are at a loss when asked to specify the relevant one. Neale's aim in this rich paper is to refine Schiffer's analysis of the aforementioned notions and discuss a variety of implicit reference, namely reference by using aphonic expressions, expressions which lack any phonological features (e. g. the so-called null constituents or empty categories like PRO). However, before getting to any of this he provides a systematic discussion of a host of background issues. For example, he draws distinctions between speaker meaning/reference, expression meaning/reference, and argues that the notions of utterance meaning/reference don't make any sense. He discusses different forms of implicit reference and provides a plethora of examples. He talks about methodology and how the notions of meaning and interpretation interact. He provides a very interesting metaphysics of words vs. utterances which abandons the type-token distinction in favor of the idea that words and other expressions are abstract artefacts whereas utterances of them are concrete events which serve as their perceptible proxies. He distinguishes between metaphysical, epistemic, and aetiological determination. He defends the Gricean view that what a speaker means is wholly determined by his communicative intentions while linguistic meaning and context only constrain the communicative intention insofar as the speaker must rely on them to provide evidence for the interpreter to figure out what their intention is. He discusses different notions of saying and what is said. And he argues that for at least context-sensitive expressions, the talk of expression reference is just talk of speaker reference with the expression. This is great reading and highly recommended to anyone interested in seeing how at least one philosopher of language tries to make everything hang together. Getting to the main part of the paper, Neale follows Schiffer in defining reference in terms of speaker-meaning. This has obvious, if unintended, echoes of Kant on the 9 primacy of judgment and Frege and Wittgenstein on the context principle. As Neale says, the idea is that speaker meaning is primary and notions like reference and predication are to be defined in terms of it (contrast this with the "atomistic" Grice-inspired stories in Bach 1992, Bertolet 1987, and Stine 1978 as well as Peter Hanks's and Scott Soames's recent work). He also says that he thinks that there's something deeply wrong with the idea of a self-standing sub-propositional act of reference. But there are two different notions of reference. In one sense, to refer just is to make something a subject of predication. Presumably everybody would grant that reference in this sense doesn't make sense independent of the accompanying predication. However, there's also the more basic notion of just indicating an object without necessarily predicating something of it. For Neale's claim to be substantive, he must think that this notion is to be defined in terms of speaker meaning as well and this is indeed what he seems to think (see fn. 76). However, there's a straightforward problem with this idea. For example, consider uses of names in calls like 'Paul!' or in vocative positions of imperatives like 'Paul, come here!'. In these cases the name is not used to mean something about its bearer, but just to get the bearer's attention. But then it's unclear how such cases of "reference" could be explained in terms of the speaker's meaning something. Leaving this aside, Neale improves on Schiffer's definition of speaker reference with an expression by expanding it to the notion of speaker reference with an expression relative to an occurrence: RW) In uttering x, S referred to o with e relative to its i-th occurrence in x iff for some audience A and relation R, S intended A to recognize that R (e, x, i, o), and, at least partly on the basis of this, that S referred to o in uttering x. As in Schiffer's definition of speaker meaning, the nature of R is left open and may be "any relation which S thinks will do the job" (Schiffer 1981: 65). For example, in the case of uttering 'Paul is a philosopher', R ('Paul', 'Paul is a philosopher', Paul) could just consist of the fact that 'Paul' occurs in the sentence and is Paul's name. Next, Neale provides the following definitions of explicit or phonic reference, implicit reference, and aphonic reference: 10 RE) In uttering x, S referred to o explicitly iff there is a phonic in x with which S referred to o. RI) In uttering x, S referred to o implicitly if (i) the truth-conditions of what S said are o-involving, (ii) but there is no phonic in x with which S referred to o. RA) In uttering x, S referred to o aphonically iff there is an occurrence i of an aphonic e in x such that S referred to o with e, relative to i. For example, if Sue uses 'It snowed this morning', meaning that it snowed in London this morning, then Sue has implicitly referred to London. It's a further question whether Sue has achieved this by using an aphonic or in some other way. However, Neale argues that there are lots of cases where implicit reference is achieved by using aphonics. To take just one example, consider 14) where an aphonic is assumed to occur as the subject of the embedded gerundive clause (bracketed): 14) Sue remembers [e lecturing on aphonics]. Intuitively, someone uttering this sentence is referring to Sue twice, once, phonically, to the person remembering, and another time, aphonically, to the person lecturing. Now, Schiffer argued against implicit reference theories by raising the meaningintention problem. Aphonic reference doesn't quite give rise to the meaning-intention problem which is a problem about the sorts of things people are supposed to be referring to. However, Neale argues that it gives rise to a related aphonic-intention problem which is a problem about the things people are supposedly referring with. If we look again at RW together with RA above, it's clear that they require speakers to have quite complex intentions about aphonics and it might seem quite implausible that they do. So there seems to be a tension between RW and the existence of aphonics. Nevertheless, Neale claims, we can hope to resolve the tension by claiming that speakers tacitly know which expressions contain aphonics and perhaps have something like tacit intentions about them. In his reply, Schiffer takes Neale to task about the very last bit. He thinks that even though the aphonic-intention problem is indeed a serious problem for the Gricean, the tacit knowledge reply has no hope at all. One sense in which a speaker could be said to have 'tacit' knowledge and intentions is in Brian Loar's sense of implicit attitude in which these 11 are just ordinary attitudes that the person is not consciously aware of but could rather easily become aware of (Loar 1976). Schiffer thinks that it's clear that no ordinary speaker has implicit knowledge about aphonics. Another sense is the Chomskyan sense of being subpersonally represented. However, Schiffer says, then the relevant states concerning aphonics do not amount to knowledge and, damningly, no sense can be made of tacit intentions. This is a fascinating issue about which it is impossible to come to any conclusion on the basis of what Neale and Schiffer say here. However, I'm happy to report that it is further taken up in a recent special issue of Croatian Journal of Philosophy dedicated to Neale's paper, containing interesting papers by Schiffer himself, Dan Harris, Elmar Unnsteinsson, and Jesse Rappaport (Harris 2017, Rappaport 2017, Schiffer 2017, Unnsteinsson 2017). All in all, the Neale-Schiffer exchange and the follow-ups are essential reading interested in the current state of Gricean thought about language. Let me sum up by making two mildly critical overall remarks. The first has to do with timing. Most of the papers respond to Things or papers published around the same time. In fact, in the case of Hofweber's paper the editor has included a note according to which it was submitted, as one of the first papers, in 2006. A lot has happened in philosophy of language in the meantime and some of the papers in the volume can therefore feel a bit out of date. Second, to me, it was a bit disappointing that there were no papers on Schiffer's interesting proposals about linguistic meaning and knowledge of meaning in Things. Nevertheless, there's a lot to chew on here, and one can only hope that because fifteen years have passed since his last book, Schiffer provides us a with another book-length update of his views soon.1 References Bach, K. 1992. "Intentions and Demonstrations". Analysis, 52, pp. 140-146 Bertolet, R. 1987. "Speaker Reference". Philosophical Studies, 52, pp. 199-226 Harris, D. 2017. "Speaker Reference and Cognitive Architecture". Croatian Journal of Philosophy, 17, pp. 319-349 1 Thanks to Ray Buchanan, Ben Lennertz, and Gary Ostertag for comments and discussion. 12 Horwich, P. 2010. Truth-Meaning-Reality. Oxford: Oxford University Press Loar, B. 1976. "The Semantics of Singular Terms". Philosophical Studies, 30, pp. 353-377 Rappaport, J. 2017. "Is there a Meaning-Intention Problem". Croatian Journal of Philosophy, 17, pp. 383-397 Salmon, N. 1992. "On Content". Mind, 101, pp. 733-751 Salmon, N. 2006. "The Resilience of Illogical Belief". Nous, 40, pp. 369-75 Schiffer, S. 1981. "Indexicals and the Theory of Reference". Synthese, 23, pp. 43-100 Schiffer, S. 1992. "Belief Ascription". Journal of Philosophy, 89, pp. 499-521 Schiffer, S. 2003. The Things We Mean. Oxford: Oxford University Press Schiffer, S. 2005. "What Reference Has to Tell Us about Meaning". Conteude e Cognicao: Actas de Seminario de Filosofia Analitica 2003-2004. Ed. J. Branquinho, Lisbon: University of Lisbon Press Schiffer, S. 2017a. "Intention and Convention in the Theory of Meaning". A Companion to Philosophy of Language, 2nd Ed. Ed. B. Hale, C. Wright, A. Miller. John Wiley & Sons, pp. 4972 Schiffer, S. 2017b. "Gricean Semantics and Vague Speaker-Meaning". Croatian Journal of Philosophy, 17, pp. 293-317 Stine, G. C. 1978. "Meaning Other Than What We Say and Referring". Philosophical Studies, 33, pp. 319-337 Unnsteinsson, J. 2017. "Saying without Knowing What or How". Croatian Journal of Philosophy, 17, pp. 351-