Philosophy

Vol.:(0123456789) HPLS (2021) 43:115 https://doi.org/10.1007/s40656-021-00468-6 1 NOTES & COMMENTS Open science, data sharing and solidarity: who benefits? Ciara Staunton1 * Carlos Andrés Barragán2 * Stefano Canali3,9 * Calvin Ho4 * Sabina Leonelli5 * Matthew Mayernik6 * Barbara Prainsack7 * Ambroise Wonkham8 Received: 21 September 2021 / Accepted: 28 September 2021 © The Author(s) 2021 Abstract Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the "open science" movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel "Open science, data sharing and solidarity: who benefits?" held at the 2021 Biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB), and hosted by Cold Spring Harbor Laboratory (CSHL). Keywords Open science * Data sharing * Governance * Benefit sharing * Ciara Staunton firstname.lastname@example.com 1 Institute for Biomedicine, Eurac Research, Bolzano, Italy 2 Department of Science & Technology Studies (STS), University of California, Davis, USA 3 Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy 4 Department of Law and Centre for Medical Ethics and Law, University of Hong Kong, Hong Kong, China 5 Department of Sociology, Philosophy and Anthropology & Exeter Centre for the Study of the Life Sciences, University of Exeter, Exeter, UK 6 National Center for Atmospheric Research, University Corporation for Atmospheric Research, Boulder, CO, USA 7 Department of Political Science, University of Vienna, Vienna, Austria 8 Division of Human Genetics, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa 9 META Social Sciences and Humanities for Science and Technology, Politecnico Di Milano, Milan, Italy C. Staunton et al. 1 3 115 Page 2 of 8 1 Background Open science and the sharing of research findings, as well as research components, is emerging as a key feature of data intensive research methods. It has been credited with increasing efficiencies in research, more reproducible science, maximizing the use of a valuable resource, the democratization of knowledge (Walport & Brest, 2011), and has been credited with the rapid development of COVID-19 vaccines, therapies and diagnostics (The Lancet, 2021). Research participants are therefore encouraged to consent to the sharing of their genetic materials in the interest of the public, and researchers access the subsequent data (e.g. genomic sequencing data generated from such materials) to maximise their use as a valuable resource. Against this backdrop, for patients, research participants, and others to make their data available to research could seem as the obviously right thing to do: in our roles as citizens, patients, and researchers, we participate and share data. The ability to benefit from this sharing of data for research, however, is not just contingent on researchers' access to data, but also on other contextual factors. This includes the research question(s) asked, whether social and economic equity has been a concern in the curation, use, and translation of research findings, ethical concerns around data ownership, the implications of data sharing for individuals and groups, standards and values of data quality, and the likelihood that research findings will lead to realworld changes (e.g. changes in diagnostic or therapeutic processes and instruments, etc.). Due to the asymmetry in infrastructures, resources, and capacity in data generation, storage, and analysis between researchers working in institutions in highincome countries (HICs) and low-and-middle-income countries (LMICs), questions are now being asked as to what are the benefits from this data sharing, who is benefiting, and its impact on equity. It is of key importance that open science policies pay attention to the political, economic and social factors that play such an important role in shaping who benefits from data sharing. To discuss some of these issues, in July 2021 a plenary on "Open science, data sharing and solidarity: who benefits?" was held at the biennial conference of the International Society for the History, Philosophy, and Social Studies of Biology (ISHPSSB). The panelists (Sabina Leonelli, Ambroise Wonkham, Barbara Prainsack, Calvin Ho, Stefano Canali, and Matthew Mayernik) and chair (Katherine Littler) approached the topic from an array of perspectives. Overall, data sharing was perceived to be an important and beneficial practice. However its direction must be shifted so that it gives a more central role to equity (Nature, 2021). In this paper we report on some of the issues discussed, in particular conceptualizing open science, equity and benefit, and the governance of international data sharing for research. 1.1 Framing open science In reflecting on open science, the concept was framed in several different ways: it is as a core set of values to guide research, a moral standard, a project on standardization, and practically, as a way in which to overcome disciplinary silos. However it 1 3 Open science, data sharing and solidarity: who benefits? Page 3 of 8 115 may be framed or conceived, the need for care around the language of data sharing was stressed. Comparisons between data and oil, exhaust, or other forms of corporate commodities were described as being unhelpful, and caution was urged in the use of these metaphors (Mayernik & Acker, 2018). Discussions then turned to the implementation of open science and the sharing of data for research. It was noted that this is done through a set of common principles that include transparency, speed, reproducibility, and data quality (Wilkinson et al., 2016). While these terms may seem to be unambiguous, discrepancies arise in their application to the various different contexts in which data is collected, stored, and shared. The meaning of these principles and how they may be applied in the various contexts in which research takes place needs to be addressed as they profoundly impact research, research methods, and good research practice. 1.2 Equity and benefit A key focus of discussion pertained to equity and benefit in the sharing of data in research. Equity is a long-standing issue in health research, regarding ancestry research participants, the research topics, the funding allocations, workforces of researchers, and the access to publishing outlet particularly for highly expensive open access journals (Munung et al., 2018; Staunton & de Vries, 2020). This inequity has become a scientific problem in interpreting data in genomic research. The non-inclusion of enough population of African ancestry in large genomic research has resulted in at least 10% of variants missing from the reference genome. The dataset itself may also be bias, as there is limited data on populations in LMICs and marginalized populations, and this lack of diversity is preventing the equitable realization of the promise of genomics research (Rotimi & Adeyemo, 2021; Wonkam, 2021). Furthermore, researchers in LMICs often respond to funding calls that are set by funders in HICs. This is problematic as it can mean that the research agenda is not set by local research needs, but rather by the funders of research. In many of these schemes, collaboration between HICs and LMICs are encouraged with limited scope for collaboration between researchers in LIMCs within a particular region. Genomic research in Africa was cited as an example where there has been a shift in research practices in the past 10 years with some domestication of the research on the continent, but challenges remain. A second problem related to HICs funders setting the research agenda is that they also set the conditions of funding. Generally, data sharing is required as a condition of funding. This too often serves the interests of those setting the agenda, and can end up overriding ethical concerns with personal data sharing and ownership on the ground. It also can result in a situation where there is formal formal equality in the access to data (where it is actually achieved (Bezuidenhout et al., 2017)) does not result in equity in benefit from the data. On the contrary, it can lead to a 'Matthew effect' whereby those who have better resources and greater capabilities are able to obtain value from research data (Merton, 1968), increasing the gap between resource-poor and research-rich areas and regions. Indeed, in the context of genomic research in Africa, published papers historically lacked local authors from where the C. Staunton et al. 1 3 115 Page 4 of 8 data and samples were obtained, but served to enhance the careers of scientists in HICs (Sherman et al., 2019; Wonkam et al., 2011). In reflecting on equity in data sharing in genomics research, panelists noted that access to the data is just one issue. Equity in the sharing of data for research is also contingent on access to the necessary technological and personnel resources to analyze and store the data, to translate findings into healthcare applications, and importantly, ensure that the local populations have access to these therapies. This echoes questions previously asked about what good is it to identify variants for breast cancer, for example, in people who won't have access to medical treatments (Wonkam, 2021). The current system of open science needs to consider more systematically the diverse settings in which research takes place, as it rests on the assumption that all have access to the technology necessary to benefit from data sharing initiatives. A further challenge is that the current system does not always reward the people doing most of the work (Pinel et al., 2020). The National Academies of Science 1985 Report was referenced as continuing to be of relevance today where it states that "sharing data mainly benefits society" but the "costs are born by the initial investigators" (NAS, 1985). The costs in data collection are immediate and local but the benefits often take time to accrue, are downstream and diffuse. The context in which data is shared can also impact benefit. If data is exchanged between researchers on a person-to-person basis, it is typically straightforward for the original data collector to see the downstream impacts of the secondary data use. This is much more challenging if data is accessed from a data repository, as secondary use is difficult to track and may never be known by the initial data collectors. Finally, panelists noted that benefit is also contingent on access to good quality data. There is a need to ensure that good quality standards are maintained when the context in which they are used change, but it is difficult to apply global and general measures of quality (Leonelli, 2017). Quality can be harder to track and maintain when data is accessed from a repository without crucial information of contextual features, the diversity of research practices, environments, techniques are not acknowledged, and technology-specific and private solutions are perused. Data quality is thus closely tied to questions of metadata quality, completeness, and appropriateness for the potential data users (Rajesh et al., 2021). With these challenges in mind, the panelists did offer some recommendations to enable equity in data sharing. Academic programs in data science and data curation must be established in universities across the world to train personnel with the necessary skills to manage, interpret, and preserve the data (Prainsack, 2019; Wonkam, 2021). They must be funded and their laboratories must have the necessary technology and infrastructure to handle and store the data. This will require the establishment of data centres across the globe. Otherwise, experienced scientists will be forced to send data overseas for analysis and storage. There is a need to re-look at the funding of research. Research funding needs to address the critical needs discussed above to enable the equitable sharing of data in research. Equally important, data sharing requirements as a condition of funding needs to be re-examined at. It was noted that the South Africa Protection of Personal Information Act (POPIA) 2013 recently came into force and 1 3 Open science, data sharing and solidarity: who benefits? Page 5 of 8 115 it impacts the sharing of genomic data. Genetic data is viewed as "special personal information" and its use generally prohibited unless it comes within one of the grounds of permitted use. Similar data protection regulations with similar provisions are in place in many jurisdictions across the world and may impact on the legality of some of the data sharing conditions of funding (Mascalzoni et al., 2019). Panelists also pointed out that there are examples of responsible and effective data sharing, such as the Global Initiative on Sharing Influenza Data (GISAID), that should be seen as best practice in what open science should aspire to. 1.3 Governance Related to the issue of equity and benefit is the governance of data sharing. In this context, panellists particularly focused on where the responsibility should lie for facilitating the conversation, developing rules, and holding those involved in the sharing of data to account. Conversations on developing sustainable data practices should not just be a scientific one, but must include the public, national and international policy makers, and funders. Open science and open data are as much institutional endeavours as they are technical or scientific projects (Leonelli, 2010). It is therefore crucial that there is wide engagement across all sectors of society, as other parties may want to repurpose the data for purposes beyond its intended uses at collection. Questions were asked, however, on how best to develop practices that reflect a diversity of views both across the differing stakeholders, but also across the differing contexts in which science takes place, while also allow science to continue and progress. The role of geopolitics in this conversation was acknowledged. This may be due to scientific competition between different nations, but equally could be due to the need to protect data as a resource. As one example, caution was urged about the need to prevent a "gold rush" for African genomic data. Considering this geopolitical climate, an honest broker was called for to drive forward a collective agenda in data system. Specific legislation on open science was called for, as well as the need for national and international governance to stop the exploitation of data. Open science was described as an instrument of neo-liberalism that is reflective of an Anglo-American narrative. This is in part due to the fact that open science has been shaped by agendas set in HICs with little or no consideration of other contexts (Maxson Jones et al., 2018). It was suggested that a federated system of governance that is participatory in approach could change the narrative. A federated system enables legal data control to remain within the country from which the data originates and the Global Alliance for Genomics and Health (GA4H) was cited as a good example (GA4H, 2016). What is important is that in the development of standards and governance, a contextual approach is taken, that is responsive to the different contexts in which data takes place, the different repositories in which data is held, the different relationships in which data is exchanged, and the different type of data that is shared (Canali, 2020). C. Staunton et al. 1 3 115 Page 6 of 8 2 Conclusion Data-intensive methods are transforming every aspect of biological and biomedical research, but changes are required so that it develops in a more equitable manner, in data generation, access, analysis capacity and intellectual property regimes. This year (2021) marks the 25th anniversary of the Bermuda Principles on DNA Sequence and Data Sharing (1996). Research adherence to the accord shows mixed balances: more success around the sharing aspect of it in some parts of the world and less success on delivering benefits to specific communities. A critical investigation of who benefits is fundamental to the entire enterprise and in moving forward, consideration of equity must be central. Conversations on how to achieve this, must be free of the current narrative that primarily rewards those in HICs who have set the agenda. Despite the problems that are inherent in the current system, there are many examples of good and responsible practices in data sharing. Just as organisations like the Research Data Alliance, CODATA and World Data Systems have been advocating (CODATA, 2020), these examples must be examined, and used to inform governance models for equitable data sharing. Authors' contributions CS & CAB conceptualised the plenary. SC, CH, SL, MM, BP & AW all presented. CS drafted the paper based on the contributions of the panelists and chair, and revised the paper based on feedback. All authors provided comments on the paper and approved the final paper. Funding The authors thank the Department of Innovation, Research and University and Museums of the Autonomous Province of Bozen/Bolzano for covering the Open Access publication costs. CS is funded by CHRIS-2D (FESR1102) and funded by the Department of Innovation, Research and Universities of the Province of South Tyrol. MM's contributions are based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation under Cooperative Agreement No. 1852977. CAB is funded by the U.S. National Science Foundation (NSF) under Grant No. SES-1849307–0. SL was funded by the European Research Council grant agreement 101001145 ("A Philosophy of Open Science for Diverse Research Environments"). Declarations Conflicts of interest The author declares that they have no conflict of interest. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. 1 3 Open science, data sharing and solidarity: who benefits? Page 7 of 8 115 References Bezuidenhout, L. M., et al. (2017). Beyond the digital divide: Towards a situated approach to open data. Science and Public Policy, 44(4), 464–475. https:// doi. org/ 10. 1093/ scipol/ scw036 Canali, S. (2020). Towards a contextual approach to data quality. Data, 5(4), 90. https:// doi. org/ 10. 3390/ data5 040090 Genomic sequencing in pandemics The Lancet (no date). Available at: https:// www. thela ncet. com/ journ als/ lancet/ artic le/ PIIS0 1406736(21) 00257-9/ fullt ext (Accessed: 19 May 2021). Leonelli, S. (2010). Documenting the emergence of bio-ontologies: Or, why researching bioinformatics requires HPSSB. History and Philosophy of the Life Sciences, 32(1), 105–125. Leonelli, S. (2017). Global data quality assessment and the situated nature of "best" research practices in biology. Data Science Journal, 16, 32. https:// doi. org/ 10. 5334/ dsj2017032 Mascalzoni, D. et al. (2019) 'Are requirements to deposit data in research repositories compatible with the European Union's general data protection regulation?' Annals of Internal Medicine [Preprint]. Available at: https://www.acpjournals.org/doi/abs/https:// doi. org/ 10. 7326/ M182854 (Accessed: 22 Sept 2020). Maxson Jones, K., Ankeny, R. A., & Cook-Deegan, R. (2018). The Bermuda triangle: The pragmatics, policies, and principles for data sharing in the history of the human genome project. Journal of the History of Biology, 51(4), 693–805. https:// doi. org/ 10. 1007/ s107390189538-7 Mayernik, M. S., & Acker, A. (2018). Tracing the traces: The critical role of metadata within networked communications. Journal of the Association for Information Science and Technology, 69(1), 177– 180. https:// doi. org/ 10. 1002/ asi. 23927 Merton, R. K. (1968). The Matthew effect in science. Science, 159(3810), 56–63. https:// doi. org/ 10. 1126/ scien ce. 159. 3810. 56 Munung, N. S., Mayosi, B. M., & de Vries, J. (2018). Genomics research in Africa and its impact on global health: Insights from African researchers. Global Health, Epidemiology and Genomics. https:// doi. org/ 10. 1017/ gheg. 2018.3 Open science for a global transformation: CODATA coordinated submission to the UNESCO Open Science Consultation | Zenodo. (2020). Available at: https:// zenodo. org/ record/ 39354 61#. YUqTd p1Kg2w (Accessed: 22 September 2021). Pinel, C., Prainsack, B., & McKevitt, C. (2020). Caring for data: Value creation in a data-intensive research laboratory. Social Studies of Science, 50(2), 175–197. https:// doi. org/ 10. 1177/ 03063 12720 906567 Prainsack, B. (2019). Precision medicine needs a cure for inequality. Current History, 118(804), 11–15. https:// doi. org/ 10. 1525/ curh. 2019. 118. 804. 11 Rajesh, A., et al. (2021). Improving the completeness of public metadata accompanying omics studies. Genome Biology, 22(1), 106. https:// doi. org/ 10. 1186/ s1305902102332-z Rotimi, C. N., & Adeyemo, A. A. (2021). From one human genome to a complex tapestry of ancestry. Nature, 590(7845), 220–221. https:// doi. org/ 10. 1038/ d4158602100237-2 Sharing research data (1996) | The national academies press (no date). Available at: https:// www. nap. edu/ catal og/ 2033/ shari ngresea rchdata (Accessed: 22 Sept 2021). Sherman, R. M., et al. (2019). Assembly of a pan-genome from deep sequencing of 910 humans of African descent. Nature Genetics, 51(1), 30–35. https:// doi. org/ 10. 1038/ s415880180273-y Staunton, C., & de Vries, J. (2020). The governance of genomic biobank research in Africa: Reframing the regulatory tilt. Journal of Law and Biosciences, 7(1), 1–20. THE Global ALLIANCE FOR GENOMICS AND HEALTH. (2016). A federated ecosystem for sharing genomic, clinical data. Science, 352(6291), 1278–1280. https:// doi. org/ 10. 1126/ scien ce. aaf61 62 (2021) The next 20 years of human genomics must be more equitable and more open' Nature, 590(7845): 183–184. https:// doi. org/ 10. 1038/ d4158602100328-0. Walport, M., & Brest, P. (2011). Sharing research data to improve public health. The Lancet, 377(9765), 537–539. https:// doi. org/ 10. 1016/ S01406736(10) 62234-9 Wilkinson, M. D., et al. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https:// doi. org/ 10. 1038/ sdata. 2016. 18 Wonkam, A., et al. (2011). Ethics of human genetic studies in Sub-Saharan Africa: The case of cameroon through a bibliometric analysis. Developing World Bioethics, 11(3), 120–127. https:// doi. org/ 10. 1111/j. 14718847. 2011. 00305.x C. Staunton et al. 1 3 115 Page 8 of 8 Wonkam, A. (2021). Sequence three million genomes across Africa. Nature, 590(7845), 209–211. https:// doi. org/ 10. 1038/ d4158602100313-7 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Animal Models in Neuropsychiatry: Do the benefits outweigh the moral costs?

Philosophy

Animal Models in Neuropsychiatry: Do the Benefits Outweigh the Moral Costs? Carrie Figdor (Forthcoming in Cambridge Quarterly of Healthcare Ethics; final accepted draft) Abstract: Animal models have long been used to investigate human mental disorders, including depression, anxiety, and schizophrenia. This practice is usually justified in terms of the benefits (to humans) outweighing the costs (to the animals). I argue on utility maximization grounds that we should phase out animal models in neuropsychiatric research. Our leading theories of how human minds and behavior evolved invoke sociocultural factors whose relation to nonhuman minds, societies, and behavior has not been homologized. Thus it is not at all clear that we are gaining the epistemic or clinical benefits we want from this animal-based research. Acknowledgements: This paper was prompted by an invitation to speak in the Case Studies in Convergence Ethics speaker series, jointly held by TU Delft and Erasmus University Medical Center, in March 2022. I thank Dr. Cristina Richie for the invitation, and Drs. Richie, Dorothea Horstkotter, and Katherine Bassil for very helpful questions that improved the original manuscript. Introduction It is a moral truism that you should not treat human beings like lab rats. This paper argues that we also should not treat lab rats like lab rats, at least not insofar as neuropsychiatric research is concerned. Such treatment is not justified even if we assume that the intrinsic moral value of human lives always outweighs that of animal lives and that this research was justified by its epistemic (and eventually clinical) consequences in the past. We should phase it out starting now. I present several independent but mutually supporting arguments for this conclusion. They do not rely on the widely accepted idea that the pain and suffering of animals has moral weight, and that we ought to stop this research to avoid their pain and suffering. For the sake of argument, I will presuppose that no amount of their pain and suffering can outweigh the benefits to humans of this research. As blind chicken arguments show1, when forced to confront our responsibility for making animals suffer for our benefit, we can simply persist in our behavior without moral qualms by eliminating their ability to feel pain. Instead, I argue that the means of continued animal 2 sacrifice do not justify the ends of human benefits because their efficacy towards those ends is no longer clear. We should phase out using animal models because it is in our self-interest to do so. Not incurring additional moral debt is a felicitous side effect. Because moral appeals in themselves only go so far, this approach may raise the probability that the recommended phase-out will actually occur. Jointly Sufficient Reasons to Phase Out Neuropsychiatric Research With Animals In what follows I present four mutually supporting arguments for the conclusion that neuropsychiatric research using animal models should be phased out. In what follows, "research" refers to research intended to provide knowledge about, and eventual clinical treatments for, human disorders that have a psychological component or co-morbidity – that is, mental disorders such as anxiety and depression, Parkinson's disease, schizophrenia, and addiction.2 This may be a wider net than "neuropsychiatric condition", but the conditions that count as neuropsychiatric are key targets of the research I target. Any definition of "mental disorder" that captures these paradigm cases will suffice for my argument. What matters is this: the reason we use animal models in the first place is to obtain epistemic benefits in the first instance without endangering human lives, and this reason fails in the neuropsychiatric case. I set aside the possibility of arguing in similar fashion for phasing out animal models in biomedical research for disorders that do not have psychological components. Almost any disorder will have psychological and social consequences, but that is another issue. The Consent Problem as Background. 3 No discussion of the use of animals in neuropsychiatric research can avoid the fundamental starting point that the benefits go to the humans while the animals bear all the costs, usually including death in order to examine their brains. This is to say the animals are forced into extreme altruistic behavior, defined in general as behavior that benefits others at the personal cost of the behaving individual.3 This moral problem is avoided with humans via the construct of informed consent. A human can agree to be used as an experimental subject without any expected benefit for themselves. Such use is morally permissible if appropriate consent procedures are followed. Animals are not asked if they consent – they are involuntary "martyrs for medicine".4 Even if human consent is ethically required for using animal-derived medical treatments on someone5, the animals are not considered at all. Of course, it is a presupposition of animal research that the animals are not capable of consent, therefore the very idea of their providing informed consent cannot even arise.6 Whether this is true is questionable: at the very least, animals do not go willingly to their deaths. The possibility of consent includes the possibility of always refusing. With humans a similar inability to consent does not entail the permissibility of indiscriminate treatment. The responsibility for informed consent is transferred to a guardian. But if there is no one to consent on behalf of a human subject, the moral default is that the human cannot be a research subject. Thus, the facts that lab animals cannot offer consent in the usual way, are not given the opportunity to refuse in their own ways, and are not associated with humans who can consent on their behalf (e.g. via ownership) do not make their use as research subjects morally permissible. Ultimately, the moral permissibility that obviates consent in the case of nonhumans rests on the conviction that human lives are morally more valuable than those of animals. The moral status of humans simply outweighs that of any nonhuman. Whether this traditional view is morally 4 justified is dubious.7 But even if we assume the traditional moral status difference, it is also widely accepted that animals have some claim to minimally moral treatment. This is due to an ability to feel pain combined with a moral norm that pain should not be inflicted unnecessarily. But when is inflicting pain necessary? For humans, undergoing surgery causes pain (usually during recovery) that is a known side effect of a necessary means to regaining health. So too with human subjects research where pain is likely or inevitable: humans may be voluntary altruists. But with research animals, the pain (and death) is endured by the animal, while the benefit goes to the humans: their altruism is involuntary. We are back to the issue of consent. It seems lab animals are stuck providing humans with benefits because humans have higher moral status. Because the conviction that humans have higher moral worth than nonhuman animals is so ingrained, the consent problem is unlikely to persuade many that animal-based research for neuropsychiatric conditions should be phased out. We do not need their consent, even if they or a human proxy could give it, because our high intrinsic moral worthiness suffices to justify the animals' sacrifice for our benefit given their low moral worthiness.8 But is it true that no amount of animal moral worth can ever amount to the moral worth of humans? Maybe if only a few dozen, or few hundred, lab rats lost their lives in the service of a potential increase in human well-being, their involuntary altruism would be justified given the lopsided moral values assigned to the human and rat lives respectively. Do the values of the lives of vast numbers of animals used in research never amount to enough to outweigh the moral value of a human? Perhaps making this comparison itself is so offensive to our sense of human moral value that we cannot even consider it. I propose to simply grant these moral convictions for the sake of argument. Trying to end or curtail research using animals by overturning these convictions is almost certainly a lost cause. Even if we asked for their consent, their refusals would fall on deaf ears. In a moral version of 5 Zeno's paradoxes, I will grant that no amount of adding up those moral statuses can ever quite reach the moral status of a human. What will be more persuasive is to argue that humans are not in fact benefitting as much as we may think we do from animal-based research. Then the question becomes tractable. If humans are not getting the benefits we want from this research, then the reason for pursuing it falls apart. We are better off seeking alternative means. If we also avoid incurring further moral debt, so much the better. If the benefits we gain from animal-based research are marginal at best, we may as well change our research habits to something more consonant with our moral values – even if we continue to value human lives more than animal lives. A Three-Stage Argument Against Animal Models in Neuropsychiatry In this section I present three overlapping considerations that jointly lead to the conclusion that we should phase out animal models in neuropsychiatric research. The epistemic benefits we hope to get from them are not clearly justified, so it is in our self-interest to seek this information by other means. The Sociocultural Environment Problem: Neuropsychiatric disorders depend on specific human sociocultural factors, even given neural and behavioral homologies across species. Our leading current theories of human cognitive (or broadly psychological) evolution posit that our minds are the result of a complex process of gene-culture co-evolution, in which sociocultural factors play an essential and primary role.9 For example, Cecilia Heyes' theory of "cognitive gadgets", or "distinctively human cognitive mechanisms", holds that such abilities as mind-reading and language have the forms they do because of how they are shaped in evolution by human methods of social and cultural information transmission. 6 Although sociocultural evolution theorists may also make claims about the uniqueness of these advanced abilities, it is not critical in this context that they be considered uniquely human. If they were, then using animal models for disorders of uniquely human psychological abilities would be a non-starter, even if human brain function is an important factor in their etiology. Rather, I will assume that there are homologies in brains and behavior relevant to neuropsychiatric conditions. Even so, these theories imply that brain similarities are less relevant to specifically human mental disorders than sociocultural factors, given the importance of the latter in phylogeny as well as ontogeny. A brain that is not primed for human sociocultural contexts will not be a brain that can be disordered in relation to those contexts. It can be disordered in similar, perhaps homologous, ways, but what is likely very critical in human neuropsychiatric disorders may be the sociocultural factors specific to us as an evolutionary matter, as well as in development. The Ignorance Problem: We are not justified in assuming experimentally induced behaviors in lab animals are relevantly similar to the human behaviors associated with human neuropsychiatric disorders. The Sociocultural Environment problem points to the need for greater attention to the social and behavioral aspects of neuropsychiatric disorders. But even if lab experiments could in principle capture some relevant similarities with human behavior, we are likely designing experiments that do not in fact capture those similarities. We may induce animal behavior that we interpret as depression-like or optimism-like, but what makes it "like" is not theoretically grounded in established or even hypothesized homologies of socioculturally relevant human behaviors and animal behaviors. Stress hormones are homologous and levels of stress hormone can be measured across species. But depression-like behaviors are not homologous just because we use the same neuropsychiatric labels across species. In part, our ignorance is due to the fact that research funding 7 that gets funneled to animal-based lab research is funding not available for investigating sociobehavioral homologies relevant to neuropsychiatric disorder – or behavioral homologies in general. Whatever the reason, however, it would be pure luck if the animal behaviors we now induce in the lab are in fact homologous to the human ones they mimic.10 For we do not know what aspects of the human sociocultural conditions might be replicated in experimental paradigms and so whether our experiments with lab animals are in fact informative in the way they are intended. It follows that lab animals are likely undergoing experimental procedures that do not provide the epistemic benefit we are sacrificing them for. As a side note, the so-called translation problem of transferring the knowledge we gain from animal-based research to human clinical contexts has already been raised for non-psychiatric biomedical research.11 Such concerns easily extend to the knowledge gained in neuropsychiatric research and its transfer to human mental health contexts. Difficulty in translating animal-based research to humans is a general problem, but neuropsychiatric research is very likely to anchor the "unhelpful" end of this continuum. The Granularity Problem: We may have already passed the point where animal models can provide epistemic benefits relevant to neuropsychiatric disorders. The calculus of justifying animal research is dynamic, as is any cost-benefit analysis. In a past state of ignorance about neurobiology, the moral dangers of human subjects research may once have justified using animals instead, given the moral status differential noted above. This general point will certainly apply to neuropsychiatric research in particular. There is no doubt that knowledge about neural structure and function, and its biological context from genes to behavior, has increased tremendously in recent decades. We have gained much epistemic benefit from this research, some of which has accrued to neuropsychiatry and resulted in novel pharmacological 8 treatments. But while other animals can tell us a great deal about humans with respect to what we have in common, at some point human subjects are essential for progress. The epistemic benefits of animal research in any biomedical domain diminish over time, and human subjects are required for the fine-grained, species-specific information that is our ultimate goal. There may still be plenty to learn about chickens or mice, but its relevance to humans is marginal at best. Because of the phylogenetic and developmental links between human sociocultural factors, human brains, and human cognition and behavior, this point of no epistemic returns is likely to be reached far sooner for neuropsychiatric research than in biomedical research that aims at physical disorder. When it comes to neuropsychiatric research, social and behavioral factors have long been sidelined in favor of the neurophysiological factors that can be manipulated in the lab using animal subjects.12 But the cost-benefit calculus today is not what it was a few decades ago. It is likely that this research has already passed the point where it contributes to the goal of learning about, and ultimately treating, human neuropsychiatric disorder. Further fine-grained knowledge of neurobiological mechanisms will do little to address the yawning gaps in our knowledge of the relevant sociocultural factors behind human mental disorder, the relevant homologies between human behavior and induced lab animal behavior, and the species-specific aspects of human neuropsychiatric conditions. We would be better off seeking the knowledge we want with other types of research. Bringing more human subjects into research pools is one alternative, despite all the moral concerns that must be addressed. Using computer simulations with human avatars is another. What is critical is to recognize that what maximized utility in the past does not automatically maximize utility in the future. Acknowledging that our epistemic self-interest may no longer best be served 9 using animal models is the first step. It promises to put us on the road to phasing out animal research and thereby avoiding its moral costs. Conclusion I have argued that the continued use of animal models for neuropsychiatric research should be phased out. It is not clear that their sacrifices lead to the benefits for humans that allegedly justify the use of animal models in the first place. A summary of my argument is as follows: P1. We force animals into involuntary altruism for our epistemic benefit, based on a conviction that our lives are more valuable. P2. Ceteris paribus, if we can get the desired epistemic benefits by means that do not require involuntary altruism, we should. P3. Human neuropsychiatric disorders depend on human psychology, which is shaped by human sociocultural factors in phylogeny and ontogeny. At best animal proxies only capture similarities (homologies or analogies), be that brains or behavior. P4. We are relatively ignorant of the relation between artificially induced lab animal behaviors and human behaviors shaped by human sociocultural factors. Our assumption of relevant similarity, in particular homology, is not clearly justified. P5. So the animals' involuntary altruism to date may be a waste due to poor experimental design relative to our ultimate epistemic goals. P6. But even if these experiments have provided valuable knowledge about neurophysiology and neural structure, we are likely near or have passed the point where animal models can provide the fine-grained knowledge we want regarding human neuropsychiatric disorder, based as it is in sociocultural factors. 10 Conclusion: We should phase out animal models in favor of better research protocols. A morally beneficial side effect of this shift would be that we would no longer be forcing animals into involuntary altruism. Perhaps it is sad that no longer engaging in morally questionable treatment of animals is, in effect, a moral cherry atop a utility-maximizing cake. But we would not be engaging in morally questionable means to get out of the problem, as with the blind chickens. We would be changing our behavior in a way that yields a morally beneficial byproduct. That is enough of a win for me. 1 Thompson P. The Opposite of Enhancement: Nanotechnology and the Blind Chicken Problem. Neuroethics 2008; 2: 305-316. 2 Willner P. The validity of animal models of depression. Psychopharmacology 1984; 83: 1-16. Castner S, Goldman-Rakic P, Williams G. Animal models of working memory: insights for targeting cognitive dysfunction in schizophrenia. Psychopharmacology 2003; 174: 111-125. Warnick J, Huang C, Acevedo E, Sufka K. Modeling the anxiety-depression continuum in chicks. Journal of Psychopharmacology 2009; 23: 143-156. Fernando A, Robbins T. Animal models of neuropsychiatric disorders. Annual Review of Clinical Psychology 2011; 7: 39-61. Sufka K, Weldon M, Allen C. The case for animal emotions: modeling neuropsychiatric disorders. In J. 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Cambridge Quarterly of Healthcare Ethics 2022; 31 (1): 5972. 7 Figdor C. The Psychological Speciesism of Humanism. Philosophical Studies 2021; 178; 15451569. 8 Angela Martin (see note 6) argues that animal research should be more aligned with the constraints on human subjects research if they have the same moral worth as humans. This is too big an "if" for many. And while Martin is correct in principle that stopping the research might mean the animals themselves would not get any benefits, in practice it is unlikely that much benefit would accrue to the animal subjects (or their descendants) and questionable whether it could ever 11 outweigh the sacrifices made to get it. However, Martin's paper is concerned with animal research in general, in particular nonharmful and low-risk research. In such cases her arguments are stronger. 9 Boyd R, Richerson P. Culture and the Evolutionary Process. Chicago: Chicago University Press; 1985. Sterelny K. Thought in a Hostile World: The evolution of human cognition. Oxford: Oxford University Press; 2003; Heyes C. Cognitive Gadgets. Oxford: Oxford University Press; 2018. 10 A major research initiative (https://sites.google.com/view/socialroots/the-project) has been launched to address some of these concerns. 11 Sullivan J. The Multiplicity of Experimental Protocols: A challenge to reductionist and nonreductionist models of the unity of neuroscience. Synthese 2009; 167: 511-539; Bolker J. Animal models in translational research: Rosetta stone or stumbling block? Bioessays 2017; 39: 1700089; Piotrowska M. From humanized mice to human disease: guiding extrapolation from model to target. Biology and Philosophy 2013; 28: 439-455. 12 Ankeny R, Leonelli S. Model Organisms. Cambridge: Cambridge University Press; 2020; see note 2, Nelson 2018.