## 1. What Are Big Data?



We are witnessing a progressive "datafication" of social
life. Human activities and interactions with the environment are being
monitored and recorded with increasing effectiveness, generating an
enormous digital footprint. The resulting "big data" are a
treasure trove for research, with ever more sophisticated
computational tools being developed to extract knowledge from such
data. One example is the use of various different types of data
acquired from cancer patients, including genomic sequences,
physiological measurements and individual responses to treatment, to
improve diagnosis and treatment. Another example is the integration of
data on traffic flow, environmental and geographical conditions, and
human behaviour to produce safety measures for driverless vehicles, so
that when confronted with unforeseen events (such as a child suddenly
darting into the street on a very cold day), the data can be promptly
analysed to identify and generate an appropriate response (the car
swerving enough to avoid the child while also minimising the risk of
skidding on ice and damaging to other vehicles). Yet another instance
is the understanding of the nutritional status and needs of a
particular population that can be extracted from combining data on
food consumption generated by commercial services (e.g., supermarkets,
social media and restaurants) with data coming from public health and
social services, such as blood test results and hospital intakes
linked to malnutrition. In each of these cases, the availability of
data and related analytic tools is creating novel opportunities for
research and for the development of new forms of inquiry, which are
widely perceived as having a transformative effect on science as a
whole.



A useful starting point in reflecting on the significance of such
cases for a philosophical understanding of research is to consider
what the term "big data" actually refers to within
contemporary scientific discourse. There are multiple ways to define
big data (Kitchin 2014, Kitchin & McArdle 2016). Perhaps the most
straightforward characterisation is as *large* datasets that
are produced in a *digital* form and can be analysed through
*computational* tools. Hence the two features most commonly
associated with Big Data are volume and velocity. *Volume*
refers to the size of the files used to archive and spread data.
*Velocity* refers to the pressing speed with which data is
generated and processed. The body of digital data created by research
is growing at breakneck pace and in ways that are arguably impossible
for the human cognitive system to grasp and thus require some form of
automated analysis.



Volume and velocity are also, however, the most disputed features of
big data. What may be perceived as "large volume" or
"high velocity" depends on rapidly evolving technologies
to generate, store, disseminate and visualise the data. This is
exemplified by the high-throughput production, storage and
dissemination of genomic sequencing and gene expression data, where
both data volume and velocity have dramatically increased within the
last two decades. Similarly, current understandings of big data as
"anything that cannot be easily captured in an Excel
spreadsheet" are bound to shift rapidly as new analytic software
becomes established, and the very idea of using spreadsheets to
capture data becomes a thing of the past. Moreover, data size and
speed do not take account of the diversity of data types used by
researchers, which may include data that are not generated in digital
formats or whose format is not computationally tractable, and which
underscores the importance of data provenance (that is, the conditions
under which data were generated and disseminated) to processes of
inference and interpretation. And as discussed below, the emphasis on
physical features of data obscures the continuing dependence of data
interpretation on circumstances of data use, including specific
queries, values, skills and research situations.



An alternative is to define big data not by reference to their
physical attributes, but rather by virtue of what can and cannot be
done with them. In this view, big data is a heterogeneous ensemble of
data collected from a variety of different sources, typically (but not
always) in digital formats suitable for algorithmic processing, in
order to generate new knowledge. For example boyd and Crawford (2012:
663) identify big data with "the capacity to search, aggregate
and cross-reference large datasets", while O'Malley and
Soyer (2012) focus on the ability to interrogate and interrelate
diverse types of data, with the aim to be able to consult them as a
single body of evidence. The examples of transformative "big
data research" given above are all easily fitted into this view:
it is not the mere fact that lots of data are available that makes a
different in those cases, but rather the fact that lots of data can be
mobilised from a wide variety of sources (medical records,
environmental surveys, weather measurements, consumer behaviour). This
account makes sense of other characteristic "v-words" that
have been associated with big data, including:


* *Variety* in the formats and purposes of data, which may
include objects as different as samples of animal tissue, free-text
observations, humidity measurements, GPS coordinates, and the results
of blood tests;
* *Veracity*, understood as the extent to which the quality
and reliability of big data can be guaranteed. Data with high volume,
velocity and variety are at significant risk of containing
inaccuracies, errors and unaccounted-for bias. In the absence of
appropriate validation and quality checks, this could result in a
misleading or outright incorrect evidence base for knowledge claims
(Floridi & Illari 2014; Cai & Zhu 2015; Leonelli 2017);
* *Validity*, which indicates the selection of appropriate
data with respect to the intended use. The choice of a specific
dataset as evidence base requires adequate and explicit justification,
including recourse to relevant background knowledge to ground the
identification of what counts as data in that context (e.g., Loettgers
2009, Bogen 2010);
* *Volatility*, i.e., the extent to which data can be relied
upon to remain available, accessible and re-interpretable despite
changes in archival technologies. This is significant given the
tendency of formats and tools used to generate and analyse data to
become obsolete, and the efforts required to update data
infrastructures so as to guarantee data access in the long term
(Bowker 2006; Edwards 2010; Lagoze 2014; Borgman 2015);
* *Value*, i.e., the multifaceted forms of significance
attributed to big data by different sections of society, which depend
as much on the intended use of the data as on historical, social and
geographical circumstances (Leonelli 2016, D'Ignazio and Klein
2020). Alongside scientific value, researchers may impute financial,
ethical, reputational and even affective value to data, depending on
their intended use as well as the historical, social and geographical
circumstances of their use. The institutions involved in governing
and funding research also have ways of valuing data, which may not
always overlap with the priorities of researchers (Tempini 2017).



This list of features, though not exhaustive, highlights how big data
is not simply "a lot of data". The epistemic power of big
data lies in their capacity to bridge between different research
communities, methodological approaches and theoretical frameworks that
are difficult to link due to conceptual fragmentation, social barriers
and technical difficulties (Leonelli 2019a). And indeed, appeals to
big data often emerge from situations of inquiry that are at once
technically, conceptually and socially challenging, and where existing
methods and resources have proved insufficient or inadequate (Sterner
& Franz 2017; Sterner, Franz, & Witteveen 2020).



This understanding of big data is rooted in a long history of
researchers grappling with large and complex datasets, as exemplified
by fields like astronomy, meteorology, taxonomy and demography (see
the collections assembled by Daston 2017; Anorova et al. 2017; Porter
& Chaderavian 2018; as well as Anorova et al. 2010, Sepkoski 2013,
Stevens 2016, Strasser 2019 among others). Similarly, biomedical
research--and particularly subfields such as epidemiology,
pharmacology and public health--has an extensive tradition of
tackling data of high volume, velocity, variety and volatility, and
whose validity, veracity and value are regularly negotiated and
contested by patients, governments, funders, pharmaceutical companies,
insurances and public institutions (Bauer 2008). Throughout the
twentieth century, these efforts spurred the development of
techniques, institutions and instruments to collect, order, visualise
and analyse data, such as: standard classification systems and
formats; guidelines, tools and legislation for the management and
security of sensitive data; and infrastructures to integrate and
sustain data collections over long periods of time (Daston 2017).



This work culminated in the application of computational technologies,
modelling tools and statistical methods to big data (Porter 1995;
Humphreys 2004; Edwards 2010), increasingly pushing the boundaries of
data analytics thanks to supervised learning, model fitting, deep
neural networks, search and optimisation methods, complex data
visualisations and various other tools now associated with artificial
intelligence. Many of these tools are based on algorithms whose
functioning and results are tested against specific data samples (a
process called "training"). These algorithms are
programmed to "learn" from each interaction with novel
data: in other words, they have the capacity to change themselves in
response to new information being inputted into the system, thus
becoming more attuned to the phenomena they are analysing and
improving their ability to predict future behaviour. The scope and
extent of such changes is shaped by the assumptions used to build the
algorithms and the capability of related software and hardware to
identify, access and process information of relevance to the learning
in question. There is however a degree of unpredictability and opacity
to these systems, which can evolve to the point of defying human
understanding (more on this below).



New institutions, communication platforms and regulatory frameworks
also emerged to assemble, prepare and maintain data for such uses
(Kitchin 2014), such as various forms of digital data infrastructures,
organisations aiming to coordinate and improve the global data
landscape (e.g., the Research Data Alliance), and novel measures for
data protection, like the General Data Protection Regulation launched
in 2017 by the European Union. Together, these techniques and
institutions afford the opportunity to assemble and interpret data at
a much broader scale, while also promising to deliver finer levels of
granularity in data
 analysis.[1]
 They increase the scope of any investigation by making it possible
for researchers to link their own findings to those of countless
others across the world, both within and beyond the academic sphere.
By enhancing the mobility of data, they facilitate their repurposing
for a variety of goals that may have been unforeseeable when the data
were originally generated. And by transforming the role of data within
research, they heighten their status as valuable research outputs in
and of themselves. These technological and methodological developments
have significant implications for philosophical conceptualisations of
data, inferential processes and scientific knowledge, as well as for
how research is conducted, organised, governed and assessed. It is to
these philosophical concerns that I now turn.


## 2. Extrapolating Data Patterns: The Role of Statistics and Software



Big data are often associated to the idea of *data-driven*
research, where learning happens through the accumulation of data and
the application of methods to extract meaningful patterns from those
data. Within data-driven inquiry, researchers are expected to use data
as their starting point for inductive inference, without relying on
theoretical preconceptions--a situation described by advocates as
"the end of theory", in contrast to theory-driven
approaches where research consists of testing a hypothesis (Anderson
2008, Hey et al. 2009). In principle at least, big data constitute the
largest pool of data ever assembled and thus a strong starting point
to search for correlations (Mayer-Schonberger & Cukier 2013).
Crucial to the credibility of the data-driven approach is the efficacy
of the methods used to extrapolate patterns from data and evaluate
whether or not such patterns are meaningful, and what
"meaning" may involve in the first place. Hence, some
philosophers and data scholars have argued that 



> 
> 
> the most important and distinctive characteristic of Big Data [is] its
> use of statistical methods and computational means of analysis,
> (Symons & Alvarado 2016: 4) 
> 
> 
> 



such as for instance machine learning tools, deep neural networks and
other "intelligent" practices of data handling. 



The emphasis on statistics as key adjudicator of validity and
reliability of patterns extracted from data is not novel. Exponents of
logical empiricism looked for logically watertight methods to secure
and justify inference from data, and their efforts to develop a theory
of probability proceeded in parallel with the entrenchment of
statistical reasoning in the sciences in the first half of the
twentieth century (Romeijn 2017). In the early 1960s, Patrick Suppes
offered a seminal link between statistical methods and the philosophy
of science through his work on the production and interpretation of
data models. As a philosopher deeply embedded in experimental
practice, Suppes was interested in the means and motivations of key
statistical procedures for data analysis such as data reduction and
curve fitting. He argued that once data are adequately
*prepared* for statistical modelling, all the concerns and
choices that motivated data processing become irrelevant to their
analysis and interpretation. This inspired him to differentiate
between models of theory, models of experiment and models of data,
noting that such different components of inquiry are governed by
different logics and cannot be compared in a straightforward way. For
instance, 



> 
> 
> the precise definition of models of the data for any given experiment
> requires that there be a theory of the data in the sense of the
> experimental procedure, as well as in the ordinary sense of the
> empirical theory of the phenomena being studied. (Suppes 1962: 253)
> 
> 
> 
> 



Suppes viewed data models as necessarily statistical: that is, as
objects 



> 
> 
> designed to incorporate all the information about the experiment which
> can be used in statistical tests of the adequacy of the theory.
> (Suppes 1962: 258) 
> 
> 
> 



His formal definition of data models reflects this decision, with
statistical requirements such as homogeneity, stationarity and order
identified as the ultimate criteria to identify a data model Z and
evaluate its adequacy: 



> 
> 
> *Z* is an N-fold model of the data for experiment *Y* if and
> only if there is a set *Y* and a probability measure *P* on
> subsets of *Y* such that \(Y = \langle Y, P\rangle\) is a model
> of the theory of the experiment, *Z* is an N-tuple of elements of
> *Y*, and *Z* satisfies the statistical tests of homogeneity,
> stationarity and order. (1962: 259) 
> 
> 
> 



This analysis of data models portrayed statistical methods as key
conduits between data and theory, and hence as crucial components of
inferential reasoning.



The focus on statistics as entry point to discussions of inference
from data was widely promoted in subsequent philosophical work.
Prominent examples include Deborah Mayo, who in her book *Error and
the Growth of Experimental Knowledge* asked: 



> 
> 
> What should be included in data models? The overriding constraint is
> the need for data models that permit the statistical assessment of fit
> (between prediction and actual data); (Mayo 1996: 136) 
> 
> 
> 



and Bas van Fraassen, who also embraced the idea of data models as
"summarizing relative frequencies found in data" (Van
Fraassen 2008: 167). Closely related is the emphasis on statistics as
means to detect error within datasets in relation to specific
hypotheses, most prominently endorsed by the error-statistical
approach to inference championed by Mayo and Aris Spanos (Mayo &
Spanos 2009a). This
approach aligns with the emphasis on computational methods for data
analysis within big data research, and supports the idea that the
better the inferential tools and methods, the better the chance to
extract reliable knowledge from data.



When it comes to addressing methodological challenges arising from the
computational analysis of big data, however, statistical expertise
needs to be complemented by computational savvy in the training and
application of algorithms associated to artificial intelligence,
including machine learning but also other mathematical procedures for
operating upon data (Bringsjord & Govindarajulu 2018). Consider
for instance the problem of overfitting, i.e., the mistaken
identification of patterns in a dataset, which can be greatly
amplified by the training techniques employed by machine learning
algorithms. There is no guarantee that an algorithm trained to
successfully extrapolate patterns from a given dataset will be as
successful when applied to other data. Common approaches to this
problem involve the re-ordering and partitioning of both data and
training methods, so that it is possible to compare the application of
the same algorithms to different subsets of the data
("cross-validation"), combine predictions arising from
differently trained algorithms ("ensembling") or use
hyperparameters (parameters whose value is set prior to data training)
to prepare the data for analysis.



Handling these issues, in turn, requires 



> 
> 
> familiarity with the mathematical operations in question, their
> implementations in code, and the hardware architectures underlying
> such implementations. (Lowrie 2017: 3) 
> 
> 
> 



For instance, machine learning 



> 
> 
> aims to build programs that develop their own analytic or descriptive
> approaches to a body of data, rather than employing ready-made
> solutions such as rule-based deduction or the regressions of more
> traditional statistics. (Lowrie 2017: 4) 
> 
> 
> 



In other words, statistics and mathematics need to be complemented by
expertise in programming and computer engineering. The ensemble of
skills thus construed results in a specific epistemological approach
to research, which is broadly characterised by an emphasis on the
means of inquiry as the most significant driver of research goals and
outputs. This approach, which Sabina Leonelli characterised as
*data-centric*, involves "focusing more on the processes
through which research is carried out than on its ultimate
outcomes" (Leonelli 2016: 170). In this view, procedures,
techniques, methods, software and hardware are the prime motors of
inquiry and the chief influence on its outcomes. Focusing more
specifically on computational systems, John Symons and Jack Horner
argued that much of big data research consists of
*software-intensive science* rather than data-driven research:
that is, science that depends on software for its design, development,
deployment and use, and thus encompasses procedures, types of
reasoning and errors that are unique to software, such as for example
the problems generated by attempts to map real-world quantities to
discrete-state machines, or approximating numerical operations (Symons
& Horner 2014: 473). Software-intensive science is arguably
supported by an *algorithmic rationality* focused on the
feasibility, practicality and efficiency of algorithms, which is
typically assessed by reference to concrete situations of inquiry
(Lowrie 2017). 


## 3. Human and Artificial Intelligence



Algorithms are enormously varied in their mathematical structures and
underpinning conceptual commitments, and more philosophical work needs
to be carried out on the specifics of computational tools and software
used in data science and related applications--with emerging work
in philosophy of computer science providing an excellent way forward
(Turner & Angius 2019). Nevertheless, it is clear that whether or
not a given algorithm successfully applies to the data at hand depends
on factors that cannot be controlled through statistical or even
computational methods: for instance, the size, structure and format of
the data, the nature of the classifiers used to partition the data,
the complexity of decision boundaries and the very goals of the
investigation.



In a forceful critique informed by the philosophy of mathematics,
Christian Calude and Giuseppe Longo argued that there is a fundamental
problem with the assumption that more data will necessarily yield more
information: 



> 
> 
> very large databases have to contain arbitrary correlations. These
> correlations appear only due to the size, not the nature, of data.
> (Calude & Longo 2017: 595) 
> 
> 
> 



They conclude that big data analysis is by definition unable to
distinguish spurious from meaningful correlations and is therefore a
threat to scientific research. A related worry, sometimes dubbed
"the curse of dimensionality" by data scientists, concerns
the extent to which the analysis of a given dataset can be scaled up
in complexity and in the number of variables being considered. It is
well known that the more dimensions one considers in classifying
samples, for example, the larger the dataset on which such dimensions
can be accurately generalised. This demonstrates the continuing, tight
dependence between the volume and quality of data on the one hand, and
the type and breadth of research questions for which data need to
serve as evidence on the other hand.



Determining the fit between inferential methods and data requires high
levels of expertise and contextual judgement (a situation known within
machine learning as the "no free lunch theorem"). Indeed,
overreliance on software for inference and data modelling can yield
highly problematic results. Symons and Horner note that the use of
complex software in big data analysis makes margins of error
unknowable, because there is no clear way to test them statistically
(Symons & Horner 2014: 473). The path complexity of programs with
high conditionality imposes limits on standard error correction
techniques. As a consequence, there is no effective method for
characterising the error distribution in the software except by
testing all paths in the code, which is unrealistic and intractable in
the vast majority of cases due to the complexity of the code.



Rather than acting as a substitute, the effective and responsible use
of artificial intelligence tools in big data analysis requires the
strategic exercise of human intelligence--but for this to happen,
AI systems applied to big data need to be accessible to scrutiny and
modification. Whether or not this is the case, and who is best
qualified to exercise such scrutiny, is under dispute. Thomas Nickles
argued that the increasingly complex and distributed algorithms used
for data analysis follow in the footsteps of long-standing scientific
attempts to transcend the limits of human cognition. The resulting
epistemic systems may no longer be intelligible to humans: an
"alien intelligence" within which "human abilities
are no longer the ultimate criteria of epistemic success"
(Nickles forthcoming). Such unbound cognition holds the promise of
enabling powerful inferential reasoning from previously unimaginable
volumes of data. The difficulties in contextualising and scrutinising
such reasoning, however, sheds doubt on the reliability of the
results. It is not only machine learning algorithms that are becoming
increasingly inaccessible to evaluation: beyond the complexities of
programming code, computational data analysis requires a whole
ecosystem of classifications, models, networks and inference tools
which typically have different histories and purposes, and whose
relation to each other--and effects when they are used
together--are far from understood and may well be
untraceable.



This raises the question of whether the knowledge produced by such
data analytic systems is at all intelligible to humans, and if so,
what forms of intelligibility it yields. It is certainly the case that
deriving knowledge from big data may not involve an increase in human
understanding, especially if understanding is understood as an
epistemic skill (de Regt 2017). This may not be a problem to those who
await the rise of a new species of intelligent machines, who may
master new cognitive tools in a way that humans cannot. But as
Nickles, Nicholas Rescher (1984), Werner Callebaut (2012) and others
pointed out, even in that case "we would not have arrived at
perspective-free science" (Nickles forthcoming). While the human
histories and assumptions interwoven into these systems may be hard to
disentangle, they still affect their outcomes; and whether or not
these processes of inquiry are open to critical scrutiny, their telos,
implications and significance for life on the planet arguably should
be. As argued by Dan McQuillan (2018), the increasing automation of
big data analytics may foster acceptance of a Neoplatonist
*machinic metaphysics*, within which mathematical structures
"uncovered" by AI would trump any appeal to human
experience. Luciano Floridi echoes this intuition in his analysis of
what he calls the *infosphere*: 



> 
> 
> The great opportunities offered by Information and Communication
> Technologies come with a huge intellectual responsibility to
> understand them and take advantage of them in the right way.
> (2014: vii)
> 
> 
> 



These considerations parallel Paul Humphreys's long-standing
critique of computer simulations as *epistemically opaque*
(Humphreys 2004, 2009)--and particularly his definition of what
he calls *essential* epistemic opacity: 



> 
> 
> A process is essentially epistemically opaque to *X* if and only
> if it is *impossible*, given the nature of *X*, for
> *X* to know all of the epistemically relevant elements of the
> process. (Humphreys 2009: 618) 
> 
> 
> 



Different facets of the general problem of epistemic opacity are
stressed within the vast philosophical scholarship on the role of
modelling, computing and simulations in the sciences: the implications
of lacking experimental access to the concrete parts of the world
being modelled, for instance (Morgan 2005; Parker 2009; Radder 2009);
the difficulties in testing the reliability of computational methods
used within simulations (Winsberg 2010; Morrison 2015); the relation
between opacity and justification (Duran & Formanek 2018);
the forms of black-boxing associated to mechanistic reasoning
implemented in computational analysis (Craver and Darden 2013; Bechtel
2016); and the debate over the intrinsic limits of computational
approaches and related expertise (Collins 1990; Dreyfus 1992). Roman
Frigg and Julian Reiss argued that such issues do not constitute
fundamental challenges to the nature of inquiry and modelling, and in
fact exist in a continuum with traditional methodological issues
well-known within the sciences (Frigg & Reiss 2009). Whether or
not one agrees with this position (Humphreys 2009; Beisbart 2012), big
data analysis is clearly pushing computational and statistical methods
to their limit, thus highlighting the boundaries to what even
technologically augmented human beings are capable of knowing and
understanding.


## 4. The Nature of (Big) Data



Research on big data analysis thus sheds light on elements of the
research process that cannot be fully controlled, rationalised or even
considered through recourse to formal tools.


 One such element is the work required to present empirical data in
a machine-readable format that is compatible with the software and
analytic tools at hand. Data need to be selected, cleaned and prepared
to be subjected to statistical and computational analysis. The
processes involved in separating data from noise, clustering data so
that it is tractable, and integrating data of different formats turn
out to be highly sophisticated and theoretically structured, as
demonstrated for instance by James McAllister's (1997, 2007,
2011) and Uljana Feest's (2011) work on data patterns, Marcel
Boumans's and Leonelli's comparison of clustering
principles across fields (forthcoming), and James Griesemer's
(forthcoming) and Mary Morgan's (forthcoming) analyses of the
peculiarities of datasets. Suppes was so concerned by what he called
the "bewildering complexity" of data production and
processing activities, that he worried that philosophers would not
appreciate the ways in which statistics can and does help scientists
to abstract data away from such complexity. He described the large
group of research components and activities used to prepare data for
modelling as "pragmatic aspects" encompassing "every
intuitive consideration of experimental design that involved no formal
statistics" (Suppes 1962: 258), and positioned them as the
lowest step of his hierarchy of models--at the opposite end of
its pinnacle, which are models of theory. Despite recent efforts to
rehabilitate the methodology of inductive-statistical modelling and
inference (Mayo & Spanos 2009b), this approach has been shared by
many philosophers who regard processes of data production and
processing as so chaotic as to defy systematic analysis. This explains
why data have received so little consideration in philosophy of
science when compared to models and theory.



The question of how data are defined and identified, however, is
crucial for understanding the role of big data in scientific research.
Let us now consider two philosophical views--the
*representational view* and the *relational
view*--that are both compatible with the emergence of big
data, and yet place emphasis on different aspects of that phenomenon,
with significant implications for understanding the role of data
within inferential reasoning and, as we shall see in the next section,
as evidence. The *representational view* construes data as
reliable representations of reality which are produced via the
interaction between humans and the world. The interactions that
generate data can take place in any social setting regardless of
research purposes. Examples range from a biologist measuring the
circumference of a cell in the lab and noting the result in an Excel
file, to a teacher counting the number of students in her class and
transcribing it in the class register. What counts as data in these
interactions are the objects created in the process of description
and/or measurement of the world. These objects can be digital (the
Excel file) or physical (the class register) and form a footprint of a
specific interaction with the natural world. This
footprint--"trace" or "mark", in the
words of Ian Hacking (1992) and Hans-Jorg Rheinberger (2011),
respectively--constitutes a crucial reference point for analytic
study and for the extraction of new insights. This is the reason why
data forms a legitimate foundation to empirical knowledge: the
production of data is equivalent to "capturing" features
of the world that can be used for systematic study. According to the
representative approach, data are objects with fixed and unchangeable
content, whose meaning, in virtue of being representations of reality,
needs to be investigated and revealed step-by-step through adequate
inferential methods. The data documenting cell shape can be modelled
to test the relevance of shape to the elasticity, permeability and
resilience of cells, producing an evidence base to understand
cell-to-cell signalling and development. The data produced counting
students in class can be aggregated with similar data collected in
other schools, producing an evidence base to evaluate the density of
students in the area and their school attendance frequency.



This reflects the intuition that data, especially when they come in
the form of numerical measurements or images such as photographs,
somehow mirror the phenomena that they are created to document, thus
providing a snapshot of those phenomena that is amenable to study
under the controlled conditions of research. It also reflects the idea
of data as "raw" products of research, which are as close
as it gets to unmediated knowledge of reality. This makes sense of the
truth-value sometimes assigned to data as irrefutable sources of
evidence--the Popperian idea that if data are found to support a
given claim, then that claim is corroborated as true at least as long
as no other data are found to disprove it. Data in this view represent
an objective foundation for the acquisition of knowledge and this very
objectivity--the ability to derive knowledge from human
experience while transcending it--is what makes knowledge
empirical. This position is well-aligned with the idea that big data
is valuable to science because it facilitates the (broadly understood)
inductive accumulation of knowledge: gathering data collected via
reliable methods produces a mountain of facts ready to be analysed
and, the more facts are produced and connected with each other, the
more knowledge can be extracted.



Philosophers have long acknowledged that data do not speak for
themselves and different types of data require different tools for
analysis and preparation to be interpreted (Bogen 2009 [2013]).
According to the representative view, there are correct and incorrect
ways of interpreting data, which those responsible for data analysis
need to uncover. But what is a "correct" interpretation in
the realm of big data, where data are consistently treated as mobile
entities that can, at least in principle, be reused in countless
different ways and towards different objectives? Perhaps more than at
any other time in the history of science, the current mobilisation and
re-use of big data highlights the degree to which data
interpretation--and with it, whatever data is taken to
represent--may differ depending on the conceptual, material and
social conditions of inquiry. The analysis of how big data travels
across contexts shows that the expectations and abilities of those
involved determine not only the way data are interpreted, but also
what is regarded as "data" in the first place (Leonelli
& Tempini forthcoming). The representative view of data as objects
with fixed and contextually independent meaning is at odds with these
observations.



An alternative approach is to embrace these findings and abandon the
idea of data as fixed representations of reality altogether. Within
the *relational view*, data are objects that are treated as
potential or actual evidence for scientific claims in ways that can,
at least in principle, be scrutinised and accounted for (Leonelli
2016). The meaning
assigned to data depends on their provenance, their physical features
and what these features are taken to represent, and the motivations
and instruments used to visualise them and to defend specific
interpretations. The reliability of data thus depends on the
credibility and strictness of the processes used to produce and
analyse them. The presentation of data; the way they are identified,
selected, and included (or excluded) in databases; and the information
provided to users to re-contextualise them are fundamental to
producing knowledge and significantly influence its content. For
instance, changes in data format--as most obviously involved in
digitisation, data compression or archival procedures-- can have
a significant impact on where, when, and who uses the data as source
of knowledge.



This framework acknowledges that any object can be used as a datum, or
stop being used as such, depending on the circumstances--a
consideration familiar to big data analysts used to pick and mix data
coming from a vast variety of sources. The relational view also
explains how, depending on the research perspective interpreting it,
the same dataset may be used to represent different aspects of the
world ("phenomena" as famously characterised by James
Bogen and James Woodward, 1988). When considering the full cycle of
scientific inquiry from the viewpoint of data production and analysis,
it is at the stage of data *modelling* that a specific
representational value is attributed to data (Leonelli 2019b).



The relational view of data encourages attention to the history of
data, highlighting their continual evolution and sometimes radical
alteration, and the impact of this feature on the power of data to
confirm or refute hypotheses. It explains the critical importance of
documenting data management and transformation processes, especially
with big data that transit far and wide over digital channels and are
grouped and interpreted in different ways and formats. It also
explains the increasing recognition of the expertise of those who
produce, curate, and analyse data as indispensable to the effective
interpretation of big data within and beyond the sciences; and the
inextricable link between social and ethical concerns around the
potential impact of data sharing and scientific concerns around the
quality, validity, and security of data (boyd & Crawford 2012;
Tempini & Leonelli, 2018).



Depending on which view on data one takes, expectations around what
big data can do for science will vary dramatically. The
representational view accommodates the idea of big data as providing
the most comprehensive, reliable and generative knowledge base ever
witnessed in the history of science, by virtue of its sheer size and
heterogeneity. The relational view makes no such commitment, focusing
instead on what inferences are being drawn from such data at any given
point, how and why.


## 5. Big Data and Evidence



One thing that the representational and relational views agree on is
the key epistemic role of data as empirical evidence for knowledge
claims or interventions. While there is a large philosophical
literature on the nature of evidence (e.g., Achinstein 2001; Reiss
2015; Kelly 2016), however, the relation between data and evidence has
received less attention. This is arguably due to an implicit
acceptance, by many philosophers, of the representational view of
data. Within the representational view, the identification of what
counts as data is prior to the study of what those data can be
evidence for: in other words, data are "givens", as the
etymology of the word indicates, and inferential methods are
responsible for determining whether and how the data available to
investigators can be used as evidence, and for what. The focus of
philosophical attention is thus on formal methods to single out errors
and misleading interpretations, and the probabilistic and/or
explanatory relation between what is unproblematically taken to be a
body of evidence and a given hypothesis. Hence much of the expansive
philosophical work on evidence avoids the term "data"
altogether. Peter Achinstein's seminal work is a case in point:
it discusses observed facts and experimental results, and whether and
under which conditions scientists would have reasons to believe such
facts, but it makes no mention of data and related processing
practices (Achinstein 2001).



By contrast, within the relational view an object can only be
identified as datum when it is viewed as having value as evidence.
Evidence becomes a category of data identification, rather than a
category of data use as in the representational view (Canali 2019). Evidence is thus constitutive of the very notion of data
and cannot be disentangled from it. This involves accepting that the
conditions under which a given object can serve as evidence--and
thus be viewed as datum - may change; and that should this evidential
role stop altogether, the object would revert back into an ordinary,
non-datum item. For example, the photograph of a plant taken by a
tourist in a remote region may become relevant as evidence for an
inquiry into the morphology of plants from that particular locality;
yet most photographs of plants are never considered as evidence for an
inquiry into the features and functioning of the world, and of those
who are, many may subsequently be discarded as uninteresting or no
longer pertinent to the questions being asked.



This view accounts for the mobility and repurposing that characterises
big data use, and for the possibility that objects that were not
originally generated in order to serve as evidence may be subsequently
adopted as such. Consider Mayo and Spanos's "minimal
scientific principle for evidence", which they define as
follows: 



> 
> 
> Data **x**0 provide poor evidence for *H* if they
> result from a method or procedure that has little or no ability of
> finding flaws in
> *H*, even if *H* is false. (Mayo & Spanos 2009b) 
> 
> 
> 



This principle is compatible with the relational view of data since it
incorporates cases where the methods used to generate and process data
may not have been geared towards the testing of a hypothesis H: all it
asks is that such methods can be made relevant to the testing of H, at
the point in which data are used as evidence for H (I shall come back
to the role of hypotheses in the handling of evidence in the next
section).



The relational view also highlights the relevance of practices of data
formatting and manipulation to the treatment of data as evidence, thus
taking attention away from the characteristics of the data objects
alone and focusing instead on the agency attached to and enabled by
those characteristics. Nora Boyd has provided a way to conceptualise
data processing as an integral part of inferential processes, and thus
of how we should understand evidence. To this aim she introduced the
notion of "line of evidence", which she defines as: 



> 
> 
> a sequence of empirical results including the records of data
> collection and all subsequent products of data processing generated on
> the way to some final empirical constraint. (Boyd 2018:406) 
> 
> 
> 



She thus proposes a conception of evidence that embraces both data and
the way in which data are handled, and indeed emphasises the
importance of auxiliary information used when assessing data for
interpretation, which includes 



> 
> 
> the metadata regarding the provenance of the data records and the
> processing workflow that transforms them. (2018: 407) 
> 
> 
> 



As she concludes, 



> 
> 
> together, a line of evidence and its associated metadata compose what
> I am calling an "enriched line of evidence". The
> evidential corpus is then to be made up of many such enriched lines of
> evidence. (2018: 407)
> 
> 
> 



The relational view thus fosters a functional and contextualist
approach to evidence as the manner through which one or more objects
are used as warrant for particular knowledge items (which can be
propositional claims, but also actions such as specific decisions or
modes of conduct/ways of operating). This chimes with the contextual
view of evidence defended by Reiss (2015), John Norton's work on
the multiple, tangled lines of inferential reasoning underpinning
appeals to induction (2003), and Hasok Chang's emphasis on the
epistemic activities required to ground evidential claims (2012).
Building on these ideas and on Stephen Toulmin's seminal work on
research schemas (1958),
Alison Wylie has gone one step further in evaluating the inferential
scaffolding that researchers (and particularly archaeologists, who so
often are called to re-evaluate the same data as evidence for new
claims; Wylie 2017) need to make sense of their data, interpret them
in ways that are robust to potential challenges, and modify
interpretations in the face of new findings. This analysis enabled
Wylie to formulate a set of conditions for robust evidential
reasoning, which include epistemic security in the chain of evidence,
causal anchoring and causal independence of the data used as evidence,
as well as the explicit articulation of the grounds for calibration of
the instruments and methods involved (Chapman & Wylie 2016; Wylie
forthcoming). A similar conclusion is reached by Jessey Wright's
evaluation of the diverse data analysis techniques that
neuroscientists use to make sense of functional magnetic resonance
imaging of the brain (fMRI scans): 



> 
> 
> different data analysis techniques reveal different patterns in the
> data. Through the use of multiple data analysis techniques,
> researchers can produce results that are locally robust. (Wright 2017:
> 1179) 
> 
> 
> 



Wylie's and Wright's analyses exemplify how a relational
approach to data fosters a normative understanding of "good
evidence" which is anchored in situated judgement--the
arguably human prerogative to contextualise and assess the
significance of evidential claims. The advantages of this view of
evidence are eloquently expressed by Nancy Cartwright's critique
of both philosophical theories and policy approaches that do not
recognise the local and contextual nature of evidential reasoning. As
she notes, 



> 
> 
> we need a concept that can give guidance about what is relevant to
> consider in deciding on the probability of the hypothesis, not one
> that requires that we already know significant facts about the
> probability of the hypothesis on various pieces of evidence.
> (Cartwright 2013: 6) 
> 
> 
> 



Thus she argues for a notion of evidence that is not too restrictive,
takes account of the difficulties in combining and selecting evidence,
and allows for contextual judgement on what types of evidence are best
suited to the inquiry at hand (Cartwright 2013, 2019). Reiss's
proposal of a pragmatic theory of evidence similarly aims to 



> 
> 
> takes scientific practice [..] seriously, both in terms of its greater
> use of knowledge about the conditions under which science is practised
> and in terms of its goal to develop insights that are relevant to
> practising scientists. (Reiss 2015: 361)
> 
> 
> 



A better characterisation of the relation between data and evidence,
predicated on the study of how data are processed and aggregated, may
go a long way towards addressing these demands. As aptly argued by
James Woodward, the evidential relationship between data and claims is
not a "a purely formal, logical, or a priori matter"
(Woodward 2000: S172-173). This again sits uneasily with the expectation that
big data analysis may automate scientific discovery and make human
judgement redundant.


## 6. Big Data, Knowledge and Inquiry



Let us now return to the idea of data-driven inquiry, often suggested
as a counterpoint to hypothesis-driven science (e.g., Hey et al.
2009). Kevin Elliot and colleagues have offered a brief history of
hypothesis-driven inquiry (Elliott et al. 2016), emphasising how
scientific institutions (including funding programmes and publication
venues) have pushed researchers towards a Popperian conceptualisation
of inquiry as the formulation and testing of a strong hypothesis. Big
data analysis clearly points to a different and arguably Baconian
understanding of the role of hypothesis in science. Theoretical
expectations are no longer seen as driving the process of inquiry and
empirical input is recognised as primary in determining the direction
of research and the phenomena--and related
hypotheses--considered by researchers.



The emphasis on data as a central component of research poses a
significant challenge to one of the best-established philosophical
views on scientific knowledge. According to this view, which I shall
label the *theory-centric view* of science, scientific
knowledge consists of justified true beliefs about the world. These
beliefs are obtained through empirical methods aiming to test the
validity and reliability of statements that describe or explain
aspects of reality. Hence scientific knowledge is conceptualised as
inherently propositional: what counts as an output are claims
published in books and journals, which are also typically presented as
solutions to hypothesis-driven inquiry. This view acknowledges the
significance of methods, data, models, instruments and materials
within scientific investigations, but ultimately regards them as means
towards one end: the achievement of true claims about the world.
Reichenbach's seminal distinction between contexts of discovery
and justification exemplifies this position (Reichenbach 1938).
Theory-centrism recognises research components such as data and
related practical skills as essential to discovery, and more
specifically to the messy, irrational part of scientific work that
involves value judgements, trial-and-error, intuition and exploration
and within which the very phenomena to be investigated may not have
been stabilised. The justification of claims, by contrast, involves
the rational reconstruction of the research that has been performed,
so that it conforms to established norms of inferential reasoning.
Importantly, within the context of justification, only data that
support the claims of interest are explicitly reported and discussed:
everything else--including the vast majority of data produced in
the course of inquiry--is lost to the chaotic context of
 discovery.[2]



Much recent philosophy of science, and particularly modelling and
experimentation, has challenged theory-centrism by highlighting the
role of models, methods and modes of intervention as research outputs
rather than simple tools, and stressing the importance of expanding
philosophical understandings of scientific knowledge to include these
elements alongside propositional claims. The rise of big data offers
another opportunity to reframe understandings of scientific knowledge
as not necessarily centred on theories and to include
non-propositional components--thus, in Cartwright's
paraphrase of Gilbert Ryle's famous distinction, refocusing on
knowing-how over knowing-that (Cartwright 2019). One way to construe
data-centric methods is indeed to embrace a conception of knowledge as
ability, such as promoted by early pragmatists like John Dewey and
more recently reprised by Chang, who specifically highlighted it as
the broader category within which the understanding of
knowledge-as-information needs to be placed (Chang 2017).



Another way to interpret the rise of big data is as a vindication of
inductivism in the face of the barrage of philosophical criticism
levelled against theory-free reasoning over the centuries. For
instance, Jon Williamson (2004: 88) has argued that advances in
automation, combined with the emergence of big data, lend plausibility
to inductivist philosophy of science. Wolfgang Pietsch agrees with
this view and provided a sophisticated framework to understand just
what kind of inductive reasoning is instigated by big data and related
machine learning methods such as decision trees (Pietsch
2015). Following John Stuart Mill, he calls this approach
*variational induction* and presents it as common to both big
data approaches and exploratory experimentation, though the former can
handle a much larger number of variables (Pietsch 2015: 913). Pietsch
concludes that the problem of theory-ladenness in machine learning can
be addressed by determining under which theoretical assumptions
variational induction works (2015: 910ff).



Others are less inclined to see theory-ladenness as a problem that can
be mitigated by data-intensive methods, and rather see it as a
constitutive part of the process of empirical inquiry. Arching back to
the extensive literature on perspectivism and experimentation (Gooding
1990; Giere 2006; Radder 2006; Massimi 2012), Werner Callebaut has
forcefully argued that the most sophisticated and standardised
measurements embody a specific theoretical perspective, and this is no
less true of big data (Callebaut 2012). Elliott and colleagues
emphasise that conceptualising big data analysis as atheoretical risks
encouraging unsophisticated attitudes to empirical investigation as a




> 
> 
> "fishing expedition", having a high probability of leading
> to nonsense results or spurious correlations, being reliant on
> scientists who do not have adequate expertise in data analysis, and
> yielding data biased by the mode of collection. (Elliott et al. 2016:
> 880)
> 
> 
> 



To address related worries in genetic analysis, Ken Waters has
provided the useful characterisation of "theory-informed"
inquiry (Waters 2007),
which can be invoked to stress how theory informs the methods used to
extract meaningful patterns from big data, and yet does not
necessarily determine either the starting point or the outcomes of
data-intensive science. This does not resolve the question of what
role theory actually plays. Rob Kitchin (2014) has proposed to see big data
as linked to a new mode of hypothesis generation within a
hypothetical-deductive framework. Leonelli is more sceptical of
attempts to match big data approaches, which are many and diverse,
with a specific type of inferential logic. She rather focused on the
extent to which the theoretical apparatus at work within big data
analysis rests on conceptual decisions about how to order and classify
data--and proposed that such decisions can give rise to a
particular form of theorization, which she calls classificatory theory
(Leonelli 2016).



These disagreements point to big data as eliciting diverse
understandings of the nature of knowledge and inquiry, and the complex
iterations through which different inferential methods build on each
other. Again, in the words of Elliot and colleagues, 



> 
> 
> attempting to draw a sharp distinction between hypothesis-driven and
> data-intensive science is misleading; these modes of research are not
> in fact orthogonal and often intertwine in actual scientific practice.
> (Elliott et al. 2016: 881, see also O'Malley et al. 2009,
> Elliott 2012)
> 
> 
> 


## 7. Big Data Between Causation and Prediction



Another epistemological debate strongly linked to reflection on big
data concerns the specific kinds of knowledge emerging from
data-centric forms of inquiry, and particularly the relation between
predictive and causal knowledge. 



Big data science is widely seen as revolutionary in the scale and
power of predictions that it can support. Unsurprisingly perhaps, a
philosophically sophisticated defence of this position comes from the
philosophy of mathematics, where Marco Panza, Domenico Napoletani and
Daniele Struppa argued for big data science as occasioning a momentous
shift in the predictive knowledge that mathematical analysis can
yield, and thus its role within broader processes of knowledge
production. The whole point of big data analysis, they posit, is its
disregard for causal knowledge: 



> 
> 
> answers are found through a process of automatic fitting of the data
> to models that do not carry any structural understanding beyond the
> actual solution of the problem itself. (Napoletani, Panza, &
> Struppa 2014: 486) 
> 
> 
> 



This view differs from simplistic popular discourse on "the
death of theory" (Anderson 2008) and the "power of
correlations" (Mayer-Schoenberg and Cukier 2013) insofar as it
does not side-step the constraints associated with knowledge and
generalisations that can be extracted from big data analysis.
Napoletani, Panza and Struppa recognise that there are inescapable
tensions around the ability of mathematical reasoning to overdetermine
empirical input, to the point of providing a justification for any and
every possible interpretation of the data. In their words, 



> 
> 
> the problem arises of how we can gain meaningful understanding of
> historical phenomena, given the tremendous potential variability of
> their developmental processes. (Napoletani et al. 2014: 487) 
> 
> 
> 



Their solution is to clarify that understanding phenomena is not the
goal of predictive reasoning, which is rather a form of *agnostic
science*: "the possibility of forecasting and analysing
without a structured and general understanding" (Napoletani et al.
2011: 12). The
opacity of algorithmic rationality thus becomes its key virtue and the
reason for the extraordinary epistemic success of forecasting grounded
on big data. While "the phenomenon may forever re-main hidden to
our understanding"(ibid.: 5), the application of mathematical models and algorithms
to big data can still provide meaningful and reliable answers to
well-specified problems--similarly to what has been argued in the
case of false models (Wimsatt 2007). Examples include the use of
"forcing" methods such as regularisation or diffusion
geometry to facilitate the extraction of useful insights from messy
datasets. 



This view is at odds with accounts that posit scientific understanding
as a key aim of science (de Regt 2017), and the intuition that what
researchers are ultimately interested in is 



> 
> 
> whether the opaque data-model generated by machine-learning
> technologies count as explanations for the relationships found between
> input and output. (Boon 2020: 44) 
> 
> 
> 



Within the philosophy of biology, for example, it is well recognised
that big data facilitates effective extraction of patterns and trends,
and that being able to model and predict how an organism or ecosystem
may behave in the future is of great importance, particularly within
more applied fields such as biomedicine or conservation science. At
the same time, researchers are interested in understanding the reasons
for observed correlations, and typically use predictive patterns as
heuristics to explore, develop and verify causal claims about the
structure and functioning of entities and processes. Emanuele Ratti
(2015) has argued that big data mining within genome-wide association
studies often used in cancer genomics can actually underpin
mechanistic reasoning, for instance by supporting eliminative
inference to develop mechanistic hypotheses and by helping to explore
and evaluate generalisations used to analyse the data. In a similar
vein, Pietsch (2016)
proposed to use variational induction as a method to establish what
counts as causal relationships among big data patterns, by focusing on
which analytic strategies allow for reliable prediction and effective
manipulation of a phenomenon.



Through the study of data sourcing and processing in epidemiology,
Stefano Canali has instead highlighted the difficulties of deriving
mechanistic claims from big data analysis, particularly where data are
varied and embodying incompatible perspectives and methodological
approaches (Canali 2016, 2019). Relatedly, the semantic and logistical
challenges of organising big data give reason to doubt the reliability
of causal claims extracted from such data. In terms of logistics,
having a lot of data is not the same as having all of them, and
cultivating illusions of comprehensiveness is a risky and potentially
misleading strategy, particularly given the challenges encountered in
developing and applying curatorial standards for data other than the
high-throughput results of "omics" approaches (see also
the next section). The constant worry about the partiality and
reliability of data is reflected in the care put by database curators
in enabling database users to assess such properties; and in the
importance given by researchers themselves, particularly in the
biological and environmental sciences, to evaluating the quality of
data found on the internet (Leonelli 2014, Fleming et al. 2017). In
terms of semantics, we are back to the role of data classifications as
theoretical scaffolding for big data analysis that we discussed in the
previous section. Taxonomic efforts to order and visualise data inform
causal reasoning extracted from such data (Sterner
& Franz 2017), and can themselves constitute a bottom-up
method--grounded in comparative reasoning--for assigning
meaning to data models, particularly in situation where a full-blown
theory or explanation for the phenomenon under investigation is not
available (Sterner 2014).



It is no coincidence that much philosophical work on the relation
between causal and predictive knowledge extracted from big data comes
from the philosophy of the life sciences, where the absence of
axiomatized theories has elicited sophisticated views on the diversity
of forms and functions of theory within inferential reasoning.
Moreover, biological data are heterogeneous both in their content and
in their format; are curated and re-purposed to address the needs of
highly disparate and fragmented epistemic communities; and present
curators with specific challenges to do with tracking complex, diverse
and evolving organismal structures and behaviours, whose relation to
an ever-changing environment is hard to pinpoint with any stability
(e.g., Shavit & Griesemer 2009). Hence in this domain, some of the
core methods and epistemic concerns of experimental
research--including exploratory experimentation, sampling and the
search for causal mechanisms--remain crucial parts of
data-centric inquiry.


## 8. The Fact/Value Distinction



At the start of this entry I listed "value" as a major
characteristic of big data and pointed to the crucial role of valuing
procedures in identifying, processing, modelling and interpreting data
as evidence. Identifying and negotiating different forms of data value
is an unavoidable part of big data analysis, since these valuation
practices determine which data is made available to whom, under which
conditions and for which purposes. What researchers choose to consider
as reliable data (and data sources) is closely intertwined not only
with their research goals and interpretive methods, but also with
their approach to data production, packaging, storage and sharing.
Thus, researchers need to consider what value their data may have for
future research by themselves and others, and how to enhance that
value--such as through decisions around which data to make
public, how, when and in which format; or, whenever dealing with data
already in the public domain (such as personal data on social media),
decisions around whether the data should be shared and used at all,
and how.



No matter how one conceptualises value practices, it is clear that
their key role in data management and analysis prevents facile
distinctions between values and "facts" (understood as
propositional claims for which data provide evidential warrant). For
example, consider a researcher who values both
*openness*--and related practices of widespread data
sharing--and scientific *rigour*--which requires a
strict monitoring of the credibility and validity of conditions under
which data are interpreted. The scale and manner of big data
mobilisation and analysis create tensions between these two values.
While the commitment to openness may prompt interest in data sharing,
the commitment to rigour may hamper it, since once data are freely
circulated online it becomes very difficult to retain control over how
they are interpreted, by whom and with which knowledge, skills and
tools. How a researcher responds to this conflict affects which data
are made available for big data analysis, and under which conditions.
Similarly, the extent to which diverse datasets may be triangulated
and compared depends on the intellectual property regimes under which
the data--and related analytic tools--have been produced.
Privately owned data are often unavailable to publicly funded
researchers; and many algorithms, cloud systems and computing
facilities used in big data analytics are only accessible to those
with enough resources to buy relevant access and training. Whatever
claims result from big data analysis are, therefore, strongly
dependent on social, financial and cultural constraints that condition
the data pool and its analysis.



This prominent role of values in shaping data-related epistemic
practices is not surprising given existing philosophical critiques of
the fact/value distinction (e.g., Douglas 2009), and the existing
literature on values in science--such as Helen Longino's
seminal distinction between constitutive and contextual values, as
presented in her 1990 book *Science as Social
Knowledge*--may well apply in this case too. Similarly, it is
well-established that the technological and social conditions of
research strongly condition its design and outcomes. What is
particularly worrying in the case of big data is the temptation,
prompted by hyped expectations around the power of data analytics, to
hide or side-line the valuing choices that underpin the methods,
infrastructures and algorithms used for big data extraction.



Consider the use of high-throughput data production tools, which
enable researchers to easily generate a large volume of data in
formats already geared to computational analysis. Just as in the case
of other technologies, researchers have a strong incentive to adopt
such tools for data generation; and may do so even in cases where such
tools are not good or even appropriate means to pursue the
investigation. Ulrich Krohs uses the term *convenience
experimentation* to refer to experimental designs that are adopted
not because they are the most appropriate ways of pursuing a given
investigation, but because they are easily and widely available and
usable, and thus "convenient" means for researchers to
pursue their goals (Krohs 2012).



Appeals to convenience can extend to other aspects of data-intensive
analysis. Not all data are equally easy to digitally collect,
disseminate and link through existing algorithms, which makes some
data types and formats more convenient than others for computational
analysis. For example, research databases often display the outputs of
well-resourced labs within research traditions which deal with
"tractable" data formats (such as "omics").
And indeed, the existing distribution of resources, infrastructure and
skills determines *high levels of inequality* in the
production, dissemination and use of big data for research. Big
players with large financial and technical resources are leading the
development and uptake of data analytics tools, leaving much publicly
funded research around the world at the receiving end of innovation in
this area. Contrary to popular depictions of the data revolution as
harbinger of transparency, democracy and social equality, the
*digital divide* between those who can access and use data
technologies, and those who cannot, continues to widen. A result of
such divides is the scarcity of data relating to certain subgroups and
geographical locations, which again limits the comprehensiveness of
available data resources.



In the vast ecosystem of big data infrastructures, it is difficult to
keep track of such distortions and assess their significance for data
interpretation, especially in situations where heterogeneous data
sources structured through appeal to different values are mashed
together. Thus, the systematic aggregation of convenient datasets and
analytic tools over others often results in a big data pool where the
relevant sources and forms of bias are impossible to locate and
account for (Pasquale 2015; O'Neill 2016; Zuboff 2017; Leonelli
2019a). In such a landscape, arguments for a separation between fact
and value--and even a clear distinction between the role of
epistemic and non-epistemic values in knowledge
production--become very difficult to maintain without
discrediting the whole edifice of big data science. Given the extent
to which this approach has penetrated research in all domains, it is
arguably impossible, however, to critique the value-laden structure of
big data science without calling into question the legitimacy of
science itself. A more constructive approach is to embrace the extent
to which big data science is anchored in human choices, interests and
values, and ascertain how this affects philosophical views on
knowledge, truth and method.


## 9. Big Data Risks and the Ethics of Data Science



In closing, it is important to consider at least some of the risks and
related ethical questions raised by research with big data. As already
mentioned in the previous section, reliance on big data collected by
powerful institutions or corporations risks raises significant social
concerns. Contrary to the view that sees big and open data as
harbingers of democratic social participation in research, the way
that scientific research is governed and financed is not challenged by
big data. Rather, the increasing commodification and large value
attributed to certain kinds of data (e.g., personal data) is
associated to an increase in inequality of power and visibility
between different nations, segments of the population and scientific
communities (O'Neill 2016; Zuboff 2017; D'Ignazio and
Klein 2020). The digital gap between those who not only can access
data, but can also use it, is widening, leading from a state of
digital divide to a condition of "data divide"
(Bezuidenout et al. 2017).



Moreover, the privatisation of data has serious implications for the
world of research and the knowledge it produces. Firstly, it affects
which data are disseminated, and with which expectations. Corporations
usually only release data that they regard as having lesser commercial
value and that they need public sector assistance to interpret. This
introduces another distortion on the sources and types of data that
are accessible online while more expensive and complex data are kept
secret. Even many of the ways in which citizens -researchers included
- are encouraged to interact with databases and data interpretation
sites tend to encourage participation that generates further
commercial value. Sociologists have recently described this type of
social participation as a form of exploitation (Prainsack & Buyx
2017; Srnicek 2017). In turn, these ways of
exploiting data strengthen their economic value over their scientific
value. When it comes to the commerce of personal data between
companies working in analysis, the value of the data as commercial
products -which includes the evaluation of the speed and efficiency
with which access to certain data can help develop new products -
often has priority over scientific issues such as for example,
representativity and reliability of the data and the ways they were
analysed. This can result in decisions that pose a problem
scientifically or that simply are not interested in investigating the
consequences of the assumptions made and the processes used. This lack
of interest easily translates into ignorance of discrimination,
inequality and potential errors in the data considered. This type of
ignorance is highly strategic and economically productive since it
enables the use of data without concerns over social and scientific
implications. In this scenario the evaluation on the quality of data
shrinks to an evaluation of their usefulness towards short-term
analyses or forecasting required by the client. There are no
incentives in this system to encourage evaluation of the long-term
implications of data analysis. The risk here is that the commerce of
data is accompanied by an increasing divergence between data and their
context. The interest in the history of the transit of data, the
plurality of their emotional or scientific value and the re-evaluation
of their origins tend to disappear over time, to be substituted by the
increasing hold of the financial value of data.



The multiplicity of data sources and tools for aggregation also
creates risks. The complexity of the data landscape is making it
harder to identify which parts of the infrastructure require updating
or have been put in doubt by new scientific developments. The
situation worsens when considering the number of databases that
populate every area of scientific research, each containing
assumptions that influence the circulation and interoperability of
data and that often are not updated in a reliable and regular way.
Just to provide an idea of the numbers involved, the prestigious
scientific publication *Nucleic Acids Research* publishes a
special issue on new databases that are relevant to molecular biology
every year and included: 56 new infrastructures in 2015, 62 in 2016,
54 in 2017 and 82 in 2018. These are just a small proportion of the
hundreds of databases that are developed each year in the life
sciences sector alone. The fact that these databases rely on short
term funding means that a growing percentage of resources remain
available to consult online although they are long dead. This is a
condition that is not always visible to users of the database who
trust them without checking whether they are actively maintained or
not. At what point do these infrastructures become obsolete? What are
the risks involved in weaving an ever more extensive tapestry of
infrastructures that depend on each other, given the disparity in the
ways they are managed and the challenges in identifying and comparing
their prerequisite conditions, the theories and scaffolding used to
build them? One of these risks is rampant conservativism: the
insistence on recycling old data whose features and management
elements become increasingly murky as time goes by, instead of
encouraging the production of new data with features that specifically
respond to the requirements and the circumstances of their users. In
disciplines such as biology and medicine that study living beings and
therefore are by definition continually evolving and developing, such
trust in old data is particularly alarming. It is not the case, for
example, that data collected on fungi ten, twenty or even a hundred
years ago is reliable to explain the behaviour of the same species of
fungi now or in the future (Leonelli 2018).



Researchers of what Luciano Floridi calls the
*infosphere*--the way in which the introduction of digital
technologies is changing the world - are becoming aware of the
destructive potential of big data and the urgent need to focus efforts
for management and use of data in active and thoughtful ways towards
the improvement of the human condition. In Floridi's own words:




> 
> 
> ICT yields great opportunity which, however, entails the enormous
> intellectual responsibility of understanding this technology to use it
> in the most appropriate way. (Floridi 2014: vii; see also
> British Academy & Royal Society 2017) 
> 
> 
> 



In light of these findings, it is essential that ethical and social
issues are seen as a core part of the technical and scientific
requirements associated with data management and analysis. The ethical
management of data is not obtained exclusively by regulating the
commerce of research and management of personal data nor with the
introduction of monitoring of research financing, even though these
are important strategies. To guarantee that big data are used in the
most scientifically and socially forward-thinking way it is necessary
to transcend the concept of ethics as something external and alien to
research. An analysis of the ethical implications of data science
should become a basic component of the background and activity of
those who take care of data and the methods used to view and analyse
it. Ethical evaluations and choices are hidden
in every aspect of data management, including those choices that may
seem purely technical.


## 10. Conclusion: Big Data and Good Science



This entry stressed how the emerging emphasis on big data signals the
rise of a data-centric approach to research, in which efforts to
mobilise, integrate, disseminate and visualise data are viewed as
central contributions to discovery. The emergence of data-centrism
highlights the challenges involved in gathering, classifying and
interpreting data, and the concepts, technologies and institutions
that surround these processes. Tools such as high-throughput
measurement instruments and apps for smartphones are fast generating
large volumes of data in digital formats. In principle, these data are
immediately available for dissemination through internet platforms,
which can make them accessible to anybody with a broadband connection
in a matter of seconds. In practice, however, access to data is
fraught with conceptual, technical, legal and ethical implications;
and even when access can be granted, it does not guarantee that the
data can be fruitfully used to spur further research. Furthermore, the
mathematical and computational tools developed to analyse big data are
often opaque in their functioning and assumptions, leading to results
whose scientific meaning and credibility may be difficult to assess.
This increases the worry that big data science may be grounded upon,
and ultimately supporting, the process of making human ingenuity
hostage to an alien, artificial and ultimately unintelligible
intelligence.



Perhaps the most confronting aspect of big data science as discussed
in this entry is the extent to which it deviates from understandings
of rationality grounded on individual agency and cognitive abilities
(on which much of contemporary philosophy of science is predicated).
The power of any one dataset to yield knowledge lies in the extent to
which it can be linked with others: this is what lends high epistemic
value to digital objects such as GPS locations or sequencing data, and
what makes extensive data aggregation from a variety of sources into a
highly effective surveillance tool. Data production and dissemination
channels such as social media, governmental databases and research
repositories operate in a globalised, interlinked and distributed
network, whose functioning requires a wide variety of skills and
expertise. The distributed nature of decision-making involved in
developing big data infrastructures and analytics makes it impossible
for any one individual to retain oversight over the quality,
scientific significance and potential social impact of the knowledge
being produced.



Big data analysis may therefore constitute the ultimate instance of a
distributed cognitive system. Where does this leave accountability
questions? Many individuals, groups and institutions end up sharing
responsibility for the conceptual interpretation and social outcomes
of specific data uses. A key challenge for big data governance is to
find mechanisms for allocating responsibilities across this complex
network, so that erroneous and unwarranted decisions--as well as
outright fraudulent, unethical, abusive, discriminatory or misguided
actions--can be singled out, corrected and appropriately
sanctioned. Thinking about the complex history, processing and use of
data can encourage philosophers to avoid ahistorical, uncontextualized
approaches to questions of evidence, and instead consider the methods,
skills, technologies and practices involved in handling data--and
particularly big data--as crucial to understanding empirical
knowledge-making.