Volume 11, Issue 2, September 2014

“Theirs’ not to make reply, Theirs’ not to reason why” – a workshop report on Big Data, forensic reasoning and the trial

Burkhard Schafer*

Cite as: B Schafer, “Theirs’ not to make reply, Theirs’ not to reason why” – a workshop report on Big Data, forensic reasoning and the trial., (2014) 11:2 SCRIPTed 145 http://script-ed.org/?p=1537

Download PDF
DOI: 10.2966/scrip.110214.145

©  Burkhard Schafer 2014. This work is licensed under a Creative
Commons Attribution-NonCommercial-ShareAlike 4.0 International
. Please click on the link to read the terms and

Over the last few years, “Big Data”, has emerged as a major topic in
the discussion on the future of the internet and an internet driven
economy. As Bollier and Firestone in “The Promise and Perils of Big
Data” put it, “[…] a
radically new kind of “knowledge infrastructure” is materializing. A
new era of Big Data is emerging, and the implications for business,
government, democracy and culture are enormous.” 2 By analysing more efficiently the
ever increasing amounts of data that companies hold about their
customers, products and processes, companies understand their own
business better and better. They are able to quantify more and more of
the crucial parameters of the business and thus, become better and
better at predicting and managing its future.3 Or as Eric Siegel writes in the
introduction to his influential Predictive analytics:
the power to predict who will click, buy, lie, or die
“You have been predicted – by companies, governments, law enforcement,
hospitals, and universities. Their computers say, “I knew you were
going to do that!” These institutions are seizing upon the power to
predict whether you′re going to click, buy, lie, or die. Why? For good
reason: predicting human behavior combats financial risk, fortifies
healthcare, conquers spam, toughens crime fighting, and boosts
Predicting how the market will react to a new music video, which
customers to target with the latest advert, if a piece of news will
result in a run on the bank or if a pattern of changes in Facebook
statuses is indicating an emerging flu epidemic are all examples of the
predictive power of Big Data analytics. Harnessing this is of course
also of potential interest to law and law enforcement – the Minority
Report may have edged just a little bit closer to reality, as Big Data
may enable police to predict unrest or civil disorder from mining
Twitter discussion, or cybersecurity experts to identify an upcoming
denial of service attack through analysing internet traffic patterns.
However, the focus on predicting future behaviour has meant that the
impact of Big Data on forensic reasoning and the trial has been largely
neglected. Fact finding in the context of a trial is typically
concerned with one specific individual event that took place in the
past – did the accused commit the crime he is charged with, did the
defendant cause the harm for which damages are sought? This focus on
reconstructing a unique past event aligns legal reasoning about facts
more closely with history, archaeology or geology than the laboratory
sciences and their aim to develop reliable predictions of the future
through the discovery of universally applicable patterns and
And yet, it cannot be doubted that trial and legal process have been
profoundly influenced by modern science. The forensic science process
that began in the 17th century and culminated
in the emergence and proliferation of dedicated forensic disciplines in
the 20th century revolutionised the way in
which facts are established in a legal setting. This difference in
basic epistemic assumptions and aims between legal trial and scientific
discovery caused lasting tensions, which the law of evidence tries to
mitigate and manage. Imprinting its own normative logic upon scientific
practice as a social phenomenon, the law of evidence tries to determine
the nature of scientific expertise. Decisions such as the Daubert decision in the US,6 or legislative initiatives such as the
recently proposed reform of the law on scientific evidence in
England7 try to guide lawyers in distinguishing
reliable from unreliable science, trustworthy from untrustworthy

Do these rules, guidelines and heuristics need
revisiting as a result of the Big Data revolution? It seems that a strong
prima facie case can be made that just as modern
science has both changed and challenged the logic of the trial, so can and
will Big Data. Potential or actual examples of Big Data analytics in
forensic contexts are already emerging. Can for instance forensic
linguistics use the abundance of samples of written English on the Internet
to determine the frequency with which an unusual slang expression is used
or a spelling mistake made, for a stylometric identification of the author
of a blackmail note? Can data on the pollution of a river, collected by
“citizen scientists” on their smartphones and uploaded on the internet be
used in prosecutions for environmental crimes? Can courts in their
“gatekeeper function” use Big Data from social networking sites and online
publishers to determine more accurately if a scientific idea or method is
“generally accepted” by the scientific community, for instance by the
pattern of retweets that indicate that a publication announcement is well
received by the peers of the author?

The last two examples indicate an important change that Big Data
science might bring about for the legal process. Traditionally,
reliability of scientific expertise was (in parts) achieved by a system
of quality control and accreditation. DNA laboratories, just like
government owned national DNA databases, are subject to more or less
stringent regulation that can give us a degree of confidence in the
quality of the underlying data and the processes by which it is
collected, curated and interpreted. By contrast, “variability”,
including variability in quality, is one of the hallmarks of Big Data.
The hope is that the sheer volume means that statistically, low quality
information will not result in wrong predictions, but be “filtered out”
through the statistical methods that are employed. What do the lack of
centrally controlled data quality and the heterogeneous and
unsystematic nature of Big Data mean for the administration of justice?
To accept expert witness statements on the basis of data that is
a priori known to be of low quality in parts
will require a major adjustment in the way in which judges have
traditionally exercised their gatekeeping function. Indeed, recent
decisions such as R v. T8 point if anything towards a
greater insistence by the courts on high levels of transparency and
demonstrable data quality to make probabilistic judgements by experts
permissible than had been in the past. If this trend continues, then it
could create barriers for the use of Big Data analytics in forensic
settings, leaving for the moment as an open question if this would
result in the welcomed exclusion of unreliable methods or the
unnecessary rejection of trustworthy ones.

A final question in relation to the use of Big Data in
criminal proceedings highlights another set of challenges and dangers,
namely: will the availability of larger and larger amounts of health care
data prevent the next Harold Shipman or cause the next miscarriage of
justice, as happened in the case of Lucia de Berk? Her case in particular
brings several of the issues surrounding a forensic use of Big Data into
sharp relief. Data from the Dutch health care system was used to
“establish” statistically that the chance of a nurse being present at the
scene of the unexplained deaths that she was charged with was one in 342
million. However, this statistical analysis was in several respects
seriously flawed. In a forensic setting, in particular in adversarial
systems of adjudication, we rely on defence counsels to “make reply” when
the prosecution introduces expert evidence and challenge it vigorously.
This however requires a solid understanding of the underlying scientific
and mathematical principles, together with a high degree of transparency of
the analytical methods that were used to derive the result. However, the
tools that are used for Big Data analytics are often proprietary and
protected by trade secrets, making independent scrutiny difficult. Even
where such an independent analysis is possible, the complexity of the
statistical analysis will regular go beyond the capabilities even of
comparatively well-trained judges or counsel for the a parties.

The case of Lucia de Berk also illustrates that potentially an even
deeper sea-change will be heralded by the use of Big Data in forensic
settings. The pattern in the data was so obvious and the correlation
between her working shifts with the unexplained deaths so strong, that
for the purposes of prosecution it was not necessary to build a
conventional story that led from a compelling motive together with
proving that she had the means at her disposal and the opportunity to
use them. One advantage of such conventional narratives” that explain
the why and how of a suspect’s actions through the everyday ontology of
causal relations was that they led to testable predictions. Assuming
the prosecuting narrative is true, we should expect to find additional
evidence, which should be absent if the defence narrative, is correct.
This approach underpins the concept of falsification in science just as
much as the practice of critical scrutiny through cross-examination and
with it the adversarial legal process.9 In the past, both legal and scientific
thinking converged in their emphasis on causal accounts of this type –
accounts that allow the finder of facts “to reason why” (and indeed
how). Big Data by contrast may leave us with a Humean world where
correlations are all there is.
For the practice of science, it has been claimed that the thinking in
causal, explanatory categories will be swept aside by the Big Data
revolution, resulting in a new, data driven practice of scientific
research. Some proponents of Big Data are going as far as suggesting
that it heralds the “end of theory” altogether10: Intelligent search algorithms that mine
huge amounts of data for patterns will replace the “academic hunches”
that lead to the formulation of tentative causal hypothesis on the
basis of limited data. Mayer-Schönberger and Cukier put it like this:
“Since Aristotle, we have fought to understand the causes behind
everything. But this ideology is fading. In the age of Big Data, we can
crunch an incomprehensible amount of information, providing us with
invaluable insights about the what rather than the why.”11
The Aristotelian thinking in terms of causal relations is however
deeply ingrained in the practice of reasoning about facts in law, in
terms of both physical causes (“what caused his death”) and mental
states (“why did she kill him”).12 How will the courts react to expertise
that denies them in principle answers to this type of question? Who in
this new world is the expert, who “owns” the numbers? Permitting
experts to quantify the strength of the evidence is a relatively recent
phenomenon, and itself the result of a long and often acrimonious
struggle between scientific experts and lawyers over control in the
courtroom. In one traditional model of legal fact finding, experts
provide the bare facts, it is the role of judge or jury to weigh the
evidence and assess its credibility. Expressing evidence in
probabilistic terms, central for many modern forms of forensic evidence
such as DNA, was often seen as an intrusion into the territory of the
finder of facts. It was only with the growing importance of DNA
evidence that statistical assessments of evidential weight by the
expert witness became acceptable. A compromise of sorts was reached
that laid out clear preconditions under which a causal account of the
evidence could be couched in probabilistic terms. However, the expert
always remained the person trained in the natural sciences e.g. biology
or chemistry, not the statisticians. Big Data calls this historical
accommodation and its underlying epistemology into question just as
much as it challenges the role of the traditional forensic scientists
as expert witness. In the world of Big Data, if some of its more
aggressive proponents are to be believed, it would have to be the data
analyst as a generalist in all forms of data analysis, independent of
domain, and not the forensic biologist, chemist or anthropologist with
their domain specific knowledge, who would take centre stage in the

So far there has been little discussion in the forensic
science and evidence law communities on these opportunities and challenges.
If some of the claims of Big Data evangelists are to be believed, then the
“Big Data paradigm” will bring considerable disruption to the practice of
forensic statistics, forensic science and legal reasoning, and with that
the administration of justice. But how serious and credible are these
challenges? How prepared is the legal system? Is there a need for new
forms of training for lawyers or juries, are there new ways needed to
communicate data driven expert evidence in the courtroom? Are there needs
for reform in the law of evidence, the regulation of scientific expertise
in the courtroom and the way in which the complementary roles and duties
are assigned to judges, party lawyers, jurors and witnesses?

To address this gap and to begin a dialogue between
lawyers, statisticians, scientists and educators on this topic, the SCRIPT
Centre for IT and IP Law organised a round table workshop jointly with the
Bell Centre for Forensic Statistics and Legal Reasoning on the
5th of September in Edinburgh.

Topics addressed included a discussion of the current practice of
statistical and probabilistic analysis in court, so to speak the legacy
that “small data” has created for the legal system and on which any
future developments will have to build. Colin Aitken from the
University of Edinburgh, representing the forensic statistics
community, introduced an ambitious project of the Royal Statistical
Society to develop a multi-volume Practitioner Guide that aims to give
an overview of all the relevant statistical knowledge that the
stakeholders in the criminal justice system need for assessing the
probative value of evidence. This guidance for judges, lawyers,
forensic scientists and expert witnesses will give a comprehensive and
standard setting account of the way in which probabilistic methods for
data analysis should be used in courts.13 The ensuing discussion tried to gauge if
this project needs to be expanded to cover Big Data analytics, or if
those aspects of Big Data that have validity are already adequately
covered by it. This followed a line of reasoning indicated by Big Data
sceptics such as danah boyd and Kate Crawford who warn against the
danger of side-lining more appropriate analytical tools as a result of
the marketing hype surrounding Big Data.14

Edinburgh’s Burkhard Schafer tried to place the
forensic potential of Big Data into the wider context of a sometimes
paradoxical search for certainty in the legal fact finding process.
Traditional, pre-scientific methods of fact finding such as confession and
trial by ordeal held the (deceptive) promise of absolute certainty by
relying on epistemically privileged observers: the accused himself, and an
omniscient and interventionist God are the only possible candidates for an
account of past events that does not involve inferences under uncertainty.
As the belief in the latter waned, and the problem of false confession,
especially when extracted under torture, became too obvious to be ignored,
modern science offered a radically different alternative. The very
possibility of certainty was abandoned under the onslaught of radical,
Humean scepticism, but as a replacement emerged the possibility to give a
precise expression to the degree of our ignorance. The scientific
revolution, and ultimately the revolution of forensic science in court,
thus not only increased our knowledge, it also increased our knowledge
about its limitations. At the moment of radical and potentially destructive
scepticism, an alternative thus emerged through a major historical
compromise: a belief in a clockwork world that follows strict laws
underpins our trust in its intelligibility and our ability to reason
reliably about it, even if we cannot have certain knowledge of these laws.
But the emergence of probability theory, often intimately linked
historically to questions of legal reasoning, created a new type of
knowledge, precise and quantifiable knowledge of the limits of what we can
know. This in turn allowed the formulation of central legal concepts such
as “proof beyond reasonable doubt” or Blackstone’s ratio. If the claims
about a radical change in the nature of science necessitated by Big Data
come to fruition, this historical compromise is in peril and a return to
the radical scepticism of Hume a possibility, with as yet unclear
consequences of our understanding of legal reasoning about facts.

Marco Gomes (IBM) represented the industry perspective,
with a fascinating insight on the role of Big Data analytics in forensic
science and fraud detection. While his focus was on the more common use of
Big Data to predict criminal behaviour and help the prevention of crime, it
also gave an account of the advances that have been made in the field of
data analytics. For the lawyers in particular, his talk opened up a
discussion on issues of privacy and data protection. At present,
exclusionary rules can prevent the use of evidence that was unlawfully
obtained. To make this determination though, the process of gathering
evidence has to be fully explicit and transparent. Obvious issue arise if
the complexity of the data collection and analysis process, another
defining feature of Big Data, make this type of legal scrutiny problematic
or impossible. If only one or two pieces of data in a very large data set
are of legally problematic, does this “contaminate” the entire analysis and
make it legally inadmissible?

Christopher Laing from Northumbria University talked about digital
forensics and the role of Big Data to guide the investigative process.
This involves prioritising the right devices (“triage”) and case
auditing requirements. Digital and computer forensics is the forensic
discipline most obviously affected by Big Data. Better analytical tools
and methods are not just an opportunity in this context; they are a
necessity, if we do not want a backlog of cases that could bring the
justice system to a standstill. His talk focussed on the potential of
Big Data to develop more rational and transparent methods on device
triage (what devices analyse first, where should our priorities lie
given constraints on resources) and case auditing. The discussion took
up the vision of the investigative
process that this approach entails. “Actuarial justice” and “actuarial
policing”, terms coined by Malcolm Feeley and Jonathan Simon to
describe a justice system based on calculation of risks using the
statistical methods of insurance companies,
15 are part of the reality of policing the
risk society Its dangers for the legitimacy of police work and resource
allocation have been widely discussed. Big Data could add a new
dimension to this debate, by reducing the strain on some resources
while potentially creating new problems elsewhere.
Finally, Rónán Kennedy from the National University of Ireland, Galway
talked about the possible role of Big Data and environmental
prosecutions. Environmental regulation presents a particularly
appropriate context for the forensic use of ‘Big Data’, as it is so
closely tied to developments in both science and technology. The
challenges of properly managing the quality of the environment are
complex and difficult, and rely more often than not on complex
computational models that are in turn driven by Big Data.16 Environmental law and science have long
been linked in a way that is distinctive, something which can be traced
through the development of classification and statistical analysis in
the 18th and 19th
centuries and into the modern focus on standard setting. In the
courts, the two have an uneasy relationship but science is often key to
determining legal liability. Rónán’s paper explored the resulting
questions, highlighting how regulators are using Big Data in practice,
the extent to which they are opening their systems to input from
citizen science and allowing NGOs and the general public to have access
to their datasets.
The workshop, attended by practicing lawyers, computer scientists,
statisticians, medical researchers, legal academics and forensic
practitioners was a first step to developing a shared vocabulary to
discuss the likely impact of Big Data on the trial process. Its aim was
also to contribute to “foresighting”, and anticipating as far as
possible the necessary changes, if any, that the legal system may have
to contemplate as Big Data enters the scientific mainstream. A core
function of the trial is not just a reliable determination of facts, it
also has a symbolic and legitimising role. It is not enough that
justice is done; it has to be seen to be done. This requires a degree
of transparency and accountability that is potentially inimical to the
underlying logic of Big Data analytics, especially when based on
proprietary software tools that are intended for competitive markets
(of which at least in England, the forensic service market is an
example). To discharge its legitimising function, the legal system
assigns complementary yet also antagonistic roles to the judge, jury,
witness and legal representatives. For juries and defence solicitors
alike, the right “to reply, and to reason why” is central. In a Big
Data environment, this right may need particular protection. This
includes new and better forms of communicating the results of Big Data
analytics to laypeople, e.g. through visualisation tools. It includes
the need for potentially new forms of training for lawyers. It may
require legal intervention e.g. in the regulation of “forensic data
analysts” as a discipline. The adversarial process requires a degree of
openness about the underlying assumptions, methods and techniques of
forensic practitioners which may not be best served through
traditional forms of scientific publication, and may be positively
hindered through intellectual property and trade secret restrictions on
access to data and software specifications. Initiatives such as the
“recomputation initiative” that aims to utilise the Internet for new
forms of dissemination of scientific knowledge could be an aspect of
the solution.17 Its aim is to make available not just the
raw research data, but all the software tools and documentation
necessary to replicate the results claimed in the academic papers that
they accompany, Where currently, legal approaches such as the Daubert
standard rely on traditional peer review, recomputation is considerably
closer to the adversarial ethos of the trial and the type of open
scrutiny that it demands.

Both the SCRIPT Centre for IT and IP Law, and the Bell
Centre for Forensic Statistics and Legal Reasoning which organised this
workshop will continue to provide forum for this ongoing discussion,
continuing the series of talks and events on this topic in February with a
workshop that will focus on fire investigation as domain.

1 Burkhard Schafer is Professor for Computational
Legal Theory at the University of Edinburgh and Director of the SCRIPT
Centre for IT and IP law. With Colin Aitken, he is also a co-founder and
co-director of the Bell Centre for Forensic Statistics and legal Reasoning.
The workshop was jointly organised by both centres, with financial
contributions from the School of Law; the School of Mathematics and
Statistics; and a personal donor gratefully acknowledged.

2 Bollier, D. and Charles M. F. The promise and
peril of Big Data. Washington, DC, USA: Aspen Institute, Communications and
Society Program, 2010 p.1

3 McAfee, A., Brynjolfsson, E., Davenport, T. H.,
Patil, D. J., & Barton, D. (2012). Big Data. The management revolution.
Harvard Bus Rev, 90(10), 61-67.

4 Siegel, E. (2013). Predictive analytics: the power
to predict who will click, buy, lie, or die. John Wiley & Sons.

5 Farber, D. A. (1997). Adjudication of Things Past:
Reflections on History as Evidence. Hastings LJ, 49, 1009.

6 Daubert v. Merrell Dow Pharmaceuticals, 509 U.S.
579 (1993)


8 R.vT [2010] EWCA Crim2439;[2011] 1 Cr. App. R.

9 see e.g.Schafer, B., & Keppens, J. (2005). ”
And then there was none”-Indirect proof and hypothetical reasoning in Law.
Archiv fuer Rechts-und Sozialphilosophie, 177-187.

10 Anderson, C. (2008). The End of Theory: The Data
Deluge Makes the Scientific Method Obsolete. Wired Magazine, (Science:

11 Mayer-Schönberger, V., & Cukier, K. (2013).
Big Data: A revolution that will transform how we live, work, and think.
Houghton Mifflin Harcourt.

12 See e.g. Bex, F., Bench-Capon, T., & Atkinson,
K. (2009). Did he jump or was he pushed? Artificial Intelligence and Law,
17(2), 79-99; Walton, D., & Schafer, B. (2006). Arthur, George and the
mystery of the missing motive: towards a theory of evidentiary reasoning
about motives. International Commentary on Evidence, 4(2).

13 Vol 2 on DNA evidence is available here :

14 See e.g. boyd, d. and Crawford K. (2012).
“Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon.” Information, Communication, &
Society 15:5, p. 662-679.

15 Feeley, M., & Simon, J. (1994). Actuarial
justice: Power/knowledge in contemporary criminal justice. The Futures of
Criminology. London: Sage.

16 See e.g. Aronova, E., Baker, K. S., & Oreskes,
N. (2010). Big science and big data in biology: From the International
Geophysical Year through the International Biological Program to the Long
Term Ecological Research (LTER) network, 1957-present.


“Theirs’ not to make reply, Theirs’ not to reason why” – a workshop report on Big Data, forensic reasoning and the trial