Helping with inquiries or helping with profits? - Centre for Criminology

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helping with profits? The trials and tribulations of a technology of forensic reasoning. Christopher J. Lawless. London School of Economics and Political Science, ...
Helping with inquiries or helping with profits? The trials and tribulations of a technology of forensic reasoning

Social Studies of Science 40(5) 731–755 © The Author(s) 2010 Reprints and permission: sagepub. co.uk/journalsPermissions.nav DOI: 10.1177/0306312710378787 sss.sagepub.com

Christopher J. Lawless

London School of Economics and Political Science, London, UK

Robin Williams

Northumbria University, Newcastle upon Tyne, UK

Abstract The commercialization of forensic scientific provision in the UK over the last two decades has had a major role in shaping a changing epistemic identity for forensic scientists working within this jurisdiction. Efforts to match the presumed epistemological standards of the ‘pure’ sciences have been brought together with concerns about value for money in a new approach to the interpretation of evidence, an activity that lies at the heart of criminal investigative practice. A study of the Case Assessment and Interpretation method developed by members of the UK Forensic Science Service is used to show how a technical innovation in the delivery of forensic science services to the police has instantiated these two recent social processes.

Keywords Bayes’ theorem, evidence, forensic science, marketization

Recent years have seen an increase in work on forensic science by scholars in science and technology studies interested in the often problematic and complex relationship between what Sheila Jasanoff has described as the ‘two institutions that, perhaps more than any other, are responsible for making order, and guarding against disorder, in contemporary societies’ (Jasanoff, 2007: 761). Notable instances of investigations of particular forensic technologies include Cole (2001) on fingerprinting, and Lynch et al. (2008) on DNA profiling, analyses of perspicuous cases, such as the O.J. Simpson trial, which was the subject of a series of papers published in Lynch and Jasanoff (1998), and Corresponding author: Christopher Lawless, London School of Economics and Political Science, ESRC Centre for Analysis of Risk and Regulation, Houghton Street, London WC2A 2AE, UK. Email: [email protected]

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a number of more general conceptual examinations of law/science interchanges, such as Golan (2004) and Jasanoff (1995). Within this body of work, the predominant focus of attention has been on the means by which scientific authority is constructed in, and deconstructed by, judicial authorities (Jasanoff, 1998; Lynch 1998, 2004). Related STS scholarship has also begun to consider how commercial imperatives condition the use of forensic science in support of criminal justice. For example, Daemmrich (1998) has described the construction of ‘convincing expert testimony’ by American forensic DNA typing firms, arguing that these companies employed a strategy of ‘vertical integration’. In order to maintain the appearance of credible scientific knowledge, firms sought to exert as much control as possible over the DNA testing process, by managing a diverse array of activities and products, both ‘downstream’ and ‘upstream’ of the actual act of DNA testing. Attempts to control the DNA testing process in the US have not been restricted to the commercial sector however, as Aronson (2008: 213) testifies in his description of the FBI’s efforts to construct an over-arching network for DNA analysis. Insightful as these works are, other aspects of the nature and uses of forensic science require further attention. Much of the work described above has focused on DNA profiling,1 yet this technology represents just one forensic resource available to criminal investigators. In the course of any investigation, a potentially vast range of artefacts may be considered as potential evidence, and the collection, analysis and interpretation of them draws on a large number of scientific disciplines. Furthermore, attention to the interrogation of forensic science in the courtroom can serve to overlook the significance of a number of technological resources used to assist the police during criminal investigations. Many such resources may be exempt from judicial scrutiny since they do not necessarily form part of the prosecution’s case against offenders. In this paper we seek to understand and explain the rise of a particular forensic scientific resource offered to UK police investigators, and in so doing pursue the general question of the relationship between science and commerce. By closely examining the development and use of the Case Assessment and Interpretation (CAI) method of evidence evaluation, we reveal in detail one way in which science, law and commerce combine in a mutually constitutive relationship to (in)form a mode of production of scientific commodities purchased by the police in support of criminal justice objectives. We do this against the background of strategic political decisions made to commercialize forensic science provision in the UK. These decisions are instances of the widespread tendency of many governments to bring neoliberal market-oriented sensibilities to bear on all manner of public services, including policing. At the time of writing, the commercialization of UK forensic science remains highly controversial. This paper does not address the wider controversy (but see, for example, the opposing arguments made by Roberts, 1996, and Gallop, 2003). Instead, we seek to show how new approaches to the shaping of forensic scientific services have been prompted by the foregrounding of commercial imperatives. We demonstrate how this shift has led to changes in the way in which forensic scientific support is shaped and delivered to their primary ‘customers’, the police. We also consider the consequences that follow for the regulation of the informational and monetary exchanges that take place between ‘suppliers’ and ‘consumers’ of scientific knowledge.

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We argue that the imposition over the last two decades of increasingly ‘marketized’ forms of forensic scientific provision in the UK has helped usher in a changing epistemic identity for forensic scientists working within this jurisdiction (and with implications for those working elsewhere). In particular, commercial considerations are instrumental for shaping new approaches to the interpretation of evidence, which lie at the heart of criminal investigative practice. We also note, however, that these considerations have come into play alongside efforts to match the presumed epistemological standards of the ‘pure’ sciences upon which forensic science relies for its necessary – and sometimes contested – credibility. Significant changes in forensic science practice are being driven by cognitive and legal interests alongside new commercial imperatives. Accordingly, in order to make sense of the impact of commercialization on an already hybridized activity, we believe it is especially important to study the CAI method, as it represents a neatly encapsulated outcome of the cross-fertilization of these interests. As we go on to demonstrate, however, the example of CAI also clearly highlights the fact that the introduction of a new commercial discourse into forensics itself has encountered resistance. Hence, we also describe how its introduction has challenged the existing series of interrelationships, understandings and attitudes embedded within technosocial networks of criminal investigation. These relate to conceptions of the contribution science can make to criminal investigations, and they also relate to contested understandings of the nature of ‘commercialized’ science, and differences regarding the nature of science itself.

Toward a new paradigm in forensic science Forensic science has been claimed to be the ‘science of individualization’ (Kirk, 1963: 236). Despite a series of critical commentaries on this assertion, a central preoccupation of contemporary forensic science practice continues to be the effort to link material objects to a particular source or origin (Jackson, 2009). As Williams (2007) has argued, however, a great deal of forensic practice appears to lack any substantive epistemological basis. The questionable scientific basis of these particular practices may relate to more general concerns with the validity and reliability of forensic science (Kennedy, 2003; Cole, 2009). In recent times, much criticism has focused on the principle of uniqueness that is commonly attributed to forms of evidence such as fingerprints. For Broeders (2006), the so-called ‘positivity doctrine’ that pervades much evidentiary interpretation rests on highly questionable philosophical principles. Saks and Kohler (2005), in a paper heralding the coming of a new ‘paradigm shift’ in forensic science, point out that the introduction of DNA profiling was accompanied by the rigorous testing of its core techniques and principles. They argue that other forensic disciplines should follow this lead by developing more robust empirical and probabilistic foundations, which would abandon the assumption of discernible uniqueness (Saks and Kohler, 2005: 895). This new conception also reflects a growing interest in the use of Bayes’ theorem by forensic practitioners. Bayes’ theorem can take the general form:



P (H|E) = P(H) × P(E|H) P(E)

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where P(H|E) is the posterior probability (probability of a hypothesis given the evidence, P(H) is the prior probability (probability of hypothesis), P(E|H) is the probability of the evidence given the hypothesis and P(E) is the probability of evidence. Bayes’ theorem has its origins in a paper published in 1763 by the Reverend Thomas Bayes (1763). However, this distinctive interpretation of probability was largely marginalized until the mid 20th century (Fienberg, 2003). Following the work of statisticians such as R.A. Fisher, Jerzy Neyman and Karl Pearson, the frequentist interpretation of probability had become the prominent form by the 1930s (Gigerenzer et al., 1989). Frequentists view probability as being concerned with outcomes of repeated events, enabling inferences about the proportion of outcomes in wider populations of real or theoretically repeated trials (Lucy, 2005). However, in the 1940s, interest began to grow in new orientations to probability. This was prompted in part by the publication of influential research by Jeffreys (1939) and de Finetti (1937), who, along with others, justified the existence of ‘personalistic’ or ‘subjective’ probability, an alternative mode that represented probability as based on measures of subjective belief. This work on personalistic probability influenced research undertaken during World War II (Fienberg, 2003). Many statisticians who worked on military projects became influenced by the personalistic thesis and continued to develop this way of thinking after returning to academia. This work contributed to the evolution of statistical decision theory during the 1950s and 1960s, a field that was developed largely to address problems found in the business world. Particularly notable developments emerged from Harvard Business School where a number of scholars furthered the use of Bayesian principles in decision theory (Fienberg, 2008; Raiffa and Schlaifer, 1961; Schlaifer, 1959). In a significant move, Kaplan (1968) sought to apply Bayesian ideas from the commercial world to the legal domain. Although there had been earlier attempts to introduce more systematic treatments of judicial reasoning (see Wigmore, 1913), Kaplan’s paper directly compared the decision-making of the businessman with that of the legal reasoner, and argued that the application of decision theory to legal problems enabled an improved understanding of the epistemological processes at play in the judicial arena: We might justify our inquiry by pointing out that many business decisions are just as subtle and complex as those in the law, yet decision theory has already been used in the business world with some success. A better justification of this, however, is that although we are in most legal areas far away from a usable quantification of our problems, the effort of thinking through the abstract quantitative relations among different variables turns out to provide a host of insights into the workings of our legal system. (Kaplan, 1968: 1065)

Kaplan demonstrated how the concepts of probability, expectation and utility associated with economic contexts could be used to re-appraise the reasoning processes of juridical actors. A landmark paper by Finkelstein and Fairley (1970) represented a further advance in the application of Bayesianism to forensic problems. Their paper argued that ‘unique’ characteristics could not be definitively attributed to an individual, but that certain infrequently occurring forms of transfer evidence could demonstrate significant probative

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weight if incorporated into a Bayesian formula. Not everyone agreed, however, with the proposal to use statistics so heavily in judicial proceedings. In an equally celebrated reply, Tribe (1971) argued that the use of Bayesian probability theory could skew legal reasoning processes with potentially dangerous consequences. Around the same time, a growing preoccupation with notions of scientific rigor in forensic science raised interest in the application of mathematical techniques to evidence evaluation. In 1964, a special session on statistics was held at a meeting of the American Academy of Forensic Sciences, which reflected the ‘growing awareness of the usefulness of statistical methods’ (Kingston, 1965a: 79). This period saw increased academic activity in applying such methods, including Bayesian models, to evaluate forms of transfer evidence (Kingston 1965a, b). The Bayesian approach to forensic evidence received further impetus through the work of Ian Evett, a statistician with the UK Forensic Science Service (FSS), who collaborated with the noted Bayesian scholar Denis Lindley (Evett, 1984, 1986; Evett and Joyce, 2005). Evett used a form of the equation in which two opposing hypotheses (Hp and Hd) are assessed in conjunction, yielding the formula:



P(Hp|E) = P(Hp) × P(E|Hp) P(Hd|E) P(Hd) P(E|Hd)

These opposing hypotheses represent prosecution and defence arguments in a particular criminal case. The construct on the far right-hand side of the equation is known as the likelihood ratio (LR), which provides a measure of the probative value of a piece of evidence (E) relative to mutually exclusive prosecution and defence hypotheses (Hp and Hd, respectively). Evett’s work significantly contributed to a growing perception of the potential of Bayes’ theorem to provide a more scientifically robust basis for the assertions of forensic scientists. For Evett, Bayesianism provided a unique solution to the problems of forensic work: That framework – call it Bayesian, call it logical – is just so perfect for forensic science. All the statisticians I know who have come into this field, and have looked at the problem of interpreting evidence … have come to see it as centring around Bayes’s Theorem. (Evett and Joyce, 2005: 37)

The use of Bayes in forensic science is now regarded by many as aiding the transition of forensic science from a categorical epistemology to a more conditional paradigm, one more in keeping with generic scientific standards. Furthermore, Bayes’ theorem is seen to provide the basis for a more accountable form of reasoning than the opaque and intuitive methods that had traditionally been associated with several forensic disciplines (such as fingerprint analysis), whereby examiners draw conclusions on a categorical ‘yes/no’ basis with a strong subjective element, and without the use of quantitative metrics to enable objective assessment of their validity (Broeders, 2006: 153).2 Bayes’ theorem is viewed by many influential figures within forensic science as especially well suited to the contingencies of criminal investigations, although this view is

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not universally shared across the criminal justice system in England and Wales, let alone elsewhere in the world. There has been significant academic commentary on the antagonism of English judges to the introduction of Bayesian reasoning into jury trials (see for example Lynch et al., 2008: 190–219). Much less attention has been given to the use of Bayesianism by forensic scientists who analyse and interpret forensic evidence to support the investigative process. The work reported here does not bear on the presentation of evidence in court but only on the interpretation of evidence for police investigators.  CAI can be seen to be a partial product of the ‘new paradigm’. Equally, it is a response to the changing commercial environment in which forensic scientific services are provided to police forces. Its double duty marks an attempt to use Bayesianism in two ways: to reform the reasoning practices of forensic scientists, and to facilitate the newly reconfigured relationship between police and forensic scientists as ‘customers’ and ‘providers’, respectively. In this sense, CAI reflects a concern with optimizing the efficiency of scientific contributions to criminal investigation, while promoting improved standards of scientific propriety. Before we describe in further detail how CAI encompasses both these imperatives, it is necessary to provide some background on efforts to reshape forensic science along commercial lines.

Commercialized science in criminal justice:The UK trajectory Central to this history is the introduction of the ‘modernizing’ economic rationality of the new public management (NPM) in the early 1980s. Variants of this approach to the organization and delivery of public services have continued to influence all public sector domains over the past 25 years. This approach, described by Garland (2001: 190) as ‘a ragbag of techniques, models, analogies and recipes for action that are loosely bound up by their appeal to economic rationality’ has meant that police work – including that part of forensic scientific support provided by the public sector – has increasingly been understood as simply one of the many ‘markets in services, provision and expertise’ (Dean, 1999: 161) that comprise modern public sector organizations, in general. Hence, changes to the provision of forensic science reflect the wider trend, promoted by successive UK governments, toward neoliberal reforms of public services. Broadly construed, these reforms have sought ‘the empowerment of users as customers’ (Ferlie et al., 1996: 210), and to induce greater accountability for public services via the introduction of marketized forms of provision, whereby ‘choice’ promotes greater transparency through the use of measurable data to enable user-customers to make informed decisions (Griffiths, 1988). The neoliberal state also views private enterprise and entrepreneurial initiative as playing key roles in promoting innovation in this sector (Harvey, 2005: 64). The Home Office Circular 114 (Manpower Effectiveness and Efficiency in the Police Service, Home Office, 1983) valorized the concepts of ‘economy’, ‘efficiency’ and ‘effectiveness’, which later became central to the NPM and which were heavily promoted by the Audit Commission in the 1980s. The endorsement by the Audit Commission of NPM and its ‘ethos of business management, monetary measurement and value-formoney government’ (Garland, 2001: 116 ) has meant that all forms of police-related practice, including forensic science, have increasingly become subject to its developing discursive framework.

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Following the recommendations made by the accounting company Touche Ross in their report on police scientific support (Touche Ross, 1987), the FSS introduced direct charging to forces and other clients in 1991 (McFarland, 2003). Around the same time, other firms began to compete for business with the FSS, most notably LGC and Scientifics Ltd. Both of these providers originated in the public sector before becoming private suppliers of forensic science services in the 1990s. In 1996, Forensic Alliance, the first truly private sector forensic firm, entered the market. In the same year, the FSS replaced standard charging (per item submitted) with product-based charging. In the latter methodology each product, such as a body fluid search, tool mark examination or cannabis identification, was defined as encompassing a standard set of activities (National Audit Office, 1998: 30). Rather than providing a price for each test, each activity was costed to give a price more closely related to the actual work performed, ‘thus providing customers with a better understanding of the true costs of services and enabling them to make informed judgements about their value’ (National Audit Office, 1998: 30). Charging was seen as a key mechanism to control demand on the part of the police and to compel the traditionally internally focused laboratories to respond to the needs of their ‘customers’. By 2001, the FSS, as the major provider of forensic science within England and Wales, had become an agency of the UK Home Office with Trading Fund status. However, the commercial freedoms licensed by this status were limited and its mission (‘to provide forensic science information and expertise to support the investigation and detection of crimes and the prosecution of offenders; and to contribute to the prevention, deterrence and reduction of crime’ [Forensic Science Service, 2001]) emphasized public service rather than business values. Nonetheless, the Government-commissioned McFarland review of the FSS, published in 2003, expressed an interest in moving toward product-based charging for services, where ‘a bundle of products are offered together with value adding information and intelligence’ (McFarland, 2003: Sec. 3.3). Forensic science service providers had begun to introduce initiatives such as volume discounts and loyalty schemes, leading McFarland to conclude that a ‘truly competitive market’ had begun to develop in UK forensics (ibid.). Furthermore, the review went on to argue that the introduction of ‘best value’ principles ‘ had forced the police to seek better value for money in the bought-in services’ (ibid.), and that forces had become ‘informed customers’ who ‘played off suppliers against each other’. The significance of these changes for the FSS can be seen by contrasting its most recent mission statement with the version from 2001 cited earlier. In place of a commitment to contribute to a ‘safer and most just society’, the current mission of the FSS is ‘to retain and reinforce our leading position as the principal provider of forensic science to the UK criminal justice system (UKCJS), and use this platform to become the leading provider worldwide, thereby enhancing long term shareholder value’ (Forensic Science Service, 2009). It should be obvious to even the most casual reader that a distinctive and new set of priorities has been operationalized in this recent revisionary text. In particular, that commercial relevancies and practices are now central to the shaping of forensic science ‘products’, that there have been consequential changes in the manner in which they are to be delivered, and that these have altered the nature of the informational and monetary exchanges taking place between the FSS as ‘supplier’ and the police as ‘customers’, or ‘consumers’.

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Although the commercialization process is likely to continue, it already has exerted a tangible influence on the course of innovation in forensic science. CAI reflects this influence, but has itself shaped developments within the field. The Association of Forensic Science Providers (AFSP), which represents both external and in-house suppliers of scientific support in the UK and Republic of Ireland, has recently announced new standards for the assessment and reporting of forensic evidence. These standard procedures are closely modelled on CAI principles, and all providers of forensic science, whether commercial companies or in-house police laboratories, will be obliged to follow them (Association of Forensic Science Providers, 2009). In what follows we describe in more detail key features of the model, which reflect this dual influence. It has been the subject of detailed empirical research by one of us (Lawless) over the past 3 years, involving a number of interviews with senior figures within forensic science and policing, including those directly involved with the origination of CAI.3

Producing case assessment and interpretation: Scientific and commercial imperatives CAI has sought to promulgate the new approach to scientific evidence assessment by consciously employing an explicitly quantitative mode of reasoning. This approach is shaped not only by recent critiques of traditional forensic theorizing, but also by an attention to new doctrines concerning the transition to product-based charging. These are reflected in the emphasis on service delivery in relation to timeliness and measurable efficiency. CAI was formulated to meet the challenges of a ‘radical change in culture’, where the costs of forensic science were ‘no longer invisible to operational policemen’ (Cook et al., 1998a: 151), and where police forces themselves were devolving greater financial responsibility to officers more closely involved in day-to-day casework. In the light of these changes, there was a sense that customers would benefit from a greater degree of consultation over how forensic casework proceeded. CAI therefore aimed to enable customers to make better decisions about how to allocate their own resources, in turn creating a ‘greater sense of value for money’ (Cook et al., 1998a: 152). The assessment of evidence within the framework of CAI is not designed to occur only after all the evidence has been accumulated; instead, it is viewed as a continually recursive and collaborative course of action, involving a significant degree of consultation, feedback and input throughout an extended investigative trajectory. This echoes the arguments of numerous UK official reports that have urged police and forensic science providers to collaborate in order to maximize the efficiency of their separate efforts (Barclay, 2009; Her Majesty’s Inspectorate of the Constabulary, 2002). In what follows, we describe the enactment of CAI and show how it can be seen to be shaped by a combination of statistical, policing, juridical and commercial considerations. The original concept for CAI arose during the early 1990s within the Interpretation Research Group at the FSS, which included a small group of statisticians working alongside a number of highly experienced forensic practitioners. This group had been brought together in order to consider responses to the introduction of direct charging. With Bayesianism beginning to be used for evidence interpretation, the group considered how its use could be extended to meet the challenges of a commercialized market:

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About four or five of us got together and we said we can do something here, employ Bayesian thinking to actually develop a better service. We decided we’d do a project, put together a model and then trial it out with practitioners, bring together practitioners in workshops, and run through some cases. And get feedback from them, see if it worked for them, and to test our thinking, and we ran a series of workshops in different disciplines across the country and refined the model. (Interviewee A2, 2007)

CAI therefore developed not through a ‘top down’ imposition of Bayesian ideas, but instead through a collaborative scientific exercise, which incorporated the insights of the practitioners who would be expected to use it. However, it is important to note that, as well as engendering a collectively produced system of forensic reasoning, CAI also emphasizes a reciprocal consideration of the customer’s specific needs: equally of importance was a second strand which was working with the users of forensic science information and opinions to find out what they needed, how we could tailor what we were giving them to meet their needs and to develop mutual understanding really … a two-way process, understanding each other’s needs and what we can do. (Interviewee A2, 2007)

Although forensic scientists are still regarded as being in the service of their police ‘customers’, CAI is seen as obliging both providers and customers to be more transparent about the bases of decisions based on evidential analysis: And once we started to charge customers … to get the customer to almost dictate what we did. But then you say how did they make those judgements? You needed something like CAI to make good judgements. (Interviewee A2, 2007)

The emphasis on good communication is perceived as necessary for the scientist to have ‘an adequate appreciation of the case circumstances so that he/she can set up a concise framework for thinking about what kind of examinations may be carried out and what may be expected from them’ (Cook et al., 1998a: 153). The authors of CAI treat the construction of such a framework as a necessary prerequisite for the development of propositions relevant to the evidence and the case. They stress the need for scientists to take a ‘balanced view’ of each case, in line with what they regard as the principles of ‘the Bayesian view of evidence’, which ‘enables the correct combination of information from answers to two opposing and relevant questions’ (Aitken, 2009: 410). In broad terms, CAI is delivered to police users in three overarching phases – customer requirement, case assessment and service delivery – where completed test results, together with an assessment of their significance to the case, are provided to the customer (Cook et al., 1999). This process is sensitive to feedback, and continually subject to the customer’s review, and new lines of inquiry generated by subsequent developments can be readily incorporated into the model. The CAI method provides a framework through which alternative propositions can be considered in a Bayesian manner. A central resource is the deployment of the technical device of likelihood ratios (LRs) through which the scientist seeks to organize the logical processing of evidential propositions with Bayes’ theorem. The central activity of CAI is thus the formation and evaluation of propositions via their re-calculation as LRs, which take the form:

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Hence each evidential question is addressed in terms of hypothetical prosecution and defence arguments. CAI obligates scientists to clarify the propositions customers wish to address, by redescribing them in more quantitative terms via the likelihood ratio method. The conscious use of proposition pairs to generate LRs in part reflects a concern to introduce a more neutral, balanced approach to police investigative work. This contrasts with previous approaches to police investigation, which were regarded as more heavily slanted toward a prosecutorial stance. It used to be like ‘Life on Mars’ really, you picked somebody who you thought might have done it and then tried to prove it, and if that didn’t work you tried to prove somebody else did it. (Interviewee B, 2008)

This approach was regarded as inimical to standards of scientific propriety, because police officers would direct forensic scientists to analyse evidence that they regarded as potentially incriminating. This created an unbalanced division of investigative labour in which the police who directed the analysis lacked relevant scientific understanding. Such a misdirection of scientific effort was regarded as risking miscarriages of justice via the neglect of important information. The LR method allows investigating scientists to consider the relative significance of a particular piece of evidence for the competing prosecution and defence propositions, enabling investigators to gain a measure of the probative value of the evidence. LRs greater than one indicate that a piece of evidence may provide support for the prosecution argument, with those less than one favouring the defence: In likelihood ratio terms you either have weight for the prosecution, weight for the defence or if the likelihood ratio is round about one it doesn’t help to progress it either way. (Interviewee A1, 2006)

Prior probability estimates play an important role in calculation of LRs, with scientists being encouraged to base their prior estimates on the most robust foundations possible. In this way, CAI emphasizes transparency, even in the early stages of an investigation: there’s things about what priors have we got and are they sound, are they realistic, are they robust and are we good at forming likelihoods with the evidence. … So it’s testable, it’s exposed, it can be challenged, but it’s explicit, people can see the priors that we are using … so anybody else can test them …. (Interviewee A1, 2006)

As well as enabling the forensic scientist to represent his/her expectations in a more precise and quantifiable way, CAI also facilitates agreement between providers and customers on the most effective course of action to pursue in an investigation. A simple example illustrates the operation of CAI. An individual wearing a ‘fleece’ garment is apprehended soon after an alleged break-in, and the police propose to analyse

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the garment for traces of broken glass, and compare the traces with samples taken from the broken window. What did happen is that police would say, there’s a break-in, there’s been a glass window broken, look for glass on the clothing … and so they kind of directed the scientists to apply certain techniques, whereas what we really try and encourage the police officers to say is, ‘what’s your problem, what are you trying to establish?’ in terms of what are the issues … is the issue, whether or not he broke the window, or is the issue whether or not he went inside the property, or is the issue whether or not he handled stolen goods … I’ll go away and have a think about it and then come back with a strategy … a costed time-strategy, and that’s the whole essence of CAI, really in terms of the case assessment bit, the ‘A’ bit …. (Interviewee A2, 2007)

Formulating such propositions, particularly when considering specific forms of evidence, necessitates a careful consideration of the possible circumstances surrounding an incident: if we’re trying to evaluate evidence, we’ve got to be very careful to specify the proposition and the alternative, quite crisply. … A common one you see used by scientists is they generate the proposition that M was involved in the burglary and you say OK, let’s have a think about the probability of the evidence, then given he was involved in the burglary and let’s say it was breaking a window, reaching in to take some goods. What’s the probability I would get this amount of glass on him that matches the window, given that he was involved in the burglary? (Interviewee A1 2006)

The focus therefore now changes to a consideration of what an examination of the garment will reveal if successive propositions are taken to be true. These expectations can be expressed as a series of probability distributions for specific numbers of glass fragments found to match control samples taken from the broken window, given two mutually exclusive propositions. Cook et al. (1998a) provide a simplified example, in which they classify the number of matching fragments into three groups, ‘none’, ‘few’ and ‘many’, and assign a probability estimate for each in the light of the two competing propositions. These estimates may be based on a number of parameters, including previous casework experience, scientific knowledge about glass transfer and previous data taken from similar cases. To assess the expected number of matching fragments when a ‘prosecution’ proposition holds, the scientist must take into account a number of further factors, including the time lapse between arrest and confiscation of the clothing, the retentive properties of that type of garment, and the likelihood that the suspect entered through the broken window (Cook et al., 1998a: 154). In order to assess a defence proposition, the scientist must instead consider the possibility that the suspect is wholly unconnected with possible intrusion. So that really clarifies the proposition and the alternative and you start to work on it a bit better and say what’s the probability of getting this amount of glass given he broke the window and reached through, there’s a probability of getting this amount of glass if he didn’t do that but he did receive [stolen goods]. (Interviewee A1, 2006)

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Here it can be seen how CAI provides a means to link probability estimates with specific conjectures. Measures of expected quantities can inform the reconstructive process. From the information in Table 1, it is possible to obtain LR values by dividing the proposition pairs corresponding to each matching fragment group, as shown in Table 2. These can be further summarized in terms of the support they show for each of the propositions (Table 3). This leads to the assessment that there is a 65 percent chance that the examination will provide moderate support for a prosecution proposition Hp (‘the suspect broke the window and entered the premises’), and 30 percent chance that it will only provide weak support. If the suspect is innocent, however, then there is a 95 percent chance of moderate support for proposition Hd (‘the suspect is unconnected with the scene’), although there is also a 5 percent chance that the evidence will falsely inculpate him. At this stage, the customer may decline to have a particular test carried out on the assumption that the information would not be useful for the investigation. A key feature of CAI, however, is the ability to reassess the decision-making process in light of updated information. For example, an intermediate test to determine the refractive index of the glass from the window could provide further indication of the probative force of the evidence. If this glass is found to be an unusual type, this will have a considerable effect on the probability estimates under the assumption of innocence, because the chances of finding such an unusual type of glass on an innocent person’s clothing would be even slimmer. Such information may influence a decision to proceed with a full search of the garment for glass fragments that match the window samples. In this way, probability estimates can be used to guide the way in which police customers make decisions about the kinds of tests (and resulting ‘products’) purchased to further their investigations. Value for money can be optimized and tests that may yield data of little use to their investigations are not undertaken.

Table 1.  Probabilities of Finding Quantities of Matching Glass (Reproduced from Cook et al., 1998a: 155) Quantity (Q) None Few Many

Probability Pr (Q|Hp)

Probability Pr (Q|Hd)

0.05 0.30 0.65

0.95 0.04 0.01

Table 2.  Likelihood Ratios for the Three Values of Q (Reproduced from Cook et al., 1998a: 155) Quantity (Q)

Likelihood Ratio (LR)

None Few Many

0.053 (Moderate support Hd) 7.5 (Weak support Hp) 65.0 (Moderate support Hp)

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Probability of Opinion if Hp is true

Probability of Opinion if Hd true

0.05 0.30 0.65

0.95 0.04 0.01

CAI therefore functions to co-ordinate a quantitative mode of analysis suitable for addressing the decidedly contingent nature of criminal investigation, but which also takes economic concerns into account. However, CAI is also designed to take account of the wider case circumstances surrounding analysis of evidential items, so that the investigative process as a whole can proceed along Bayesian lines. This is achieved by ordering propositions into a hierarchy, which acts as an organizing principle for their construction and assessment.4 This hierarchy classifies propositions along three levels, in order of their relevance to the ultimate issue of whether a criminal offence has been carried out (Cook et al., 1998b). Level I, the source level, includes propositions on the origins of the evidentiary material, and exclusively involves the measurement and comparison of quantitative data (Cook et al., 1998b: 232–33).5 For example, to address the Level I proposition on the probability of glass fragments originating from a particular window, it could be feasible to analyse the refractive index of the garment fragments against a database including refractive indices of glass windows from several different manufacturers. Level II, the activity level, involves a reconstruction of events in the case. In the above example, Hp and Hd represent activity-level propositions. Level II propositions may be considered prior to commissioning tests of Level I propositions, in order to encourage customers to clarify the precise questions they wish to address. Level III, the offence level, concerns the probability that the suspect committed a criminal offence. Level III propositions generally are regarded as being the domain of the jury, assisted by the judge (Jackson et al., 2006). Although Level III propositions are taken to be outside the domain of the forensic scientist, the latter are regarded as well placed to assist the court in their deliberations. Hence, in order to progress through the hierarchy, the analyst needs to translate the circumstances of the case into propositions, most notably at Level II. In the following example, DNA was isolated from a discarded ammunition cartridge recovered from the scene of a fatal shooting. The DNA subsequently matched that of a suspect. As the following discussion shows, however, the match alone was not sufficient grounds to assume the suspect was the killer: In preparation for arresting this guy there’s a discussion with the scientist about the potential strength of evidence and the officer says something like ‘you find me his DNA on that cartridge

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case and I’ll charge him with murder’. Now that may or may not have been a sound strategy but obviously what he was doing was translating the potentially matching DNA on the cartridge case, which would be the bottom of our hierarchy, straight through to guilt, and I think most lay people would say his DNA’s on the cartridge case, he must have fired the gun. And that just shows you may get a magnificent likelihood ratio for the source of DNA on the cartridge case but what that is in terms of whether or not he pulled the trigger, there’s a lot of intermediary stages to go through to translate the weight of evidence for DNA to weight of evidence for guilt in terms of pulling the trigger. (Interviewee A1, 2006)

This example highlights the inferential burden that needs to be overcome in order to establish a link between the presence of the DNA match and the contribution of the suspect to the murder. The question of whether the suspect pulled the trigger represents a Level II concern. In order to address this question, and to link it with the DNA findings, a Bayesian analysis is dependent on optimal information about the case circumstances. If such information is not forthcoming, then the contribution of the scientist may be limited to source level: if we stay there, then someone has got to progress it to activity level, to get to the offence, and the people who are gonna do that are gonna be people like lawyers, judges, jurors, lay people. Who aren’t as well placed as a scientist should be to get to activity … so there’s that disjunction really … to move from source to activity. But the big temptation is, for other people to do it then, on behalf of the scientist. (Interviewee A2, 2007)

Consideration of Level II propositions reflects a stated aim of CAI, namely that scientists, wherever possible, should address ‘questions that are a stage advanced beyond the simply analytical’ (Jackson, 2000: 84). However, the consideration of Level II propositions by forensic scientists raises some questions about the division of ratiocinative labour in the investigation of a criminal case. The kinds of level II proposition outlined in the CAI literature are lines of enquiry that one would expect the police to pursue. For any of the hierarchy … the skills of the scientist would be around formulating the prosecution hypothesis and defence hypothesis based on what the two stories are, and being expert at providing sound probabilities for the evidence. (Interviewee A1, 2006)

The hierarchy of propositions can be regarded as a means of further incorporating the input of forensic scientists into an investigation. In this way, the application of CAI implicates a notably different mode of criminal investigative practice. Rather than promoting a version of commercialized forensic science involving the provision of tests in relative isolation from other elements of the casework process, CAI promotes a form of ‘forensic science’ which renders the criminal investigative process as a form of scientific inquiry itself (although it stops short of prescribing the method for use in the final deliberations of the jury). The adoption of a more balanced mode of inquiry is largely driven by a desire to directly address ‘customer’ requirements, tailoring the examination strategy and providing ‘expert’ assessment in order to provide value for money to police clients (Booth et al., 2002: 225).

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This section has sought to demonstrate how Bayesian reasoning guides the decision-making process by assigning measures of potential probative value to evidential artefacts. The return of measures of expected usefulness of evidence is intended to provide an indication to customers as to how resources may be most efficiently utilized. Decisions made about the most efficient course of action in an investigation not only provide the customer with value for money, but also limit the amount of redundant material that the provider may have to analyse. Hence the Bayesian approach to selecting investigative strategy also functions as a streamlining process for the provider, aiding the latter in efficient resource allocation. The use of Bayesianism plays a key role in meeting this challenge by acting as a framework for the construction of a process in which both provider and customer resources can be managed efficiently. At the same time however, it is intended to render the investigative reasoning process more ‘scientific’ by ensuring that it follows a robust and transparent path of empirically informed reasoning. It enables the re-translation of a fragmented array of judgments about evidence into a seemingly cohesive, ‘logical’ series of quantitative assessments. This sense of ‘logicality’ is a hybridized epistemology. It provides an apparently improved ‘scientific’ framework for arranging the contextualized interpretation of evidence, but which simultaneously determines the potential ‘value’ of evidence in order to shape a commercially ‘efficient’ decisionmaking process. There has, however, been resistance to the adoption of CAI by forensic scientists and other actors. In the next section, we describe some of the issues raised by efforts to implement CAI to support criminal investigations.

Resistances At first glance, the CAI method can be seen to function as a ‘boundary object’ (Star and Griesemer, 1989). It is true that CAI is intended to bring together actors who broadly share common goals – to ensure justice is done – but who inhabit the different epistemic worlds of science and policing. The Bayesian framework upon which CAI is based facilitates a mode of reasoning which is adaptable, abstracted from local contingencies, and acts ‘as a means of communicating and cooperating symbolically’ (Star and Griesemer, 1989: 410). Equally, CAI provides a ‘standardized form’ for exchanges between the diverse epistemic communities of scientists and police, in that it represents a means for converting evidential items, in whichever form they take, into commonly assessable indices. However, when used in practice, such translations exposed significant complications for those who sought to develop and use them. For example, one clear issue concerned the extent to which the use of the hierarchy of propositions was seen to challenge the decision-making power of the courts. Although CAI accepts that Level III propositions are the domain of the court, the manner in which the link between Level II and Level III is formalized remains unclear. The use of Bayesianism in court has already been shown to be a source of notable tensions (Lynch et al., 2008: 190–219), and it is unclear whether the design of CAI is able to fully overcome the communication problems between scientists and the court.

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We must emphasize, however, that the epistemic tensions on which we focus in this paper arise predominantly within the relationship between  forensic scientists and police in the course of investigations. In describing issues at this stage of the criminal justice process we seek to supplement previous work that has been concerned with the problems that arise when probabilistic reasoning is introduced into courtroom proceedings. Although the CAI literature portrays a process in which data are unproblematically embedded into the Bayesian formula, many practitioners encountered significant difficulties with using Bayes’ theorem. Rather than being regarded as a convenient algorithm, some regarded Bayesianism in almost artisanal terms. For example, one American developer of Bayesian systems for forensic analysis described using them as akin to ‘learning a craft’ (Interviewee C, 2007). Many problems lay with the construction of alternative (generally defence-led) propositions. Prosecution propositions simply need to reflect the argument that a certain suspect committed a particular offence, but an indeterminate number of factors must be taken into account when attempting to construct an alternative hypothetical account of the events in question. Difficulties were encountered in choosing what were regarded to be appropriate pairs of propositions. Interviewee B, who used CAI to reconstruct incidents in ‘cold case’ reviews, highlighted this problem thus: The propositions – it was the result we’ve got is ‘how likely is it to arise if it [Firearms Discharge Residue] got into his pocket at the time he fired the weapon that killed [British TV personality] Jill Dando’, and ‘how likely is it to have got into his pocket at some other time?’ Now that does illustrate my difficulty with the process. One, why is that second proposition the one they used? Why isn’t it ‘anybody else in the street’, why isn’t it ‘anybody else who has an interest in guns’? (Interviewee B, 2008)

This example indicates the myriad of factors that could be taken into consideration when constructing propositions. Furthermore, intractable problems were often encountered when attempting to derive data upon which propositions could be considered: We probably were influenced by the fact that we could see some of the propositions we weren’t going to get data on. And that wasn’t sensible was it? Really, we should have got the propositions right and worried about the data afterwards …. (Interviewee B, 2008)

The shortage of data forced him and his team to go ‘backwards and forwards’, and the type of propositions constructed were directly dependent on the availability of the data. Rather than constructing propositions a priori and then evaluating them, propositions were formulated after considering which data were available. Our field research uncovered that some forensic scientists were sceptical about CAI. A notable proportion of the law enforcement community also was reluctant to adopt Bayesian reasoning practices, contrary to the prominence of Bayes’ theorem in theoretical texts. This scepticism was especially evident in discussions during a visit to one of the largest police forces in the UK with a significant in-house forensic science capability.

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Bayesian methods for the interpretation of many forms of evidence were notably absent from operating procedures, even in the case of a large-scale ballistics analysis facility this force had developed. During the visit one senior forensic scientist commented that he regarded CAI as an ‘interesting theoretical project … but difficult to see the practical application’ (Interviewee D, 2008). More significantly, our research strongly indicated that attempts to apply CAI led to further tensions between forensic scientists and their customers. For CAI to function effectively, forensic scientists need high levels of information from the police. This can be seen in the way the hierarchy of propositions obliges police to disclose more information to forensic scientists in order to produce a Bayesian analysis. CAI also emphasizes the desire for scientists to have more influence over which pieces of evidence should be most pertinent to a case. However, it became clear that such re-positioning of forensic scientists within the decision-making hierarchy was out of keeping with traditional police views. CAI was found to conflict with the increased pressure on police forces to prosecute suspects: We had another group of officers who kind of misinterpreted really … we had to do a bit of work re-assuring them that we weren’t out to … how shall I put it? Help the defendant …. (Interview A2, 2007)

It appeared that police investigators viewed the role of forensic scientists to be more limited: simply to provide scientific data about evidence, separate from any other deliberations made by police when investigating a criminal case. CAI also was found to have stimulated other areas of controversy within the forensic science community. During interviews, many practitioners acknowledged their reluctance to open up their methods to the kind of epistemological scrutiny that Bayesianism demands: Another area for objection might be because we were challenging conventional wisdom, and we were almost challenging the basis of their expertise. (Interview A2, 2007)

Other forensic scientists disagreed with the systematic way Bayesianism had been adopted. For example, one individual criticized the developers for conflating an adjunctive technique with the scientific method itself, noting that ‘[s]tatistics is a tool. Its not science’ (Interviewee E, 2008). This statement points to a fundamental difference of opinion within the epistemic community over how ‘science’ should be framed. Scepticism was also voiced about the suitability of Bayes’ theorem to forensic problems: Bayes’ theorem was really designed for long-running experiments, Bayes used it for rolling balls along a table … now a crime scene is a single experiment, irreproducible in that sense …. (Interviewee E, 2008)

More seriously, the epithet ‘Bayes’ was regarded as acting as a mere re-packaging of the role of human judgement in forensic casework:

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It’s giving a scientific coating to what basically is a human judgment about the belief in something … now of course that’s what Bayes was trying to get around, the belief thing, but put it on an objective basis, on measured results … this is not what’s happening …. (Interviewee E, 2008)

The main issue at the heart of these criticisms is not the adequacy of Bayes’ theorem per se; it has more to do with current prescriptions for its use in forensic scientific practice. Another respondent, a researcher at a policing oversight body, questioned whether the scope of Bayesian analysis in the CAI method was too narrow, while also acknowledging the complications involved in the extension of Bayesianism: What would be useful is if you went away and did a Bayesian analysis of the whole process right from the recovery of evidence, as there are so many factors to consider even from there. (Interviewee F, 2008)

Finally tensions also became apparent with another set of crucial actors: the FSS management themselves. Although CAI emphasizes engagement with customers to facilitate efficient use of the latter’s resources, it seems that CAI clashed with management interest in maximizing revenue from the same set of actors: The FSS could see a lot of benefit … but felt some commercial problems … the way we were working in terms of earning our incomes was items examined, the more items we examined the more income we got. So there was almost a counter-pressure not to apply CAI, because CAI in some ways, said ‘lets just look at the items that are gonna be really effective, really efficient’, in addressing this question, and if you decide with the customer these are the key issues in the case, the strategy to address these key issues is this, this and this ... and I think therein lay some of the difficulties from the managers and leaders, because you could see the natural consequences, if we apply CAI … we’re gonna lose a lot of income, potentially. (Interviewee A2, 2007)

It was clear that the FSS management saw commercialization in a different light. Rather than providing tests in a selective and targeted manner specific to customer needs, FSS management viewed forensic scientific ‘products’ as being provided to customers on a wholesale basis. Indeed, the FSS appears to favour a move away from an integrated approach: In the FSS now, they’ve gone away from experienced case scientists looking at multiple evidence types and synthesizing all of the evidence together to neat little boxes of ‘products’ where ‘you want the hair analyzed? (Interviewee E, 2008)

This move away from the integrated approach therefore appears to favour the supplier more than the CAI-led approach, which attempts equitable treatment of customers’ resourcing concerns. Here it is possible to identify different ideas of what a ‘commercialized’ science might entail, since the CAI developer’s interest in maximizing the customer’s own value for money is understood to contradict the need for the business leaders to maximize readily measurable profits, particularly in a competitive economic environment.

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CAI – a hybrid construct? The CAI method is meant to render the process of forensic investigation more ‘testable, more explicit’ (Interviewee A1, 2006). The application of CAI becomes a useful means of converting the closed, intuitive reasoning forms of forensic scientists into a transparent and measurable form of probabilistic reasoning. Operationalizing this ‘new paradigm’ of forensic science also supplies systematic foundations for assessing statements made by forensic scientists. Accordingly, scientific expertise can be effectively audited via CAI, as it obligates scientists to be explicit about the bases of their inferences, which can then be tested with statistics. By introducing this probabilistic discourse, CAI is intended to enable both customers and service providers to assess the potential ‘worth’ of carrying out a scientific test, and the subsequent value of the evidence in an investigation. This, in turn, is meant to assist both customers and providers in making decisions about the allocation of further scientific and other investigative resources. CAI represents a significant attempt at developing an important tool for customers to make such decisions under conditions of uncertainty, and to provide a means of measuring the epistemic risks associated with them. Finally, it is aimed at allowing providers to prioritize their own resources, by seeking to encourage customers to purchase only those tests that are more likely to yield meaningful results, hence reducing potentially wasteful redundant procedures. In addition to reflecting accepted notions of scientific propriety, CAI is strongly compatible with certain ideas about how neoliberal regimes obligate ‘experts’ to be more accountable to their audiences (Rose, 1996). By treating CAI as an auditing mechanism, one can understand how Bayesianism can be regarded as serving to promulgate a ‘liberalizing’ economic orientation to forensic science. Rose (1996) describes a so-called ‘advanced liberal’ form of governance as one that reduces the role of ‘the State’ and detaches expertise from the political apparatus, re-positioning experts ‘within a market governed by the rationalities of competition, accountability and consumer demand’ (Rose, 1996: 41). Auditing has become ‘one of the key mechanisms for responding to the plurality of expertise and the inherent controversy and undecidability of its truth claims’ (p. 55). The imposition of such a culture requires public activities to be ‘made auditable’, producing new visibilities ‘for the conduct of organisations and those who inhabit them’, thus challenging the opaque epistemologies previously associated with many forensic ‘experts’. One may go so far to argue that the Bayesian philosophy can itself be seen to reflect a deeper continuity with the individualizing tendencies of neoliberalism. Mirowski (2004: 172) argues that Bayesian probability exhibits a ‘willful methodologically individualist’ character. According to this view, Bayesianism is compatible with a utilitarian conception of individual action, which grounds rationality ‘in the epistemic capacities and inclinations of isolated individuals’ (ibid., emphasis added). With its emphasis on the logical justification and quantification of personal beliefs, CAI provides a means in which personalized epistemologies can be used to inform apparently ‘objective’ logical forms. However, while the tenets of this system may demand that assumptions must be scrutinized, the contingent and time-pressured nature of forensic scientific activity may impede the use of more objective databases to support propositions, or indeed force users

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to employ more pragmatic means of reasoning in a manner that subverts the process. Furthermore, ambiguities associated with proposition construction may potentially exert a tendency to conceal sources of subjectivity, rather than exposing them. The CAI method nonetheless provides a potentially empowering intellectual currency for these actors. The structuring of propositions, moving gradually from ‘source’ to ‘activity’, for example, allows forensic scientific knowledge to impinge upon reasoning practices traditionally seen as the preserve of other actors. The conversion of postulated instances of a suspicious event into a series of quantifiable ‘propositions’ converts more information into a form that the scientist is better placed to manage. It enables the scientist to move away from strictly laboratory-based concerns to wider questions about evidence perhaps more commonly associated with police officers. Converting this information into a more ‘scientific’ form subsumes the importance of other forms of knowing, which in this example include less easily visible, experiential forms of reasoning, which may reflect a certain ‘organizational memory’. This epistemological colonization is largely unseen by the court, however, even though CAI adopts the adversarial discourse of ‘prosecution’ and ‘defence’ propositions to enable forensic scientists and police to progress their investigations. Juries still may have the final say on matters of guilt and innocence, but the nature and origins of the evidential testimony presented to them and the court as a whole may be carefully constructed. CAI has had to negotiate a series of contestations before it could have any actual impact on the practices of forensic scientists and their clients, although CAI principles have now been formally adopted by all forensic science providers in the UK and Ireland. We have, however, been able to highlight critical perspectives that previously had been overlooked. By doing so, we showed how the CAI revealed a series of fissures in institutionalized attitudes about the role of forensic scientists, the commercialization of that domain, the disciplinary identity of forensic science, and the nature of the scientific method as a whole. The developers of CAI had to contend with these fissures, and indeed dissenting voices can still be heard. In this way, the CAI exposes the extent to which the technosocial networks that constitute modern criminal investigative practice are characterized by division and contradiction as well as by a sense of common mission. The imposition of the CAI within these networks serves to make visible these dynamics, and highlights the strongly embedded nature of these differing attitudes to forensic science.

Conclusion In this paper we argued that an account of the changing circumstances in which UK forensic science is practiced is necessary for understanding the development of the CAI method. In providing a system that shapes and organizes forensic inquiry, CAI contributes to the construction of a new disciplinary identity for forensic science, supposedly bringing it closer to the epistemic standards common to ‘pure’ scientific disciplines. In addition, its attractiveness to key forensic stakeholders is supposedly enhanced by its compatibility with the UK Government’s liberalizing rationalities, regarding the management of epistemic risk and the marketization of its provision. Insofar as CAI reflects a new, commercially influenced orientation to the disciplining of forensic science, it also reconfigures the relationship between forensic science providers and their customers – especially the police. Taking on board Bruno Latour’s

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(1996) arguments concerning the need to look for the hybrid entities submerged beneath constructions of the ‘scientific’ and the ‘social’, CAI can be regarded as just such a hybrid, reflecting the interests of criminal justice, but also (and more importantly), those of a neoliberal rendering of scientific practice sensitive to an emerging marketplace whose commercial exchanges it seeks to shape. At the same time, however, attempts to apply this regulatory model expose the fragility of its construction. To use Latour and Woolgar’s (1979) term, the application of CAI threatens to open up a series of ‘black boxes’ within an extended network that constitutes the wider criminal justice system. In this case, the ‘black boxes’ relate to particular understandings of ‘science’ circulating within this configuration of actors, encompassing police, commercial organizations and the judicial apparatus. By challenging these understandings, and opening up spaces of controversy, the adoption of CAI encourages debates about the identity of forensic science, and its position in relation to other elements in the criminal justice system. Forensic science practice already is a highly contested terrain, in which marketization represents a further challenge. The contested claims about CAI stand at the centre of this terrain, and our examination of its origins and uses demonstrates how actors continue to exchange versions of what constitutes ‘science’ and argue about the role it plays in law enforcement. Furthermore, these debates are also informed by differing versions of the significance of commercial imperatives in the shaping of science in this legal context. Thus, a focus on the CAI method reveals how notions of science and commerciality are simultaneously co-constructed by those who produce and consume practically relevant instances of forensic science knowledge. This in itself questions the claim that science is constituted by a set of core values, but it also suggests that no single response to market mechanisms will be sufficient to shape scientific practice. Finally, while the marketization of forensic science practice in England and Wales has been achieved in theory, its realization has been interpreted in various ways by key actors in criminal justice institutions. An ongoing contest remains in force between state authorities and other actors over how forensic science is being, and should be, liberalized; a contest that increasingly is being shaped by exogeneous developments. While state authorities have attempted to construct a particular commercialized arrangement for the provision of scientific services, other actors (including forensic science organisations and individual police forces) have used the new market freedoms to create new spaces in which a particular form of science is (re)created and managed in novel ways. The history of CAI is an instantiation of these wider developments. Notes 1. For further examples, see Halfon (1998) and Gerlach (2004). 2. The development of probabilistic methods for fingerprint analysis has now been officially recognized as a priority (Home Office, 2009). 3. List of interviewees: Interviewee A: CAI author (Interviewed on two separate occasions, 2006 (A1), 2007 (A2)) Interviewee B: CAI User Interviewee C: US developer of Bayesian expert systems Interviewee D: Forensic Scientist at in-house police lab.

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Interivewee E: Forensic Science Consultant Interviewee F: Policing Researcher 4. A step prior to the hierarchy of propositions is the hierarchy of issues, in which the issues considered pertinent to a case are defined (Jackson, 2009). 5. Another Level, sub-source, has been proposed to address the issue of whether DNA evidence was recovered from blood, semen or other material, an issue which may vary in importance depending on the nature of the crime, or other circumstances surrounding the incident (Jackson, 2009).

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Biographical notes Christopher Lawless is a Postdoctoral Research Fellow at the Centre for Analysis of Risk and Regulation (CARR) at the London School of Economics and Political Science. Robin Williams is Professor of Forensic Science Studies at Northumbria University and Emeritus Professor of Sociology at Durham University.