rule society and our lives (R0ssaak, 2016, p. 34). Compared to human processing, computational algorithms have many advantages since they are much faster, can deal with more complexity and are more accurate than humans will ever be. The downside of this computational processing is that these systems rely on processes and abilities 'that are radically beyond what is possible for human beings to understand' (Danaher, 2016, p. 247). They are black boxes and that is what gives rise to many concerns, because we do not understand how these algorithms operate as the new power brokers in society (Diakopoulos, 2014, p. 2). Critics like Evgeny Morozov and Cathy O'Neil stress that algorithms are constructed models, based on choices what to include and what to leave out. And these choices 'are not just about logistics, profits and efficiency. They are fundamental moral' (O'Neil, 2016, p. 218; Morozov, 2014, p. 182-186). John Danaher warns that the increasingly reliance on algorithms in decision making processes might turn society in an 'algocracy', a governance system in which computer-programmed algorithms are used 'to collect, collate and organise the data upon which decisions are typically made, and to assist in how data is processed and communicated through the relevant governance system' (Danaher, 2016, p. 247). While in a bureaucracy laws and regulations structure and enforce how humans act, in an 'algocracy' the algorithms are the structuring and constraining components. Janssen and Kuk emphasise that algorithms do not work on their own, but form an 'algorithmic materiality', which means that there is an intricate relationality between algorithm, systems, data and humans resulting in a dynamic and 'complex socio-technical ensemble of people, technologies, code developers and designers' (Janssen Kuk, 2016, p. 274-275), which is very similar to the archive as 'the apparatus through which we map the everyday' (Giannachi, 2016, p. xv). A few societal examples discussed There are good reasons to worry about this emerging 'algorithmic governmentality' as some scholars label this data-driven exercise of power and policymaking (Thomas Berns, 2013; Rodrigues, 2016). Before discussing the archival implications of these socio-technical developments, I want to review some examples. Real time processing of large quantities of data from criminal records, police databases and surveillance data to predict where criminal activities are likely to happen (predictive policing) has already been put into practice in several countries (Joh, 2015). Even in the courtroom computational algorithmic support has been introduced to underpin court decisions. The independent non-profit organisation of investigative journalism ProPublica, recently published a series of critical articles on the accurateness of algorithms used in courtrooms to assess the likelihood of recidivism of defendants. ProPublica journalists analysed the accuracy of a widely-used risk assessment tool named COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) by investigating 10,000 criminal defendants in Florida and compared their predicted recidivism rates with the actual rates. The researchers found out 'that black defendants were far more likely than white defendants to be incorrectly judged to be at a higher risk of recidivism, while white defendants were more likely than black defendants to be incorrectly flagged as low risk'.4 Although the Supreme Court of Wisconsin expressed its concern about this race correlation in COMPAS, in an appeal from an order of a circuit court it judged that the evidence- based risk assessment tool COMPAS can be used at sentencing.5 In the explanation of its decision, the Supreme Court circumscribed its use by stressing that risk scores are 'not intended to determine the severity of the sentence or whether an offender is incarcerated' and that risk scores 'may not be considered as the determinative factor in deciding whether the offender can be supervised safely and effectively in the community'. Although the Supreme Court agreed that the defendant-appellant was not able to review and challenge how the COMPAS algorithm - which is part of the trade secret of the developer Northpointe Inc. - calculates risk, the order judged the ability to review and challenge the resulting risk scores as satisfactory.6 Interestingly, one of the judges, Shirley S. Abrahamson wrote a separate consideration in which she emphasised the relevance of recording the use of risk assessment tools. Precisely because scholars were critical on using these risk assessment tools in sentencing, courts should 'evaluate on the records the strengths, weaknesses, and relevance to the individualized sentence being rendered of the evidence based tool (or, more precisely, the research-based or data-based tool)'. Abrahamson recognised that this might be an extra demand on and administrative burden for the circuit courts, 'but making a record, including a record explaining consideration of the evidence based tools and the limitations and strengths thereof, is part of the long-standing basic requirement that a circuit court explain its exercise of discretion at sentencing'.7 We need to question how the record is defined if the judges accept that algorithms can be used in sentencing although the algorithm itself, the lens through which the data are filtered, sorted etc., remains a black box because of the mentioned trade secret. The record-making as defined by the Supreme Court has to do with accountability of how the judges use the tools in the process of sentencing, not with the processing activity of the algorithms themselves. This appeal clearly shows the limitations of the traditional scope of the concept of the record. If the informational algorithm remains a closed black-box in cases with far-reaching consequences for citizens (even if the outcomes can only be used as additional information for decisions) the claim that records provide the best means for warranting accountability is severely affected. The ever-increasing interrelationship between man and technology requires a clearer notion of the scope of the record, especially when a relation is made to accountability of decision-making. There are good reasons to redefine the scope of the record in that tight relationship between humans and machines. I agree with Amelia Acker, who argues that examining the infrastructure of records, 'archivists can think big enough about the "black box" and all the layers of construction behind digital records and emerging documentation practices' (Acker, 2016, p. 294-295). One of these layers of construction are the algorithms that are used in the processing of data. The use of 'black-box' algorithms in decision-making processes will be mirrored in the records that are created, and it is not without consequence to the attributed quality of the records as means of accountability. It is imaginable that for some decision-making processes (which immediately shows an additional selection perspective) open and understandable algorithms are required. That is for instance the motive of a motion for the European archives in liquid times 202 charles jeurgens threats of the data-flood. an accountability perspective in the era of ubiquitous computing. 4 accessed at 30 March 2017 5 Supreme Court of Wisconsin, State of Wisconsin versus Eric L. Loomis on certification from the court of appeals, 13 July 2016, available at < pdf?content=pdf&seqNo=171690> accessed at 30 March 2017. 6 Ibid., par. 53. 7 Ibid., par. 141 203

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Jaarboeken Stichting Archiefpublicaties | 2017 | | pagina 103