On Chances and Risks of Security Related Algorithmic Decision Making Systems

Zweig, Katharina Anna; Wenzelburger, Georg; Krafft, Tobias D.

doi:10.1007/s41125-018-0031-2

Cited by 25 publications

(13 citation statements)

References 39 publications

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“…Today, ADM systems are used in different contexts and support bureaucrats when detecting tax fraud (Botelho and Antunes 2011), assigning future students to universities (Grenet 2018;van Zanten and Legavre 2014), matching job seekers to training schemes (Desiere et al 2019;Fröhlich and Spiecker 2019) or calculating the risk of reoffending for early release from a prison sentence (Berk 2017;Berk et al 2017). While many public administration scholars emphasize the chances that big data and automated pattern detection entails and see the bureaucracy on the path toward "digital era governance" (Margetts and Dunleavy 2013), critical voices emphasize the lack of transparency and accountability of algorithms (e.g., Mittelstadt et al 2016;Ananny and Crawford 2018;Zweig et al 2018). These studies maintain that ADM systems may even produce biased decisions-not only because they incorporate certain values (which may be biased, e.g., Hildebrandt 2016;Yeung 2018), but also because they learn from input data and reproduce the biases found in this data, e.g., concerning ethnic or gender inequalities (Barocas and Selbst 2016;Lepri et al 2018).…”

Section: Algorithms and Public Administration: From The Laboratory To Messy Realitymentioning

confidence: 99%

Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA

Hartmann

Wenzelburger

2021

Policy Sci

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Algorithms are increasingly used in different domains of public policy. They help humans to profile unemployed, support administrations to detect tax fraud and give recidivism risk scores that judges or criminal justice managers take into account when they make bail decisions. In recent years, critics have increasingly pointed to ethical challenges of these tools and emphasized problems of discrimination, opaqueness or accountability, and computer scientists have proposed technical solutions to these issues. In contrast to these important debates, the literature on how these tools are implemented in the actual everyday decision-making process has remained cursory. This is problematic because the consequences of ADM systems are at least as dependent on the implementation in an actual decision-making context as on their technical features. In this study, we show how the introduction of risk assessment tools in the criminal justice sector on the local level in the USA has deeply transformed the decision-making process. We argue that this is mainly due to the fact that the evidence generated by the algorithm introduces a notion of statistical prediction to a situation which was dominated by fundamental uncertainty about the outcome before. While this expectation is supported by the case study evidence, the possibility to shift blame to the algorithm does seem much less important to the criminal justice actors.

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Section: Algorithms and Public Administration: From The Laboratory To Messy Realitymentioning

confidence: 99%

Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA

Hartmann

Wenzelburger

2021

Policy Sci

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“…By using ML algorithms to forecast crime, predictive models are not constructed according to a specific theory but directly as a result of analysing data with regard to patterns and correlations. Based on an iterative learning, testing and feedback process, the algorithm adjusts the predictive model until a desired predictive quality is reached (Zweig et al., 2018). Ideally, there is no need for any kind of human-made predictive modelling informed by subject-matter theories (Amoore and Raley, 2017: 4).…”

Section: Implications For the Constitution Of Predictive Knowledgementioning

confidence: 99%

Epistemologies of predictive policing: Mathematical social science, social physics and machine learning

Hälterlein

2021

Big Data & Society

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Predictive policing has become a new panacea for crime prevention. However, we still know too little about the performance of computational methods in the context of predictive policing. The paper provides a detailed analysis of existing approaches to algorithmic crime forecasting. First, it is explained how predictive policing makes use of predictive models to generate crime forecasts. Afterwards, three epistemologies of predictive policing are distinguished: mathematical social science, social physics and machine learning. Finally, it is shown that these epistemologies have significant implications for the constitution of predictive knowledge in terms of its genesis, scope, intelligibility and accessibility. It is the different ways future crimes are rendered knowledgeable in order to act upon them that reaffirm or reconfigure the status of criminological knowledge within the criminal justice system, direct the attention of law enforcement agencies to particular types of crimes and criminals and blank out others, satisfy the claim for the meaningfulness of predictions or break with it and allow professionals to understand the algorithmic systems they shall rely on or turn them into a black box. By distinguishing epistemologies and analysing their implications, this analysis provides insight into the techno-scientific foundations of predictive policing and enables us to critically engage with the socio-technical practices of algorithmic crime forecasting.

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“…Accountability and transparency are essential properties in the new era of artificial intelligence, big data, and digitalization. Not only are these properties necessary for algorithms used in the name of security and public policy, but these are increasingly seen also as crucial for sustaining the very foundations of democratic political systems (Zweig et al 2018). Following this reasoning, the basic Premise P 1 from Fig.…”

Section: Discussionmentioning

confidence: 99%

An Acid Test for Europeanization: Public Cyber Security Procurement in the European Union

Ruohonen

2019

Eur J Secur Res

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Public procurement refers to processes through which national, regional, and local public authorities, state-owned enterprises, or other related bodies governed by public law, purchase products, services, and public work. Such purchases have been a particularly important element in developing the Internal Market of the European Union (EU). Given recent procurement reforms in the EU, including the 2009 reform on defense procurement, this paper examines public cyber security procurement in Europe. Two questions are examined: (1) whether cyber security procurement differs from public procurement in general, and (2) whether there are any noteworthy signs of Europeanization in terms of cyber security procurement. According to the empirical results, cyber security procurement tends to differ from general public procurement. In particular, competition obstacles are visible in terms of bids for cyber security procurement tenders. This result is accompanied with a visible lack of Europeanization, although the same observation generalizes to public procurement in the EU generally. With these results and the accompanying discussion, the paper contributes to the recent lively discussion about European security and its relation to marketization.

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On Chances and Risks of Security Related Algorithmic Decision Making Systems

Cited by 25 publications

References 39 publications

Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA

Uncertainty, risk and the use of algorithms in policy decisions: a case study on criminal justice in the USA

Epistemologies of predictive policing: Mathematical social science, social physics and machine learning

An Acid Test for Europeanization: Public Cyber Security Procurement in the European Union

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