“The Computer Said So”: On the Ethics, Effectiveness, and Cultural Techniques of Predictive Policing

Karppi, Tero

doi:10.1177/2056305118768296

Cited by 32 publications

(19 citation statements)

References 20 publications

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“…It seems highly unlikely that border guards would have significantly longer to assess the results of an automated system. Furthermore, based on conversations with individuals knowledgeable about the matter, it seems highly unlikely that a police officer would not follow any individual leads provided by the automated system (Karppi, ). This is in part because police forces use individual searches to calibrate the system and ensure that they are targeting the right groups or individuals.…”

Section: Keeping a “Human In The Loop”? Three Casesmentioning

confidence: 99%

Liable, but Not in Control? Ensuring Meaningful Human Agency in Automated Decision‐Making Systems

Wagner¹

2019

Policy & Internet

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Automated decision making is becoming the norm across large parts of society, which raises interesting liability challenges when human control over technical systems becomes increasingly limited. This article defines “quasi‐automation” as inclusion of humans as a basic rubber‐stamping mechanism in an otherwise completely automated decision‐making system. Three cases of quasi‐automation are examined, where human agency in decision making is currently debatable: self‐driving cars, border searches based on passenger name records, and content moderation on social media. While there are specific regulatory mechanisms for purely automated decision making, these regulatory mechanisms do not apply if human beings are (rubber‐stamping) automated decisions. More broadly, most regulatory mechanisms follow a pattern of binary liability in attempting to regulate human or machine agency, rather than looking to regulate both. This results in regulatory gray areas where the regulatory mechanisms do not apply, harming human rights by preventing meaningful liability for socio‐technical decision making. The article concludes by proposing criteria to ensure meaningful agency when humans are included in automated decision‐making systems, and relates this to the ongoing debate on enabling human rights in Internet infrastructure.

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Section: Keeping a “Human In The Loop”? Three Casesmentioning

confidence: 99%

Liable, but Not in Control? Ensuring Meaningful Human Agency in Automated Decision‐Making Systems

Wagner¹

2019

Policy & Internet

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“…Research focusing on inconclusive evidence refers to the way in which non-deterministic, ML algorithms produce outputs that are expressed in probabilistic terms (James et al 2013;Valiant 1984). These types of algorithms generally identify association and correlation between variables in the underlying data, but not causal connections.…”

Section: Inconclusive Evidence Leading To Unjustified Actionsmentioning

confidence: 99%

The ethics of algorithms: key problems and solutions

et al. 2021

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Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016 (Mittelstadt et al. Big Data Soc 3(2), 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms.

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“…Fairness could be further hampered by the combined use of this algorithm with others driving decisions on neighbourhood police patrolling. The fact these algorithms may be prone to drive further patrolling in poor neighbourhoods may result from a training bias as crimes occurring in public tend to be more frequently reported (Karppi, 2018). One can easily understand how these algorithms may jointly produce a vicious cycle-more patrolling would lead to more arrests that would worsen the neighbourhood average recidivism-risk score, which would in turn trigger more patrolling.…”

Section: Guidelines and Secondary Literature On Ai Ethics Its Dimensmentioning

confidence: 99%

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

Piano

2020

Humanit Soc Sci Commun

148

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Decision-making on numerous aspects of our daily lives is being outsourced to machinelearning (ML) algorithms and artificial intelligence (AI), motivated by speed and efficiency in the decision process. ML approaches-one of the typologies of algorithms underpinning artificial intelligence-are typically developed as black boxes. The implication is that ML code scripts are rarely scrutinised; interpretability is usually sacrificed in favour of usability and effectiveness. Room for improvement in practices associated with programme development have also been flagged along other dimensions, including inter alia fairness, accuracy, accountability, and transparency. In this contribution, the production of guidelines and dedicated documents around these themes is discussed. The following applications of AIdriven decision-making are outlined: (a) risk assessment in the criminal justice system, and (b) autonomous vehicles, highlighting points of friction across ethical principles. Possible ways forward towards the implementation of governance on AI are finally examined.

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“The Computer Said So”: On the Ethics, Effectiveness, and Cultural Techniques of Predictive Policing

Cited by 32 publications

References 20 publications

Liable, but Not in Control? Ensuring Meaningful Human Agency in Automated Decision‐Making Systems

Liable, but Not in Control? Ensuring Meaningful Human Agency in Automated Decision‐Making Systems

The ethics of algorithms: key problems and solutions

Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward

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