Spotting When Algorithms Are Wrong

Buijsman, Stefan; Veluwenkamp, Herman

doi:10.1007/s11023-022-09591-0

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…7 Another famous example includes the fraud detection system “SyRI” in the Netherlands, which collected large amounts of personal data. SyRI was challenged by civil rights organizations in the District Court of The Hague, which then ruled that the technology violates the right to privacy (van Bekkum and Borgesius 2021 ; Buijsman and Veluwenkamp 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Barysė

Sarel

2023

Artif Intell Law

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Artificial intelligence plays an increasingly important role in legal disputes, influencing not only the reality outside the court but also the judicial decision-making process itself. While it is clear why judges may generally benefit from technology as a tool for reducing effort costs or increasing accuracy, the presence of technology in the judicial process may also affect the public perception of the courts. In particular, if individuals are averse to adjudication that involves a high degree of automation, particularly given fairness concerns, then judicial technology may yield lower benefits than expected. However, the degree of aversion may well depend on how technology is used, i.e., on the timing and strength of judicial reliance on algorithms. Using an exploratory survey, we investigate whether the stage in which judges turn to algorithms for assistance matters for individual beliefs about the fairness of case outcomes. Specifically, we elicit beliefs about the use of algorithms in four different stages of adjudication: (i) information acquisition, (ii) information analysis, (iii) decision selection, and (iv) decision implementation. Our analysis indicates that individuals generally perceive the use of algorithms as fairer in the information acquisition stage than in other stages. However, individuals with a legal profession also perceive automation in the decision implementation stage as less fair compared to other individuals. Our findings, hence, suggest that individuals do care about how and when algorithms are used in the courts.

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Section: Introductionmentioning

confidence: 99%

Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Barysė

Sarel

2023

Artif Intell Law

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Managing Artificial Intelligence Algorithmic Discrimination: The Internal Audit Function Role

Nzama-Sithole

2023

Accounting, Finance, Sustainability, Governance &Amp; Fraud: Theory and Application

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Transparency for AI systems: a value-based approach

Buijsman

2024

Ethics Inf Technol

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With the widespread use of artificial intelligence, it becomes crucial to provide information about these systems and how they are used. Governments aim to disclose their use of algorithms to establish legitimacy and the EU AI Act mandates forms of transparency for all high-risk and limited-risk systems. Yet, what should the standards for transparency be? What information is needed to show to a wide public that a certain system can be used legitimately and responsibly? I argue that process-based approaches fail to satisfy, as knowledge about the development process is insufficient to predict the properties of the resulting system. Current outcome-based approaches [Mitchell et al., 2019; Loi et al., 2021] are also criticized for a lack of attention to the broader socio-technical system and failure to account for empirical results that show that people care about more than just the outcomes of a process [as reported by Meyerson et al. (Procedural justice and relational theory: Empirical, philosophical, and legal perspectives, Taylor & Francis, 2021)]. Instead, I propose value-based transparency, on which the information we need to provide is what values have been considered in the design and how successful these have been realized in the final system. This can handle the objections to other frameworks, matches with current best practices on the design of responsible AI and provides the public with information on the crucial aspects of a system’s design.

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Spotting When Algorithms Are Wrong

Cited by 6 publications

References 39 publications

Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Algorithms in the court: does it matter which part of the judicial decision-making is automated?

Managing Artificial Intelligence Algorithmic Discrimination: The Internal Audit Function Role

Transparency for AI systems: a value-based approach

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