AMS-Algorithmen_Bericht_final.pdf

Allhutter, Ita Doris; Mager, Ita Astrid

doi:10.1553/ita-pb-2020-02

Cited by 4 publications

References 13 publications

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Procedural fairness in algorithmic decision-making: the role of public engagement

Decker,

Wegner,

Leicht-Scholten

2024

Ethics Inf Technol

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Despite the widespread use of automated decision-making (ADM) systems, they are often developed without involving the public or those directly affected, leading to concerns about systematic biases that may perpetuate structural injustices. Existing formal fairness approaches primarily focus on statistical outcomes across demographic groups or individual fairness, yet these methods reveal ambiguities and limitations in addressing fairness comprehensively. This paper argues for a holistic approach to algorithmic fairness that integrates procedural fairness, considering both decision-making processes and their outcomes. Procedural fairness emphasizes the importance of fair decision-making procedures, which aligns with theories of relational justice that stress the quality of social relations and power dynamics. We highlight the need for substantive procedural fairness to ensure better outcomes and address forward-looking responsibilities. Additionally, we propose leveraging Public Engagement, a core dimension within the well-established Responsible Research and Innovation framework, to enhance procedural fairness in ADM systems. Our contribution underscores the value of Public Engagement in fostering fairer ADM processes, thereby expanding the current focus beyond technical outcome-based approaches to encompass broader procedural considerations.

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Procedural fairness in algorithmic decision-making: the role of public engagement

Decker,

Wegner,

Leicht-Scholten

2024

Ethics Inf Technol

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Rethinking Transparency as a Communicative Constellation

Eyert

Lopez

2023

2023 ACM Conference on Fairness, Accountability, and Transparency

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When Small Decisions Have Big Impact: Fairness Implications of Algorithmic Profiling Schemes

Kern,

Bach,

Mautner

et al. 2024

ACM J. Responsib. Comput.

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Algorithmic profiling is increasingly used in the public sector with the hope of allocating limited public resources more effectively and objectively. One example is the prediction-based profiling of job seekers to guide the allocation of support measures by public employment services. However, empirical evaluations of potential side-effects such as unintended discrimination and fairness concerns are rare in this context. We systematically compare and evaluate statistical models for predicting job seekers’ risk of becoming long-term unemployed concerning subgroup prediction performance, fairness metrics, and vulnerabilities to data analysis decisions. Focusing on Germany as a use case, we evaluate profiling models under realistic conditions using large-scale administrative data. We show that despite achieving high prediction performance on average, profiling models can be considerably less accurate for vulnerable social subgroups. In this setting, different classification policies can have very different fairness implications. We therefore call for rigorous auditing processes before such models are put to practice.

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AMS-Algorithmen_Bericht_final.pdf

Cited by 4 publications

References 13 publications

Procedural fairness in algorithmic decision-making: the role of public engagement

Procedural fairness in algorithmic decision-making: the role of public engagement

Rethinking Transparency as a Communicative Constellation

When Small Decisions Have Big Impact: Fairness Implications of Algorithmic Profiling Schemes

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