Disciplining deliberation: a sociotechnical perspective on machine learning trade-offs
<|reference_start|>Disciplining deliberation: a sociotechnical perspective on machine learning trade-offs: This paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this prevalent interpretation by drawing attention to three sets of considerations that are critical for bridging the gap between these formal trade-offs and their practical impacts on relevant values. I show how neglecting these considerations can distort our normative deliberations, and result in costly and misaligned interventions and justifications. Taken together, these considerations form a sociotechnical framework that could guide those involved in AI governance to assess how, in many cases, we can and should have higher aspirations than the prevalent interpretation of the trade-offs would suggest. I end by drawing out the normative opportunities and challenges that emerge out of these considerations, and highlighting the imperative of interdisciplinary collaboration in fostering responsible AI.<|reference_end|>
@article{fazelpour2024disciplining,
    title={Disciplining deliberation: a sociotechnical perspective on machine
  learning trade-offs},
    author={Sina Fazelpour},
    journal={arXiv preprint arXiv:2403.04226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.04226},
    primaryClass={cs.CY}
}