Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making

論文URL:http://dl.acm.org/citation.cfm?doid=3173574.3174014

論文アブストラクト:Calls for heightened consideration of fairness and accountability in algorithmically-informed public decisions-like taxation, justice, and child protection-are now commonplace. How might designers support such human values? We interviewed 27 public sector machine learning practitioners across 5 OECD countries regarding challenges understanding and imbuing public values into their work. The results suggest a disconnect between organisational and institutional realities, constraints and needs, and those addressed by current research into usable, transparent and 'discrimination-aware' machine learning-absences likely to undermine practical initiatives unless addressed. We see design opportunities in this disconnect, such as in supporting the tracking of concept drift in secondary data sources, and in building usable transparency tools to identify risks and incorporate domain knowledge, aimed both at managers and at the 'street-level bureaucrats' on the frontlines of public service. We conclude by outlining ethical challenges and future directions for collaboration in these high-stakes applications.

日本語のまとめ:

公共の意思決定のアルゴリズムでの支援につき、デザイナーが考慮すべき観点を5国27名の公共セクター機械学習実践者へIVで調査。データ収集目的とモデリング目的が異なり、モデルの説明責任が追えないなど5つの倫理的問題を挙げる。

(110文字)

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