A Qualitative Exploration of Perceptions of Algorithmic Fairness

論文URL:http://dl.acm.org/citation.cfm?doid=3173574.3174230

論文アブストラクト:Algorithmic systems increasingly shape information people are exposed to as well as influence decisions about employment, finances, and other opportunities. In some cases, algorithmic systems may be more or less favorable to certain groups or individuals, sparking substantial discussion of algorithmic fairness in public policy circles, academia, and the press. We broaden this discussion by exploring how members of potentially affected communities feel about algorithmic fairness. We conducted workshops and interviews with 44 participants from several populations traditionally marginalized by categories of race or class in the United States. While the concept of algorithmic fairness was largely unfamiliar, learning about algorithmic (un)fairness elicited negative feelings that connect to current national discussions about racial injustice and economic inequality. In addition to their concerns about potential harms to themselves and society, participants also indicated that algorithmic fairness (or lack thereof) could substantially affect their trust in a company or product.

日本語のまとめ:

アルゴリズミックフェアネスに関する米国の少数派(世帯収入・人種・教育等で)の印象を調査。アルゴリズムの不公平性がユーザーに社会問題を喚起させる、企業がアルゴリズムの公平さを失うと企業の信用に関わるというデータが得られた。

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