Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of 
Automated Decision-Making

Sele, Daniela; Chugunova, Marina

doi:10.1371/journal.pone.0298037

アイテム詳細

登録内容を編集ファイル形式で保存

一時保存へ追加

タグ情報を表示リリース履歴を表示詳細要約

公開

学術論文

Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making

MPS-Authors

/persons/resource/persons226995

Chugunova, Marina
MPI for Innovation and Competition, Max Planck Society;

External Resource

https://ssrn.com/abstract=4285645
(プレプリント)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

フルテキスト (公開)

公開されているフルテキストはありません

付随資料 (公開)

There is no public supplementary material available

引用

Sele, D., & Chugunova, M. (2024). Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making. PLOS ONE, 19(2):. doi:10.1371/journal.pone.0298037.

引用: https://hdl.handle.net/21.11116/0000-000F-E48C-2

要旨

Automated decision-making gains traction, prompting discussions on regulation with calls for human oversight. Understanding how human involvement affects the acceptance of algorithmic recommendations and the accuracy of resulting decisions is vital. In an online experiment (N = 292), for a prediction task, participants choose a recommendation stemming either from an algorithm or another participant. In a between-subject design, we varied if the prediction was delegated completely or if the recommendation could be adjusted. 66% of times, participants preferred to delegate the decision to an algorithm over an equally accurate human. The preference for an algorithm increased by 7 percentage points if participants could monitor and adjust the recommendations. Participants followed algorithmic recommendations more closely. Importantly, they were less likely to intervene with the least accurate recommendations. Hence, in our experiment the human-in-the-loop design increases the uptake but decreases the accuracy of the decisions.