[论文解读] Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment

Kosuke Imai, Zhichao Jiang+3 more|arXiv (Cornell University)|Dec 4, 2020

Ethics and Social Impacts of AI被引用 9

一句话总结

本文開發了一套因果推斷框架，用於評估演算法輔助人類決策的影響，並以預防性公共安全評估（Pretrial Public Safety Assessment, PSA）在司法保釋決策中的隨機對照試驗為例。研究發現，PSA建議對法官決策的整體影響有限，可能使低風險女性獲得較輕處罰，但卻使高風險男性受到更嚴厲對待；除非再犯成本極高，否則PSA建議往往過於嚴苛，遠遠超出最佳決策規則。

ABSTRACT

Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers to guide their decisions. While there exists a fast-growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions. We develop a statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to preliminary data from the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment (PSA) in the criminal justice system. A goal of the PSA is to help judges decide which arrested individuals should be released. On the basis of the preliminary data available, we find that providing the PSA to the judge has little overall impact on the judge's decisions and subsequent arrestee behavior. However, our analysis yields some potentially suggestive evidence that the PSA may help avoid unnecessarily harsh decisions for female arrestees regardless of their risk levels while it encourages the judge to make stricter decisions for male arrestees who are deemed to be risky. In terms of fairness, the PSA appears to increase the gender bias against males while having little effect on any existing racial differences in judges' decision. Finally, we find that the PSA's recommendations might be unnecessarily severe unless the cost of a new crime is sufficiently high.

研究动机与目标

• 開發一套通用的統計方法，用於評估演算法建議對人類決策的因果影響。
• 評估演算法建議是否能提升高風險領域（如刑事司法）中決策品質與公平性。
• 在不同負面結果與過度嚴苛成本結構下，識別最佳決策規則。
• 探討演算法建議如何與人類決策偏誤互動，特別是按性別與種族區分。
• 提供一個實驗評估政策相關領域中混合人機系統的框架。

提出的方法

• 使用因果推斷中的主要群體分層方法定義評估目標，考慮實驗環境中的遵守行為與潛在干擾。
• 採用隨機對照試驗設計，將法官隨機分配至接收或不接收PSA風險分數與DMF建議的組別。
• 運用決策理論的效用框架，比較不同成本加權結果下法官決策與演算法建議的表現。
• 推導潛在結果下因果效應的識別條件，允許對無法驗證的假設進行敏感度分析。
• 估算法官決策與演算法建議在多項結果（如新犯罪、未出庭）上的期望效用差異。
• 進行敏感度分析，評估發現對無偏性與單調性假設違反的穩健性。

实验结果

研究问题

• RQ1 向法官提供PSA是否顯著改變其保釋決策？
• RQ2 演算法建議是否能提升司法決策的公平性，特別是按性別與種族區分？
• RQ3 在不同成本結構下，DMF系統的演算法建議是否優於實際法官決策？
• RQ4 法官在多大程度上偏離演算法建議，這種偏離如何影響決策品質？
• RQ5 再犯成本與過度監禁成本如何影響最佳決策規則？

主要发现

• 提供PSA分數對法官保釋決策整體影響極小，顯示對司法行為影響有限。
• 有初步證據顯示，PSA使用可能使所有風險層級的女性被告獲得更寬鬆的處罰，但卻使高風險男性被告受到更嚴厲對待。
• PSA似乎加劇了司法決策中已有的性別差異，特別是增加了對男性被告的嚴厲程度，但未觀察到顯著的種族差異。
• 在絕大多數情況下，除非再犯罪成本遠高於過度監禁成本，否則最佳決策應為簽署擔保而非現金擔保。
• 在所有結果中，法官決策的期望效用始終高於DMF建議，顯示演算法建議可能過於嚴苛。
• DMF建議資訊量僅略高，因為當建議為現金擔保與非現金擔保時，最佳決策比例差異極小。