Kosuke Imai presents "Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment"

Presentation Date: 

Wednesday, December 2, 2020

Location: 

https://harvard.zoom.us/j/99424949004?pwd=aWtPNFM3ZzFYbWxIMXNoZDlyUElVZz09
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 in order 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 decisions under various settings.  We apply the proposed methodology to 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.  We find that the PSA provision has little overall impact on the judge’s decisions and subsequent arrestee behavior.  However, our analysis suggests 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 the existing racial biases of the judge’s decisions against non-white males.  Finally, we show that PSA’s recommendations are often too severe and can only be justified if the societal cost of a new crime is much higher than the cost of an unnecessarily harsh decision.
 
See also: 2020