English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Despite an increasing reliance on fully-automated algorithmic decision-makingin our day-to-day lives, human beings still make highly consequentialdecisions. As frequently seen in business, healthcare, and public policy,recommendations produced by algorithms are provided to human decision-makers toguide their decisions. While there exists a fast-growing literature evaluatingthe bias and fairness of such algorithmic recommendations, an overlookedquestion is whether they help humans make better decisions. We develop astatistical methodology for experimentally evaluating the causal impacts ofalgorithmic recommendations on human decisions. We also show how to examinewhether algorithmic recommendations improve the fairness of human decisions andderive the optimal decision rules under various settings. We apply the proposedmethodology to preliminary data from the first-ever randomized controlled trialthat evaluates the pretrial Public Safety Assessment (PSA) in the criminaljustice system. A goal of the PSA is to help judges decide which arrestedindividuals 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 thejudge's decisions and subsequent arrestee behavior. However, our analysisyields some potentially suggestive evidence that the PSA may help avoidunnecessarily harsh decisions for female arrestees regardless of their risklevels while it encourages the judge to make stricter decisions for malearrestees who are deemed to be risky. In terms of fairness, the PSA appears toincrease the gender bias against males while having little effect on anyexisting racial differences in judges' decision. Finally, we find that thePSA's recommendations might be unnecessarily severe unless the cost of a newcrime is sufficiently high.

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