Strategic Evaluation: Subjects, Evaluators, and Society
Benjamin Laufer, Jon Kleinberg, Karen Levy, Helen Nissenbaum

TL;DR
This paper models the strategic interactions between decision subjects, evaluators, and society in algorithmic evaluations, emphasizing how evaluation design influences behavior and societal outcomes.
Contribution
It introduces a novel model of evaluation as a strategic interaction among three agents, highlighting the influence of institutional incentives on behavior and societal goals.
Findings
Evaluation design can incentivize strategic behavior.
Strategic evaluation impacts societal trust and fairness.
Model applies to various social systems with strategic actors.
Abstract
A broad current application of algorithms is in formal and quantitative measures of murky concepts -- like merit -- to make decisions. When people strategically respond to these sorts of evaluations in order to gain favorable decision outcomes, their behavior can be subjected to moral judgments. They may be described as 'gaming the system' or 'cheating,' or (in other cases) investing 'honest effort' or 'improving.' Machine learning literature on strategic behavior has tried to describe these dynamics by emphasizing the efforts expended by decision subjects hoping to obtain a more favorable assessment -- some works offer ways to preempt or prevent such manipulations, some differentiate 'gaming' from 'improvement' behavior, while others aim to measure the effort burden or disparate effects of classification systems. We begin from a different starting point: that the design of an…
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