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\newcommand{\ourtitle}{Evaluating Decision Makers over Selectively Labeled Data}

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\title{\ourtitle}

\author{Michael Mathioudakis}
\affiliation{%
  \institution{University of Helsinki}
  \city{Helsinki} 
  \country{Finland} 
}
\email{michael.mathioudakis@helsinki.fi}


\begin{abstract}
Today, AI systems replace humans in an increasing number of decisions affecting people's lives.
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Therefore, it is important to evaluate the performance of such systems {\it offline}, i.e., before they are deployed in real settings --
and compare it to the performance of human decisions they aim to replace.
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One major challenge in such cases is that often past decisions have skewed the data on which the evaluation is performed. 
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For example, when a bank decides whether a customer should be granted a loan, it is desired to grant loans to customers who would honor its conditions, but not to ones who would violate them.
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However, we can directly evaluate only the decision to grant the loan, while we cannot observe whether customers who were not granted the loan would indeed violate its conditions. 
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Such skew appears in the decisions of both human and AI decision makers -- and should be properly taken into account for evaluation.
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In this paper, we develop a Bayesian approach towards this end that uses counterfactual-based imputation to infer unobserved outcomes.
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Compared to previous state-of-the-art, the quality of decisions is estimated more accurately and with lower variance. 
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The approach is also shown to be robust to different variations in the decision mechanisms in the data.
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\mcomment{On one hand, since we use judicial data in our experiments, it makes sense to use the bail-or-jail case in the abstract. On the other hand, this does not connect with the motivation we provide to evaluate the decision of (computer/ML/AI) systems, since jail-or-bail decisions are not currently made by such systems (risk scores are used as assisting tools). The bank loan example might look better in the abstract.}
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\end{abstract}


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