Auditing for Human Expertise

• URL: http://arxiv.org/abs/2306.01646v2
• Date: Fri, 27 Oct 2023 19:00:05 GMT
• Title: Auditing for Human Expertise
• Authors: Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis Shung
• Abstract summary: We develop a statistical framework under which we can pose this question as a natural hypothesis test. We propose a simple procedure which tests whether expert predictions are statistically independent from the outcomes of interest. A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data.
• Score: 13.740812888680614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may exercise intuition that is difficult to model and/or have access to information (e.g., conversations with a patient) that is simply unavailable to a would-be algorithm. This raises a natural question whether human experts add value which could not be captured by an algorithmic predictor. We develop a statistical framework under which we can pose this question as a natural hypothesis test. Indeed, as our framework highlights, detecting human expertise is more subtle than simply comparing the accuracy of expert predictions to those made by a particular learning algorithm. Instead, we propose a simple procedure which tests whether expert predictions are statistically independent from the outcomes of interest after conditioning on the available inputs (`features'). A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data, and has direct implications for whether human-AI `complementarity' is achievable in a given prediction task. We highlight the utility of our procedure using admissions data collected from the emergency department of a large academic hospital system, where we show that physicians' admit/discharge decisions for patients with acute gastrointestinal bleeding (AGIB) appear to be incorporating information that is not available to a standard algorithmic screening tool. This is despite the fact that the screening tool is arguably more accurate than physicians' discretionary decisions, highlighting that -- even absent normative concerns about accountability or interpretability -- accuracy is insufficient to justify algorithmic automation.

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