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  3. The Inversion Problem: Why Algorithms Should Infer Mental State And Not Just Predict Behavior.
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The Inversion Problem: Why Algorithms Should Infer Mental State and Not Just Predict Behavior.

Jon Kleinberg1, Jens Ludwig2, Sendhil Mullainathan3

  • 1Department of Computer Science, Cornell University.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|December 12, 2023

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning algorithms often predict observable behaviors but aim to infer unobserved mental states, creating "inversion problems." New tools integrating behavioral and computational science are needed to bridge this gap for accurate human behavior modeling.

Keywords:
algorithmsbiasesdecision-makingheuristics

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Area of Science:

  • Computational Social Science
  • Behavioral Science
  • Machine Learning Ethics

Background:

  • Machine learning is increasingly applied to predict human behavior.
  • Algorithms often predict observable actions (e.g., clicks, diagnoses) but aim to infer underlying mental states (e.g., preferences, judgments).
  • This discrepancy creates a significant, often overlooked, problem in algorithm design and application.

Purpose of the Study:

  • To identify and define the

Main Methods:

  • Conceptual analysis drawing on psychology and behavioral science.
  • Examination of case studies in recommender systems and medical diagnostics.
  • Argument for the necessity of new interdisciplinary tools.

Main Results:

  • A ubiquitous problem termed "inversion problems" is identified, where algorithms predict proxies for, rather than directly measuring, desired mental states.
  • Psychological insights reveal why behavioral data can be a poor proxy for true goals (e.g., mindless clicking, expert fatigue).
  • Current machine learning approaches are insufficient for addressing these inversion problems.

Conclusions:

  • Solving inversion problems requires moving beyond direct behavioral prediction.
  • New methodologies integrating behavioral and computational sciences are essential for accurately modeling human mental states.
  • Addressing these problems is critical for the responsible development and deployment of machine learning in human-centric applications.