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

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.