Functional Misalignment in Human-AI Interactions on Digital Platforms

2026-04-13

AI Summary

This work proposes a “functional misalignment” theoretical framework to explain how current algorithmic systems, while optimized for predictable user behaviors such as clicks and engagement, systematically diverge from human well-being, leading to collective harms including deteriorating mental health, societal polarization, and erosion of trust. The study identifies three core mechanisms underlying this paradox—algorithms’ bias toward short-term reactive behaviors, human–AI feedback loops that amplify biases, and group-level dynamics that magnify negative outcomes—even when individual-level predictions are highly accurate. Integrating behavioral modeling, feedback loop analysis, and complex systems theory, the paper outlines an interdisciplinary research agenda that exposes a fundamental tension between AI optimization objectives and long-term human welfare, offering a foundation for designing human–AI interaction systems better aligned with societal values.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Algorithmic systems, particularly social media recommenders, have achieved remarkable success in predicting behavior. By optimizing for observable signals such as clicks, views, and engagement, these systems effectively capture user attention and guide interaction. Yet their widespread adoption has coincided with troubling outcomes, including rising mental health concerns, increasing polarization, and erosion of trust. This paper argues that these effects are consequences of a structural functional misalignment between what algorithms optimize - predictable behavior - and the human goals these predictions are intended to serve. We propose that this misalignment arises through three mechanisms: (1) a bias toward modeling fast, reactive behavioral signals over reflective judgment, (2) feedback loops that couple user behavior with algorithmic learning, and (3) emergent collective dynamics that amplify these effects at scale. Together, these mechanisms explain how accurate individual-level predictions can produce adverse societal outcomes. We present functional misalignment as a unifying framework and outline a research agenda for studying and mitigating its effects in human-AI interaction systems.

Problem

• functional misalignment
• human-AI interaction
• algorithmic systems
• social media recommenders
• behavioral prediction

Innovation

• functional misalignment
• algorithmic feedback loops
• behavioral prediction
• human-AI interaction
• emergent collective dynamics