Bounded Minds, Generative Machines: Envisioning Conversational AI that Works with Human Heuristics and Reduces Bias Risk

TL;DR

This paper proposes designing conversational AI that collaborates with human heuristics and bounded rationality to improve decision-making and reduce bias, moving beyond traditional accuracy metrics.

Contribution

It introduces a research framework focused on aligning AI with human cognitive limitations and heuristics, emphasizing decision quality and cognitive robustness.

Findings

Highlights importance of working with human heuristics

Suggests new evaluation metrics beyond accuracy

Proposes methods for detecting cognitive vulnerabilities

Abstract

Conversational AI is rapidly becoming a primary interface for information seeking and decision making, yet most systems still assume idealized users. In practice, human reasoning is bounded by limited attention, uneven knowledge, and reliance on heuristics that are adaptive but bias-prone. This article outlines a research pathway grounded in bounded rationality, and argues that conversational AI should be designed to work with human heuristics rather than against them. It identifies key directions for detecting cognitive vulnerability, supporting judgment under uncertainty, and evaluating conversational systems beyond factual accuracy, toward decision quality and cognitive robustness.