The Role of Heuristics and Biases During Complex Choices with an AI Teammate

TL;DR

This study examines how framing and anchoring biases influence human decision-making with AI helpers during complex choices, revealing that over-reliance on AI can lead to worse outcomes.

Contribution

It introduces a novel experimental paradigm for studying heuristics and biases in complex human-AI decision-making contexts.

Findings

Framing and anchoring effects influence AI-assisted decision outcomes.

Loss framing increases over-reliance on AI, leading to poorer choices.

The paradigm provides data suitable for computational modeling of human-AI interactions.

Abstract

Behavioral scientists have classically documented aversion to algorithmic decision aids, from simple linear models to AI. Sentiment, however, is changing and possibly accelerating AI helper usage. AI assistance is, arguably, most valuable when humans must make complex choices. We argue that classic experimental methods used to study heuristics and biases are insufficient for studying complex choices made with AI helpers. We adapted an experimental paradigm designed for studying complex choices in such contexts. We show that framing and anchoring effects impact how people work with an AI helper and are predictive of choice outcomes. The evidence suggests that some participants, particularly those in a loss frame, put too much faith in the AI helper and experienced worse choice outcomes by doing so. The paradigm also generates computational modeling-friendly data allowing future studies of human-AI decision making.