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

arXiv: https://arxiv.org/abs/2301.05969 · HTML: https://mlsanity.com/paper/2301.05969/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 43 references that are in the map.
- First posted on arXiv 14 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)

## Built on by (most cited first, 2 of 2)

- [Detecting AI Assistance in Abstract Complex Tasks](https://mlsanity.com/paper/2507.10761/) (2025)
- [Understanding Role Switching in Human-AI Collaboration through Multimodal Behavioral Signals](https://mlsanity.com/paper/2509.20666/) (2025)

## Abstract (arXiv)

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.