# BIASeD: Bringing Irrationality into Automated System Design

arXiv: https://arxiv.org/abs/2210.01122 · HTML: https://mlsanity.com/paper/2210.01122/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 133 references that are in the map.
- First posted on arXiv 1 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Clarification of the Nuances in the Fairness Metrics Landscape](https://mlsanity.com/paper/2106.00467/) (2021)
- [Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making](https://mlsanity.com/paper/2010.07938/) (2020)
- [A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models](https://mlsanity.com/paper/1804.02969/) (2018)
- [Towards Socially Responsible AI: Cognitive Bias-Aware Multi-Objective Learning](https://mlsanity.com/paper/2005.06618/) (2020)
- [Fooling with facts: Quantifying anchoring bias through a large-scale online experiment](https://mlsanity.com/paper/1911.12275/) (2019)
- [Bounded Rational Decision-Making in Feedforward Neural Networks](https://mlsanity.com/paper/1602.08332/) (2016)
- [VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting](https://mlsanity.com/paper/1502.00582/) (2015)
- [Designing nudge agents that promote human altruism](https://mlsanity.com/paper/2110.00319/) (2021)

## Built on by (most cited first, 5 of 5)

- [Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation](https://mlsanity.com/paper/2305.02231/) (2023)
- [(Ir)rationality and Cognitive Biases in Large Language Models](https://mlsanity.com/paper/2402.09193/) (2024)
- [(Ir)rationality in AI: State of the Art, Research Challenges and Open Questions](https://mlsanity.com/paper/2311.17165/) (2023)
- [Exploring Conversational Agents as an Effective Tool for Measuring Cognitive Biases in Decision-Making](https://mlsanity.com/paper/2401.06686/) (2024)
- [Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI](https://mlsanity.com/paper/2410.02820/) (2024)

## Abstract (arXiv)

Human perception, memory and decision-making are impacted by tens of cognitive biases and heuristics that influence our actions and decisions. Despite the pervasiveness of such biases, they are generally not leveraged by today's Artificial Intelligence (AI) systems that model human behavior and interact with humans. In this theoretical paper, we claim that the future of human-machine collaboration will entail the development of AI systems that model, understand and possibly replicate human cognitive biases. We propose the need for a research agenda on the interplay between human cognitive biases and Artificial Intelligence. We categorize existing cognitive biases from the perspective of AI systems, identify three broad areas of interest and outline research directions for the design of AI systems that have a better understanding of our own biases.