# Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems

arXiv: https://arxiv.org/abs/2203.09911 · HTML: https://mlsanity.com/paper/2203.09911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 134 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 25 of 25)

- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Relational inductive biases, deep learning, and graph networks](https://mlsanity.com/paper/1806.01261/) (2018)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Recognition in Terra Incognita](https://mlsanity.com/paper/1807.04975/) (2018)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Carbon Emissions and Large Neural Network Training](https://mlsanity.com/paper/2104.10350/) (2021)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [Inductive Biases for Deep Learning of Higher-Level Cognition](https://mlsanity.com/paper/2011.15091/) (2020)
- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning](https://mlsanity.com/paper/1912.10389/) (2019)
- [Algorithms that Remember: Model Inversion Attacks and Data Protection Law](https://mlsanity.com/paper/1807.04644/) (2018)
- [Learning the Preferences of Ignorant, Inconsistent Agents](https://mlsanity.com/paper/1512.05832/) (2015)
- [Understanding Human Intelligence through Human Limitations](https://mlsanity.com/paper/2009.14050/) (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)
- [Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind](https://mlsanity.com/paper/2106.13249/) (2021)
- [Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animals](https://mlsanity.com/paper/2202.10848/) (2022)
- [Forbidden knowledge in machine learning -- Reflections on the limits of research and publication](https://mlsanity.com/paper/1911.08603/) (2019)
- [Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learning](https://mlsanity.com/paper/2008.11463/) (2020)
- [Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoning](https://mlsanity.com/paper/1902.06744/) (2019)
- [Investigating Efficient Learning and Compositionality in Generative LSTM Networks](https://mlsanity.com/paper/2004.07754/) (2020)

## Built on by (most cited first, 7 of 7)

- [AI Awareness](https://mlsanity.com/paper/2504.20084/) (2025)
- [Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution](https://mlsanity.com/paper/2607.13034/) (2026)
- [Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school students](https://mlsanity.com/paper/2305.18320/) (2023)
- [What Makes Language Models Good-enough?](https://mlsanity.com/paper/2406.03666/) (2024)
- [Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI](https://mlsanity.com/paper/2410.02820/) (2024)
- [Speciesism in AI: Evaluating Discrimination Against Animals in Large Language Models](https://mlsanity.com/paper/2508.11534/) (2025)
- [AI Biases as Asymmetries: A Review to Guide Practice](https://mlsanity.com/paper/2503.07326/) (2025)

## Abstract (arXiv)

This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertain real-world environments, we argue for the structurewise implementation of human cognitive biases in learning algorithms. Secondly, we argue that in order to achieve ethical machine behavior, filter mechanisms have to be applied for selecting biased training stimuli that represent social or behavioral traits that are ethically desirable. We use insights from cognitive science as well as ethics and apply them to the AI field, combining theoretical considerations with seven case studies depicting tangible bias implementation scenarios. Ultimately, this paper is the first tentative step to explicitly pursue the idea of a re-evaluation of the ethical significance of machine biases, as well as putting the idea forth to implement cognitive biases into machines.