mlsanityData to 7 Oct 2026

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

arXiv 2203.09911 ↗First posted 18 Mar 2022cs.LG cs.AIcited by 7

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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.

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

Builds on

25 of 134 references.

References · 25

  1. 2020Shortcut Learning in Deep Neural Networkscited by 1,928
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,410
  4. 2018Relational inductive biases, deep learning, and graph networkscited by 1,341
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  6. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,242
  7. 2018Recognition in Terra Incognitacited by 655
  8. 2019Green AIcited by 620
  9. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  10. 2021Carbon Emissions and Large Neural Network Trainingcited by 561
  11. 2019The Woman Worked as a Babysitter: On Biases in Language Generationcited by 540
  12. 2018Confounding variables can degrade generalization performance of radiological deep learning modelscited by 374
13 more
  1. 2020Inductive Biases for Deep Learning of Higher-Level Cognitioncited by 273
  2. 2018Deep Learning: A Critical Appraisalcited by 214
  3. 2019Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learningcited by 116
  4. 2018Algorithms that Remember: Model Inversion Attacks and Data Protection Lawcited by 52
  5. 2015Learning the Preferences of Ignorant, Inconsistent Agentscited by 45
  6. 2020Understanding Human Intelligence through Human Limitationscited by 33
  7. 2018A review of possible effects of cognitive biases on the interpretation of rule-based machine learning modelscited by 22
  8. 2021Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mindcited by 19
  9. 2022Speciesist bias in AI -- How AI applications perpetuate discrimination and unfair outcomes against animalscited by 11
  10. 2019Forbidden knowledge in machine learning -- Reflections on the limits of research and publicationcited by 5
  11. 2020Ethical behavior in humans and machines -- Evaluating training data quality for beneficial machine learningcited by 4
  12. 2019Using Machine Learning to Guide Cognitive Modeling: A Case Study in Moral Reasoningcited by 1
  13. 2020Investigating Efficient Learning and Compositionality in Generative LSTM Networkscited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2025AI Awarenesscited by 6
  2. 2026Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Executioncited by 3
  3. 2023Cognitive network science reveals bias in GPT-3, ChatGPT, and GPT-4 mirroring math anxiety in high-school studentscited by 2
  4. 2024What Makes Language Models Good-enough?cited by 2
  5. 2024Heuristics and Biases in AI Decision-Making: Implications for Responsible AGIcited by 2
  6. 2025Speciesism in AI: Evaluating Discrimination Against Animals in Large Language Modelscited by 1
  7. 2025AI Biases as Asymmetries: A Review to Guide Practicecited by 0