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Deep Learning and Ethics

arXiv 2305.15239 ↗First posted 24 May 2023cs.AI cs.CY cs.LGcited by 0

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Abstract (arXiv)

This article appears as chapter 21 of Prince (2023, Understanding Deep Learning); a complete draft of the textbook is available here: http://udlbook.com. This chapter considers potential harms arising from the design and use of AI systems. These include algorithmic bias, lack of explainability, data privacy violations, militarization, fraud, and environmental concerns. The aim is not to provide advice on being more ethical. Instead, the goal is to express ideas and start conversations in key areas that have received attention in philosophy, political science, and the broader social sciences.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 142 references that are in the map. First posted on arXiv 24 May 2023.

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32 of 142 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  4. 2016Concrete Problems in AI Safetycited by 1,642
  5. 2019Energy and Policy Considerations for Deep Learning in NLPcited by 1,254
  6. 2022Quantifying Memorization Across Neural Language Modelscited by 763
  7. 2023Extracting Training Data from Diffusion Modelscited by 732
  8. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  9. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  10. 2020Artificial Intelligence, Values and Alignmentcited by 448
  11. 2020PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Modelscited by 358
  12. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
20 more
  1. 2019Tackling Climate Change with Machine Learningcited by 265
  2. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  3. 2019Risks from Learned Optimization in Advanced Machine Learning Systemscited by 258
  4. 2021The Values Encoded in Machine Learning Researchcited by 172
  5. 2022Power to the People? Opportunities and Challenges for Participatory AIcited by 138
  6. 2019The Ethics of AI Ethics -- An Evaluation of Guidelinescited by 129
  7. 2023Foundation Models and Fair Usecited by 116
  8. 2022The Fallacy of AI Functionalitycited by 88
  9. 2020Privacy in Deep Learning: A Surveycited by 71
  10. 2020The De-democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Researchcited by 67
  11. 2017A Voting-Based System for Ethical Decision Makingcited by 55
  12. 2021Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communitiescited by 40
  13. 2020The Grey Hoodie Project: Big Tobacco, Big Tech, and the threat on academic integritycited by 38
  14. 2021About Face: A Survey of Facial Recognition Evaluationcited by 27
  15. 2016Responses to Critiques on Machine Learning of Criminality Perceptions (Addendum of arXiv:1611.04135)cited by 14
  16. 2019A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Imagescited by 7
  17. 2020The Tragedy of the AI Commonscited by 7
  18. 2021Est-ce que vous compute? Code-switching, cultural identity, and AIcited by 6
  19. 2020A Framework for Democratizing AIcited by 4
  20. 2022The Linguistic Blind Spot of Value-Aligned Agency, Natural and Artificialcited by 1

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