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AI-induced hyper-learning in humans
16 references·1 citing·39 second hop
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this work references cited by second hop

References

  • 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification3,509 cited
  • 2017Semantics derived automatically from language corpora contain human-like biases2,845 cited
  • 2019Algorithm appreciation: People prefer algorithmic to human judgment2,045 cited
  • 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings1,615 cited
  • 2018An Empathy-Driven, Conversational Artificial Intelligence Agent (Wysa) for Digital Mental Well-Being: Real-World Data Evaluation Mixed-Methods Study1,061 cited
  • 2018AI can be sexist and racist — it’s time to make it fair764 cited
  • 2021Noise : a flaw in human judgment753 cited
  • 2021Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency541 cited
  • 2020AI in mental health406 cited
  • 2023Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale340 cited
  • 2024How human–AI feedback loops alter human perceptual, emotional and social judgements283 cited
  • 2025On the conversational persuasiveness of GPT-4282 cited
  • 2022Ethics of Data and Analytics248 cited
  • 2023Influencing human–AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness188 cited

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  • 2023Working With AI to Persuade: Examining a Large Language Model's Ability to Generate Pro-Vaccination Messages167 cited
  • 2023The emergence of economic rationality of GPT163 cited
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    • 2026Artificial intelligence-enabled process safety: Advances, challenges, and future directions for high-risk industrial systems5 cited