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Computer says ‘no’: Exploring systemic bias in ChatGPT using an audit approach
16 references·5 citing·44 second hop
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this work references cited by second hop

References

  • 2023Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics13,695 cited
  • 2008Bootstrap-Based Improvements for Inference with Clustered Errors4,311 cited
  • 2017Semantics derived automatically from language corpora contain human-like biases2,845 cited
  • 2023Experimental evidence on the productivity effects of generative artificial intelligence2,124 cited
  • 2019Artificial Intelligence in Human Resources Management: Challenges and a Path Forward1,585 cited
  • 2023ChatGPT is fun, but not an author1,433 cited
  • 2021Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review1,104 cited
  • 2022Artificial Intelligence and Jobs: Evidence from Online Vacancies1,071 cited
  • 2023Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT903 cited
  • 2017Meta-analysis of field experiments shows no change in racial discrimination in hiring over time877 cited
  • 2019Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction684 cited
  • 2011Why Do Skilled Immigrants Struggle in the Labor Market? A Field Experiment with Thirteen Thousand Resumes641 cited
  • 2023Tools such as ChatGPT threaten transparent science; here are our ground rules for their use604 cited

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2019
A comparative study of fairness-enhancing interventions in machine learning
601 cited
  • 2016Ethnic discrimination in hiring decisions: a meta-analysis of correspondence tests 1990–2015594 cited
  • 2023GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models540 cited
  • Cited by

    • 2024The Silicon Ceiling: Auditing GPT’s Race and Gender Biases in Hiring45 cited
    • 2025Designing Conversational AI for Aging: A Systematic Review of Older Adults' Perceptions and Needs42 cited
    • 2024Surprising gender biases in GPT24 cited
    • 2024Auditing large language models for race & gender disparities: Implications for artificial intelligence-based hiring15 cited
    • 2025Generative AI and News Consumption: Design Fictions and Critical Analysis13 cited