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Systematic literature review on bias mitigation in generative AI
16 references·0 citing·51 second hop
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References

  • 2002Analyzing the Past to Prepare for the Future: Writing a Literature Review7,440 cited
  • 2023Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy4,557 cited
  • 2015The second machine age: work, progress, and prosperity in a time of brilliant technologies4,380 cited
  • 2017Human Decisions and Machine Predictions1,039 cited
  • 2023Generative AI at Work993 cited
  • 2019AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias884 cited
  • 2023Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies871 cited
  • 2024Generative artificial intelligence, human creativity, and art455 cited
  • 2021AI-enabled business-model innovation and transformation in industrial ecosystems: A framework, model and outline for further research424 cited
  • 2019AI-enabled recruiting: What is it and how should a manager use it?400 cited
  • 2024Talking about Large Language Models378 cited
  • 2023Artificial intelligence in developing countries: The impact of generative artificial intelligence (AI) technologies for development318 cited
  • 2023Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods315 cited

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  • 2020Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation285 cited
  • 2024GenAI against humanity: nefarious applications of generative artificial intelligence and large language models262 cited
  • 2023Using LLMs for Market Research185 cited
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