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Limitations of Deep Learning vs. Human Intelligence: Training Data, Interpretability, Bias, and Ethics
5 references·0 citing·17 second hop
Limitations of Deep Learning vs. Human Intelligence: Training Data, Interpretability, Bias, and Ethics (2025)Limitations of Deep Learning vs. Human In…A systematic review of trustworthy artificial intelligence applications in natural disasters (2024)A systematic review of trustw…A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions (2024)A Comprehensive Review of Bia…The Considerations of Trustworthy AI Components in Generative AI; A Letter to Editor (2023)The Considerations of Trustwo…From Machine Learning to Artificial General Intelligence: A Roadmap and Implications (2023)From Machine Learning to Arti…What humanlike errors do autonomous vehicles need to avoid to maximize safety? (2020)What humanlike errors do auto…Research Priorities for Robust and Beneficial Artificial Intelligence (2015)Future Progress in Artificial Intelligence: A Survey of Expert Opinion (2016)Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (2020)Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations (2019)Towards Fairness in Visual Recognition: Effective Strategies for Bias Mitigation (2020)Bias and Unfairness in Machine Learning Models: A Systematic Review on Datasets, Tools, Fairness Metrics, and Identification and Mitigation Methods (2023)Mitigating Bias in Machine Learning Models with Ethics-Based Initiatives: The Case of Sepsis (2025)The impact of guilt on student interactions with generative AI technology (2025)Proceedings of the International Joint Conference on Neural Networks, 2003. (2003)A Survey on Bias and Fairness in Machine Learning (2021)Artificial intelligence, systemic risks, and sustainability (2021)Disaster Risk Reduction and Management With Emerging Technologies (2025)Exploring the Intersection of Machine Learning and Big Data: A Survey (2025)High-Risk Artificial Intelligence (2025)Autonomous Vehicle Accident Data Analysis: California OL 316 Reports: 2015–2020 (2021)Attention for Vision-Based Assistive and Automated Driving: A Review of Algorithms and Datasets (2022)Locating the Ethics of ChatGPT—Ethical Issues as Affordances in AI Ecosystems (2025)
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References

  • 2024A systematic review of trustworthy artificial intelligence applications in natural disasters311 cited
  • 2020What humanlike errors do autonomous vehicles need to avoid to maximize safety?104 cited
  • 2024A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions103 cited
  • 2023From Machine Learning to Artificial General Intelligence: A Roadmap and Implications29 cited
  • 2023The Considerations of Trustworthy AI Components in Generative AI; A Letter to Editor15 cited

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