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Showing 1–7 of 7 results
Advanced filters: Author: Tapabrata Chakraborti Clear advanced filters

  • As the European Union’s Artificial Intelligence Act takes effect, AI systems that mimic how human teams collaborate can improve trust in high-risk situations, such as clinical medicine.

    • Christopher R. S. Banerji
    • Tapabrata Chakraborti
    • Ben D. MacArthur
    Comments & Opinion
    Nature
    Volume: 639, P: 32-34

  • Artificial intelligence tools usually aim to maximize predictive accuracy, but personalized measures of uncertainty, using new techniques such as conformal prediction, are needed for clinical artificial intelligence to realize its potential and improve human health.

    • Christopher R. S. Banerji
    • Tapabrata Chakraborti
    • Ben D. MacArthur
    Comments & Opinion
    Nature Medicine
    Volume: 29, P: 2996-2998

  • AI tools are increasingly used for important decisions, but they can be uncertain about specific individuals or groups. Chakraborty et al. discuss the need for better methods to assess uncertainty in high-stakes applications such as healthcare and finance, and outline a set of main challenges to provide practical guidance for AI researchers.

    • Tapabrata Chakraborti
    • Christopher R. S. Banerji
    • Ben MacArthur
    Reviews
    Nature Machine Intelligence
    Volume: 7, P: 522-530

  • Gathering big datasets has become an essential component of machine learning in many scientific areas, but it is unavoidable that some data values are missing. An important and growing effect that needs careful attention, especially when heterogeneous data sources are combined, is that of structured missingness, where data values are missing not at random, but with a specific structure.

    • Robin Mitra
    • Sarah F. McGough
    • Ben D. MacArthur
    Reviews
    Nature Machine Intelligence
    Volume: 5, P: 13-23

    • Anthony Baptista
    • Alessandro Barp
    • Christopher R. S. Banerji
    ResearchOpen Access
    Scientific Reports
    Volume: 14, P: 1-11