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  • The regulatory landscape for artificial intelligence (AI) is shaping up on both sides of the Atlantic, urgently awaited by the scientific and industrial community. Commonalities and differences start to crystallize in the approaches to AI in medicine.

    • Kerstin N. Vokinger
    • Urs Gasser
    Comment
  • Large language models, which are increasingly used in AI applications, display undesirable stereotypes such as persistent associations between Muslims and violence. New approaches are needed to systematically reduce the harmful bias of language models in deployment.

    • Abubakar Abid
    • Maheen Farooqi
    • James Zou
    Comment
  • The COVID-19 pandemic has highlighted key challenges for patient care and health provider safety. Adaptable robotic systems, with enhanced sensing, manipulation and autonomy capabilities could help address these challenges in future infectious disease outbreaks.

    • Hao Su
    • Antonio Di Lallo
    • Axel Krieger
    Comment
  • To truly understand the societal impact of AI, we need to look beyond the exclusive focus on quantitative methods, and focus on qualitative methods like ethnography, which shed light on the actors and institutions that wield power through the use of these technologies.

    • Vidushi Marda
    • Shivangi Narayan
    Comment
  • Synthesizing robots via physical artificial intelligence is a multidisciplinary challenge for future robotics research. An education methodology is needed for researchers to develop a combination of skills in physical artificial intelligence.

    • Aslan Miriyev
    • Mirko Kovač
    Comment
  • Addressing the problems caused by AI applications in society with ethics frameworks is futile until we confront the political structure of such applications.

    • Jathan Sadowski
    • Mark Andrejevic
    Comment
  • For machine learning developers, the use of prediction tools in real-world clinical settings can be a distant goal. Recently published guidelines for reporting clinical research that involves machine learning will help connect clinical and computer science communities, and realize the full potential of machine learning tools.

    • Bilal A. Mateen
    • James Liley
    • Sebastian J. Vollmer
    Comment
  • There is a need to consider how AI developers can be practically assisted in identifying and addressing ethical issues. In this Comment, a group of AI engineers, ethicists and social scientists suggest embedding ethicists into the development team as one way of improving the consideration of ethical issues during AI development.

    • Stuart McLennan
    • Amelia Fiske
    • Alena Buyx
    Comment
  • As robot swarms move from the laboratory to real-world applications, a routine checklist of questions could help ensure their safe operation.

    • Edmund R. Hunt
    • Sabine Hauert
    Comment
  • Artificial intelligence tools can help save lives in a pandemic. However, the need to implement technological solutions rapidly raises challenging ethical issues. We need new approaches for ethics with urgency, to ensure AI can be safely and beneficially used in the COVID-19 response and beyond.

    • Asaf Tzachor
    • Jess Whittlestone
    • Seán Ó hÉigeartaigh
    Comment
  • The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

    • Nathan Peiffer-Smadja
    • Redwan Maatoug
    • Jean-Rémi King
    Comment
  • In an unprecedented effort of scientific collaboration, researchers across fields are racing to support the response to COVID-19. Making a global impact with AI tools will require scalable approaches for data, model and code sharing; adapting applications to local contexts; and cooperation across borders.

    • Miguel Luengo-Oroz
    • Katherine Hoffmann Pham
    • Bernardo Mariano
    Comment
  • The attention and resources of AI researchers have been captured by COVID-19. However, successful adoption of AI models in the fight against the pandemic is facing various challenges, including moving clinical needs as the epidemic progresses and the necessity to translate models to local healthcare situations.

    • Yipeng Hu
    • Joseph Jacob
    • Danail Stoyanov
    Comment
  • The Catholic Church is challenged by scientific and technological innovation but can help to integrate multiple voices in the ongoing dialogue regarding AI and machine ethics. In this context, a multidisciplinary working group brought together by the Church reflected on roboethics, explored the themes of embodiment, agency and intelligence.

    • Edoardo Sinibaldi
    • Chris Gastmans
    • Vincenzo Paglia
    Comment
  • As robotic systems become more autonomous, it gets less straightforward to determine liability when humans are harmed. This is an emerging challenge, with legal implications, in the field of surgical robotic systems. The iRobotSurgeon Survey explores public opinions about responsibility and liability in the area of surgical robotics.

    • Aimun A. B. Jamjoom
    • Ammer M. A. Jamjoom
    • Hani J. Marcus
    Comment
  • As artificial intelligence becomes prevalent in society, a framework is needed to connect interpretability and trust in algorithm-assisted decisions, for a range of stakeholders.

    • Julia Stoyanovich
    • Jay J. Van Bavel
    • Tessa V. West
    Comment
  • Machine learning models have great potential in biomedical applications. A new platform called GradioHub offers an interactive and intuitive way for clinicians and biomedical researchers to try out models and test their reliability on real-world, out-of-training data.

    • Abubakar Abid
    • Ali Abdalla
    • James Zou
    Comment
  • Many high-level ethics guidelines for AI have been produced in the past few years. It is time to work towards concrete policies within the context of existing moral, legal and cultural values, say Andreas Theodorou and Virginia Dignum.

    • Andreas Theodorou
    • Virginia Dignum
    Comment