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  • Substantial advances have been made in the past decade in developing high-performance machine learning models for medical applications, but translating them into practical clinical decision-making processes remains challenging. This Perspective provides insights into a range of challenges specific to high-dimensional, multimodal medical imaging.

    • Rohan Shad
    • John P. Cunningham
    • William Hiesinger
  • When the training data for machine learning are highly personal or sensitive, collaborative approaches can help a collective of stakeholders to train a model together without having to share any data. But there are still risks to the privacy of the data. This Perspective provides an overview of potential attacks on collaborative machine learning and how these threats could be addressed.

    • Dmitrii Usynin
    • Alexander Ziller
    • Jonathan Passerat-Palmbach
  • The ethical use of publicly available datasets with human data for which consent has not been explicitly given needs urgent attention from researchers, funders, research institutes and publishers. A specific challenging case is research involving hacked data and this Perspective discusses whether and under what conditions it is morally and ethically justified to conduct such research.

    • Marcello Ienca
    • Effy Vayena
  • Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

    • Vishwali Mhasawade
    • Yuan Zhao
    • Rumi Chunara
  • As highly automated systems become pervasive in society, enforceable governance principles are needed to ensure safe deployment. This Perspective proposes a pragmatic approach where independent audit of AI systems is central. The framework would embody three AAA governance principles: prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements.

    • Gregory Falco
    • Ben Shneiderman
    • Zee Kin Yeong
  • Traditional sensing techniques apply computational analysis at the output of the sensor hardware to separate signal from noise. A new, more holistic and potentially more powerful approach proposed in this Perspective is designing intelligent sensor systems that ‘lock-in’ to optimal sensing of data, making use of machine leaning strategies.

    • Zachary Ballard
    • Calvin Brown
    • Aydogan Ozcan
  • Online targeted advertising fuelled by machine learning can lead to the isolation of individual consumers. This problem of ‘epistemic fragmentation’ cannot be tackled with current regulation strategies and a new, civic model of governance for advertising is needed.

    • Silvia Milano
    • Brent Mittelstadt
    • Christopher Russell
  • Modern machine learning approaches, such as deep neural networks, generalize well despite interpolating noisy data, in contrast with textbook wisdom. Mitra describes the phenomenon of statistically consistent interpolation (SCI) to clarify why data interpolation succeeds, and discusses how SCI elucidates the differing approaches to modelling natural phenomena represented in modern machine learning, traditional physical theory and biological brains.

    • Partha P. Mitra
  • Medical artificial intelligence and machine learning technologies marketed directly to consumers are on the rise. The authors argue that the regulatory landscape for such technologies should operate differently when a system is designed for personal use than when it is designed for clinicians and doctors.

    • Boris Babic
    • Sara Gerke
    • I. Glenn Cohen
  • Many researchers have become interested in implementing artificial intelligence methods in applications with socially beneficial outcomes. To provide a way to study and benchmark such ‘AI for social good’ applications, Josh Cowls et al. use the United Nations’ Sustainable Development Goals to systematically analyse AI for social good applications.

    • Josh Cowls
    • Andreas Tsamados
    • Luciano Floridi
  • The Conference on Neural Information Processing Systems (NeurIPS) introduced a new requirement in 2020 that submitting authors must include a statement on the broader impacts of their research. Prunkl and colleagues discuss challenges and benefits of this requirement and propose suggestions to address the challenges.

    • Carina E. A. Prunkl
    • Carolyn Ashurst
    • Allan Dafoe
  • Evolutionary computation is inspired by biological evolution and exhibits characteristics familiar from biology such as openendedness, multi-objectivity and co-evolution. This Perspective highlights where major differences still exist, and where the field of evolutionary computation could attempt to approach features from biological evolution more closely, namely neutrality and random drift, complex genotype-to-phenotype mappings with rich environmental interactions and major organizational transitions.

    • Risto Miikkulainen
    • Stephanie Forrest
  • DNN classifiers are vulnerable to small, specific perturbations in an input that seem benign to humans. To understand this phenomenon, Buckner argues that it may be necessary to treat the patterns that DNNs detect in these adversarial examples as artefacts, which may contain predictive information.

    • Cameron Buckner
  • Deep learning has resulted in impressive achievements, but under what circumstances does it fail, and why? The authors propose that its failures are a consequence of shortcut learning, a common characteristic across biological and artificial systems in which strategies that appear to have solved a problem fail unexpectedly under different circumstances.

    • Robert Geirhos
    • Jörn-Henrik Jacobsen
    • Felix A. Wichmann
  • Robots could play an important part in transforming healthcare to cope with the COVID-19 pandemic. This Perspective highlights how robotic technology integrated in a range of tasks in the surgical environment could help to ensure a continuation of medical services while reducing the risk of infection.

    • Ajmal Zemmar
    • Andres M. Lozano
    • Bradley J. Nelson
  • Evidence syntheses produced from the scientific literature are important tools for policymakers. Producing such evidence syntheses can be highly time- and labour-consuming but machine learning models can help as already demonstrated in the health and medical sciences. This Perspective describes a machine learning-based framework specifically designed to support evidence syntheses in the area of agricultural research, for tackling the UN Sustainable Development Goal 2: zero hunger by 2030.

    • Jaron Porciello
    • Maryia Ivanina
    • Haym Hirsh
  • Developing swarm robots for a specific application is a time consuming process and can be alleviated by automated optimization of the behaviour. Birattari and colleagues discuss that there are two fundamentally different design approaches; a semi-autonomous one, which allows for situation specific tuning from human engineers and one that needs to be entirely autonomous.

    • Mauro Birattari
    • Antoine Ligot
    • Ken Hasselmann
  • Machine learning models are commonly used to predict risks and outcomes in biomedical research. But healthcare often requires information about cause–effect relations and alternative scenarios, that is, counterfactuals. Prosperi et al. discuss the importance of interventional and counterfactual models, as opposed to purely predictive models, in the context of precision medicine.

    • Mattia Prosperi
    • Yi Guo
    • Jiang Bian