News & Comment

The organizers of the TinyML Design Contest describe the top machine-learning-based real-time detection algorithms for ventricular arrhythmia.

As many authors are experimenting with using large language models in writing articles, some guidelines are becoming clear, but these will need to evolve as the capabilities and integration of such tools develop further.

Metaverse-enabled healthcare is no longer hypothetical. Developers must now contend with ethical, legal and social hazards if they are to overcome the systematic inefficiencies and inequities that exist for patients who seek care in the real world.

Generative AI programs can produce high-quality written and visual content that may be used for good or ill. We argue that a credit–blame asymmetry arises for assigning responsibility for these outputs and discuss urgent ethical and policy implications focused on large-scale language models.

Fairness approaches in machine learning should involve more than an assessment of performance metrics across groups. Shifting the focus away from model metrics, we reframe fairness through the lens of intersectionality, a Black feminist theoretical framework that contextualizes individuals in interacting systems of power and oppression.

We are trying to keep up with the torrent of developments and discussions in AI and language models since ChatGPT was unleashed on the world.

In the next phase of space exploration, human crews will be sent on missions beyond the low Earth orbit. Artificial intelligence (AI) is expected to play a main role in autonomous biomonitoring, research and Earth-independent healthcare.

We explore the intersection between algorithms and the State from the perspectives of legislative action, public perception and the use of AI in public administration. Taking India as a case study, we discuss the potential fallout from the absence of rigorous scholarship on such questions for countries in the Global South.

A recent data competition steers clear from leaderboard chasing and promotes the use of a diverse range of metrics to develop rounded, practical algorithms.

Despite the promise of medical artificial intelligence applications, their acceptance in real-world clinical settings is low, with lack of transparency and trust being barriers that need to be overcome. We discuss the importance of the collaborative process in medical artificial intelligence, whereby experts from various fields work together and tackle transparency issues and build trust over time.

The organizers of the EvalRS recommender systems competition argue that accuracy should not be the only goal and explain how they took robustness and fairness into account.

To fully leverage big data, they need to be shared across institutions in a manner compliant with privacy considerations and the EU General Data Protection Regulation (GDPR). Federated machine learning is a promising option.

Guidelines are urgently needed for the use of generative AI tools like ChatGPT in scientific writing.

A recent case of a flawed medical AI system that was backed by public funding provides an opportunity to discuss the impact of government policies and regulation in AI.