Editorials in 2022

2022 has seen eye-catching developments in AI applications. Work is needed to ensure that ethical reflection and responsible publication practices are keeping pace.

AI promises to bring many benefits to healthcare and research, but mistrust has built up owing to many instances of harm to under-represented communities. To amend this, participatory approaches can directly involve communities in AI research that will impact them. An important element of such approaches is ensuring that communities can take control over their own data and how they are shared.

We introduced reusability reports, an article type to highlight code reusability, almost two years ago. On the basis of the results and positive feedback from authors and referees, we remain enthusiastic about the format.

The public release of ‘Stable Diffusion’, a high-quality image generation tool, sets new standards in open-source AI development and raises new questions.

There is growing interest in using machine learning to mitigate climate change. But as avoiding catastrophic temperature rises becomes more urgent, action is also needed to understand the environmental impact of machine learning research.

As with last summer, COVID-19 is still with us, but there is a semblance of what life was like before the pandemic. Here, we recommend AI podcasts from the past year that may inform, inspire or entertain, as we get an opportunity to travel or take time away from regular activities.

Soon into the COVID-19 pandemic, civil-rights groups raised the alarm over the increase in digital surveillance infringing on individual rights. But there are other potential harms as tech companies accelerate their expansion into new areas essential to public-service provision.

A biomimetic sense of touch could ground robots better in the physical world and improve their interactions with humans.

Considering the potential for unintended harmful applications of AI tools can lead to deeply concerning findings. An urgent question is how to achieve the right balance between keeping science open and preventing misuse or malicious repurposing.

The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

Autonomous vehicle technologies need to be safer than humans by a considerable margin before they can be truly self-driving. But they can provide substantial benefits as assistive driving technology already today — provided their limitations are properly communicated.

Growing criticisms of datasets that were built from user-generated data scraped from the web have led to the retirement or redaction of many popular benchmarks. Their afterlife, as copies or subsets that continue to be used, is a cause for concern.