We present a Focus that calls attention to the current state of diversity, equity, and inclusion in computational science, including discussions on the challenges of improving equitable access and representation, as well as on strategies for improving computational tools to avoid contributing to inequalities.

Many AI companies implement safety systems to protect users from offensive or inaccurate content. Though well intentioned, these filters can exacerbate existing inequalities, and data shows that they have disproportionately removed LGBTQ+ content.

Nature Computational Science asked a group of scientists to discuss strategies for increasing the presence of Black, Indigenous, People of Color (BIPOC) researchers in computational science, as well as the various considerations to be made for improving education and methods design.

We can design, build and use AI systems with intentionality, to make them an equalizing force within society, or we can use AI without intentionality, in which case AI could become a force that exacerbates inequality, or both. Society has the power to decide which.

This work introduces a framework that enhances deep learning for anomalous diffusion, enabling reliable detection of out-of-distribution dynamics and characterization of complex behaviors across diverse systems.

Deep learning electronic structure calculations are generalized from the atomic-orbital basis to the plane-wave basis, resulting in higher accuracy, improved transferability and the capability to utilize existing electronic structure big data.

Active deep learning is a promising approach to learn from low-data scenarios in drug discovery. This study illuminates key success factors of active learning and shows that it can boost hit discovery by up to sixfold over traditional methods.

By employing the subtask decomposition strategy, STAMP outperforms existing models in single, multiple and cross-cell-line scenarios for genetic perturbation prediction, showing potential to uncover gene regulations and interactions.

Clustering in high-dimensional spaces with a large number of clusters and identifying common aspects of within-cluster variability remain challenging. Here the authors develop an unsupervised method for this purpose and demonstrate it on brain single-cell datasets.

MeK-means clusters single-cell multimodal data by linking modalities through their biophysical relationships. We redefine clusters through transcription kinetics to reveal how RNA production and processing drive cellular diversity and disease.

Navigating built environments can be a challenge for persons with disabilities. Emerging computational capabilities are promising to help by providing the right information at the right time in accessible formats.

The proliferation of artificial intelligence (AI) algorithms for public use has led to many creative healthcare applications, some with the potential to create or worsen health inequities. Here, we argue that similar to prescription medicine labels, AI algorithms should be accompanied by a responsible use label.

The widespread availability of digital traces capturing individuals’ daily mobility has the potential to enrich the understanding of the relationship between mobility, gender and socioeconomic factors. In fact, it has led to a heightened interest in deriving policy insights from these data. However, it is also essential to put the focus on methodological aspects to address the data gaps and biases.

We present a Focus that calls attention to the current state of diversity, equity, and inclusion in computational science, including discussions on the challenges of improving equitable access and representation, as well as on strategies for improving computational tools to avoid contributing to inequalities.

The current global economy heavily relies on digital and data-based technologies, which have the use of supercomputing at their core. Latin America is a vast source of human talent in computer science, but the lag in infrastructure investment due to economic and political struggles may cause the economic development of the region to fall behind.

We can design, build and use AI systems with intentionality, to make them an equalizing force within society, or we can use AI without intentionality, in which case AI could become a force that exacerbates inequality, or both. Society has the power to decide which.