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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.
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.
Additive manufacturing plays an essential role in producing metamaterials by precisely controlling geometries and multiscale structures to achieve the desired properties. In this Comment, we highlight the challenges and opportunities from additive manufacturing for computational metamaterials design.
This issue of Nature Computational Science features a Focus that highlights recent advancements, challenges, and opportunities in computational models for metamaterials design and manufacturing, as well as explores their potential promises in emerging information processors and computing technologies.
Dr Yongmin Liu — professor of mechanical and industrial engineering and professor of electrical and computer engineering at Northeastern University — talks to Nature Computational Science about his career trajectory, his research on photonic metamaterials, and the synergistic effects between photonic metamaterials research and artificial intelligence (AI).
