Rethinking Our Approach to Accessibility in the Era of Rapidly Emerging Technologies.

Accessibility has always played catch-up to the detriment of people with disabilities - and this appears to be exacerbated by the rapid advancements in technology. A key question becomes, can we better predict where technology will be in 10 or 20 years and develop a plan to be better positioned to make these new technologies accessible when they make it to market? To attempt to address this question, a "Future of Interface Workshop" was convened in February 2023, chaired by Vinton Cerf and Gregg Vanderheiden that brought together leading researchers in artificial intelligence, brain-computer interfaces, computer vision, and VR/AR/XR, and disability to both a) identify barriers these new technologies might present and how to address them, and b) how these new technologies might be tapped to address current un- or under-addressed problems and populations. This paper provides an overview of the results of the workshop as well as the current version of the R&D Agenda work that was initiated at the conference.

Will AI allow us to dispense with all or most accessibility regulations?

With advances in AI, computer vision, and interface understanding, there is the potential to offload much of the work currently spent by companies' developers in making products accessible. There is also the potential to move our major accessibility approach from an 'inclusively-designed-products-plus-AT focus to a 'universal-interface-transformer focus. This would be a major reversal of approach and have significant ramifications for legislation, regulation, and the established large-scale accessibility industries that have grown up around them.

Bridging the Digital Divide: Using Free Open-Source Tools to Expand Access to Shared-Use Computers in Schools and Libraries.

The use of computers in everyday life has moved from hobby and technical professional use to being essential to almost all activities in people's lives. However, not everyone has a computer themselves or access to the internet at home. To address this, society provides computers that people can use at school, in libraries, at job centers, in community centers, and at government service centers.

Automatic Calibration of Microscopic Traffic Simulation Models Using Artificial Neural Networks.

Traffic simulations are valuable tools for urban mobility planning and operation, particularly in large cities. Simulation-based microscopic models have enabled traffic engineers to understand local transit and transport behaviors more deeply and manage urban mobility. However, for the simulations to be effective, the transport network and user behavior parameters must be calibrated to mirror real scenarios.

Data Representativeness in Accessibility Datasets: A Meta-Analysis.

As data-driven systems are increasingly deployed at scale, ethical concerns have arisen around unfair and discriminatory outcomes for historically marginalized groups that are underrepresented in training data. In response, work around AI fairness and inclusion has called for datasets that are representative of various demographic groups. In this paper, we contribute an analysis of the representativeness of age, gender, and race & ethnicity in accessibility datasets-datasets sourced from people with disabilities and older adults-that can potentially play an important role in mitigating bias for inclusive AI-infused applications.