Artificial Intelligence (AI) can predict judicial decisions, but can it explain how they are legally jusitified, and can judges trust it? A Perspective in National Science Review argues that legal computing requires higher interpretability and reliability, and judicial values should be embedded into model and algorithm design. It outlines a value-guided framework within a “society–law–ethics–algorithms–data” system and identifies three scenario-specific applications: legislation, feedback-validated enforcement and trustworthy, human-centered adjudication. The Perspective further suggests that combining empirical learning with formal reasoning may offer a particularly promising path forward.

University of Phoenix has published a new white paper, Three Pillars for Embracing AI at University of Phoenix, by Christina Neider, Ed.D., and Marc Booker, Ph.D., outlining the University's institutional framework for integrating generative artificial intelligence (AI) across academic programs, student learning experiences, and institutional processes, policies and workflows while helping prepare working adult learners for an AI-enabled workforce. The white paper examines the role higher education institutions can play in helping learners develop the knowledge, skills and judgment needed to use AI effectively and responsibly. Drawing on University of Phoenix's generative AI implementation work, the authors share a scalable, three-pillar framework for higher education AI adoption aligned with workforce relevance and student success.

Our eyes do not always tell us exactly where things are – and that may be a feature of how biological vision works, rather than simply a flaw. A new study by York University researchers uses a common illusion to ask if artificial intelligence is meant to see more like us, should it make some of the same systematic perceptual “mistakes”? For example, after staring at something moving steadily in one direction, a stationary object viewed immediately afterward can appear slightly displaced in the opposite direction. This well-known visual illusion, called a motion aftereffect, gives scientists an unusual window into the computations underlying perception: the image itself has not moved, but our experience of where it is has changed.

JMIR Publications released four feature News and Perspectives articles this week, covering developments in smart textiles, PMOS treatment in India, the FDA’s TEMPO pilot for regulating AI-enabled devices, and the ethical quandaries of using online forums in research.