With the proliferation of AI in the workforce labor advocates, workers, and the public at large have started pushing for protections against AI overreach in different employment sectors. Here, Mishal Khan looks at the myriad approaches to one of these types of protections: human-in-the-loop provisions. While many states have passed different versions of these provisions, Khan cautions against a complacency that allows these provisions to co-opt workers rather than centering them.

Reflecting on the panel discussion “From Studio to Study: Artistic Research on AI and Society” cohosted by the Just Tech program and NEW INC, Ever Bussey and Rodrigo Ugarte tease out the thematic threads that ran through the conversation between artists Lauren Lee McCarthy, Romi Morrison, Dorothy Santos, and X.A. Li. They touch on how artistic practice can be a means to reveal dynamics of agency, power, and feeling with regards to technology and artificial intelligence, as well as how engineers are not the only experts.

Pope Leo XIV’s first encyclical, Magnifica Humanitas, brings to bear Catholic social teaching on the impact of artificial intelligence on society. Here, Anna Su reflects on the pope’s message, arguing that within the encyclical’s skepticism of AI and technology lies a more radical appreciation for human imperfection. Magnifica Humanitas also introduces a moral dimension to the AI question. However, for Su, the encyclical falls short in answering questions on the nature of our own humanity and its worth; questions she believes the Catholic Church is well placed to answer.

A collaboration between scholars and practitioners, this essay introduces the Theory of Interrogative Reasoning, a community-centered framework that reconceptualizes ethical AI as a function of power, belief, and timing rather than solely technical design. Here, Jameila “Meme” Styles, Zameshia Williams, Jose Teran, and Sylvester Johnson draw on real-world application of this approach to demonstrate how community-led interrogation can transform lived experience into actionable evidence that informs AI governance, system design, and public accountability.