CORTEXA
← Browse
arxivcs.CYcs.AIcs.HC2026-07-17Cited by 0

How Formerly Incarcerated People Envision Technologies for Prison Parole

Saiph Savage, Jesse Nava, Wanqing Iris Zhou, Hwijoon Lee

AI-driven algorithms and automated tools are increasingly embedded in the correctional landscape, shaping parole eligibility,release decisions, and surveillance. These tools are also often framed as objective, inevitable solutions to inefficiency andbias. Yet, these computational systems are rarely designed with input from justice-impacted individuals, which means theymight fail to address the real needs of incarcerated people. To address this gap, we surveyed 31 formerly incarcerated peopleabout their parole experiences and their visions for technologies that could support parole preparation. Contrary to dominantassumptions, participants did not imagine computational tools as instruments to dismantle the prison system, but as resourcesfor navigating power: translating complex parole concepts into culturally familiar terms, documenting personal transformationin board-legible ways, and recognizing the often-invisible labor of families. We argue that these imaginaries point towardtechnologies designed for strategic agency, where tools help incarcerated individuals and their families build the capacity tonavigate existing power structures in pursuit of freedom. We conclude by reframing computational tools for parole awayfrom surveillance and toward human-centered systems that support people in navigating carceral power structures.

View free PDFSource page

Related papers

arxivcs.CYcs.HC2026-07-31

Hypergamigication Through Integrating Game Engines and Learning Management Systems: Ender's Game

Araz Yusubov, Michael Bechtel, Tangiz Alizada

This paper discusses games, their use in education, and previous work on integrating game engines and learning management systems (LMS). It proposes a bidirectional integration where game environments are generated using LMS content, introducing the concept of hypergamification a…

View free PDFSource page
arxivcs.CLcs.AIcs.CVcs.HC2026-07-31

FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models

Jeffrey M. Girard, Jason Z. Zheng, Jacqueline R. Vertino, Antony D'Avirro, Benjamin Peloquin

Reading a social situation often depends on behavior, not words alone. We introduce FriendBench, a benchmark for inferring whether two people are already familiar or are meeting as strangers, from a 20-second clip of a dyadic ice-breaker conversation. Every pair answers the same…

View free PDFSource page