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.AIcs.HC2026-07-15

Learning Engagement Assistant (LEA): Cross-Course Scalability and Classroom Evaluation of an Agentic AI Tutoring System

Teri Rumble, Javad Zarrin, P. George Lovell, Ruth Falconer

This paper is an extension of a paper presented at the ICAART 2026 conference, which introduced LEA (Learning Engagement Assistant), an adaptive AI tutoring agent combining course-specific Retrieval-Augmented Generation (RAG) with structured Knowledge Component (KC) models across…

View free PDFSource page
arxivcs.AIcs.CYcs.HC2026-07-15

AI advice suppresses people's willingness to say "I don't know", even when the advice is wrong and accuracy is incentivized

Chiara Marcoccia, Walter Quattrociocchi, Valerio Capraro

Knowing when to say "I don't know" is fundamental to human judgment, yet AI assistants offer a fluent answer to almost any question. In five experiments (N = 3,132; four preregistered, one direct replication), participants answered difficult questions and could always decline to…

View free PDFSource page
arxivcs.MAcs.AIcs.CYcs.HC2026-07-17

When Not to Automate: A Formal Protocol for Human Preservation in AI-Optimized Organizations

Jose Manuel de la Chica Rodriguez, Jairo Rodriguez Arias, Spyridon Chouliaras

Standard automation ROI misses four categories of systemic risk -- tacit knowledge erosion, resilience reduction, regulatory exposure, and socio-institutional capital degradation -- that affect long-term organizational performance. PHP-AIO (Protocol for Human Preservation in AI-O…

View free PDFSource page
arxivcs.AIcs.CYcs.HC2026-07-20

The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems

Samuel Presgraves

Existing AI measurement frameworks quantify cognitive capability, task automation, or catastrophic risk, but none measure autonomous agency: the extent to which a system behaves in a self-directed way. A system can saturate capability benchmarks while remaining entirely reactive,…

View free PDFSource page
arxivcs.AIcs.CLcs.CYcs.HC2026-07-15

Measuring How Students Rely on Generative AI in Academic Writing: Development and Multi-Source Validation of the Generative AI Reliance Types Scale (GenAI-RTS)

Shahin Hossain, Tukhbita Afroz Nawmi

As generative AI (GenAI) becomes increasingly embedded in undergraduate academic writing, how students rely on these tools, rather than simply whether they use them, has become a central question for learning, academic integrity, and educational equity. Existing measures of relia…

View free PDFSource page
arxivcs.HCcs.AIcs.CYcs.ETcs.RO2026-07-14

Practical Judgment, Virtue, and Intuition in the Use of Opaque AI-Enabled Systems

Nathan G. Wood, Andrew P. Rebera

AI-enabled systems are seeing increasing deployment across numerous domains, with many being "black boxes" with respect to core functions and capabilities. I.e., many systems take inputs and give outputs, but without users having any ability to see how the former lead to the latt…

View free PDFSource page