While millions of workers have gained access to PFML, the lack of uniformity in mandatory PFML programs created a growing patchwork of state laws, differing on nearly 30 policy components across four key areas: substantive benefits, financing, eligibility, and administrative requirements.
This essay explains why the Center on Privacy & Technology has chosen to stop using terms like "artificial intelligence," "AI," and "machine learning," arguing that such language obscures human accountability and overstates the capabilities of these technologies.
User research requires working as a team, since it necessitates running sessions with participants, observing and moderating research sessions, analyzing and synthesizing results, as well as communicating results effectively.
Guidance on improving how well AI systems can understand digital content. It emphasizes using machine-readable formats and applying clear content design strategies to enhance both AI processing and human accessibility
A profile on FormFest spearker’s Barry Roeder, Barabara Deffenderfer, Glenn Brown, and Izzie Hirschy-Reyes highlighting how the Bay Area Housing Finance Authority and its partners use AI and human-centered design to streamline paper housing applications.
Hear perspectives on topics including centering beneficiaries and workers in new ways, digital service delivery, digital identity, and automation.This video was recorded at the Digital Benefits Conference (BenCon) on June 14, 2023.
This paper introduces a framework for algorithmic auditing that supports artificial intelligence system development end-to-end, to be applied throughout the internal organization development lifecycle.
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT)
The guidelines for bias-free language contain both general guidelines for writing about people without bias across a range of topics and specific guidelines that address the individual characteristics of age, disability, gender, participation in research, racial and ethnic identity, sexual orientation, socioeconomic status, and intersectionality.
This paper argues that a human rights framework could help orient the research on artificial intelligence away from machines and the risks of their biases, and towards humans and the risks to their rights, helping to center the conversation around who is harmed, what harms they face, and how those harms may be mitigated.
