Artificial intelligence (AI) technologies are increasingly adopted into everyday life, with most investment and development concentrated in the U.S. In response to rapid AI integration and scant federal guidelines, U.S. states have formed AI committees charged with studying AI-related societal trade-offs. We analyzed the 18 existing state-level AI committee reports to understand how policymakers discuss AI-related benefits and risks. We then compared the risks surfaced by policymakers to an established taxonomy of AI risks aggregated from literature and examined how policymakers’ concerns align—or misalign—from those of HCI scholars. These insights provide important mileposts for shaping currently ongoing policy initiatives and future research. Our findings reveal important gaps: while committees invoke responsible AI, their framings often omit broader socio-technical concerns emphasized in HCI. We discuss opportunities for HCI to support socio-technical perspectives, employ participatory design, and close the gap between research and policy.

When designing and analyzing a study, researchers must navigate a large space of methodological decisions, or “researcher degrees of freedom.” If these choices are not preregistered or transparently reported, they can increase the risk of inflated false-positive rates and exaggerated effect sizes, undermining scientific credibility. Drawing on psychology research that characterizes these degrees of freedom, we create a protocol for scoring how hypotheses are reported in the HCI literature (ReportDoF). We manually apply ReportDoF to 100 hypotheses from HCI texts authored between 2015-2025, including both preregistrations and papers. Based on this experience, we contribute an LLM workflow and proof-of-concept interactive interface (RDoFlow) that applies ReportDoF to new texts, enabling large-scale analysis of the composition and quality of reported analysis specifications. For example, RDoFlow reveals that HCI research more frequently tests multiple dependent variables for a single hypothesis than psychology research does—a practice that increases the risk of false positives.

Wildfire and forest management increasingly rely on geospatial technologies, i.e., data and tools contributing to the geographic mapping and analysis of the Earth, to inform measures for the control of wildfires. Nevertheless, challenges arising from domain experts adopting these complex, non-intuitive technologies are not well understood. We interviewed 12 participants in wildfire and forest management to explore the technical and socio-technical nature of these challenges, revealing that (1) knowledge and data are fragmented across stakeholders, ranging from governmental agencies to small landowners. This fragmentation causes participants to (2) struggle in sharing knowledge and expertise. Participants (3) voice concerns about model bias since decisions informed by geospatial technologies can have far-reaching impacts. Yet, they (4) face barriers engaging people most impacted by these decisions. We detail an HCI research agenda that includes: exploring opportunities to connect stakeholders and sharing knowledge, standardizing decision-making, and engaging local communities.