Daniel S. Schiff @dschiff.bsky.social · 15/09/2026Only 34% (41 of 120) addressed at least one public value. Individual rights appeared most often (23.3%), followed by the common good (20.8%), sustainability (15.8%), and human dignity, accountability, and openness (13.3% each). 110
Daniel S. Schiff @dschiff.bsky.social · 15/09/2026We analyze 120 sections or subsections of federal laws and regulations enacted from 2020 through 2023. Using AGORA, compiled by @csetgeorgetown.bsky.social and Purdue GRAIL, we connect the AI risks and harms in each text to a public values framework. 111
Daniel S. Schiff @dschiff.bsky.social · 24/03/20265/9 🚀 The solution: Human-centered, harmonized, adaptive governance. A roadmap: global collaboration among ISO, IEEE, OECD. Use crosswalks to map overlaps and reduce redundancy. 100
Daniel S. Schiff @dschiff.bsky.social · 24/03/20264/9 Challenge 3: The "15 competing standards" problem. Proliferation creates decision paralysis. Organizations face patchwork frameworks without clear guidance. 100
Daniel S. Schiff @dschiff.bsky.social · 24/03/2026🚨 AI governance is fragmented chaos. Over 500 standards, overlapping frameworks, contradictory regulations create a maze. But there's a roadmap to harmonize this and build human-centered AI governance 🧵 link.springer.com/rwe/10.1007... 110
Daniel S. Schiff @dschiff.bsky.social · 18/03/2026AI governance faces 5 fundamental tensions: centralized vs. decentralized control, robust vs. minimal enforcement, adaptive vs. enduring rules. How do we navigate these trade-offs? 🤖 My new paper draws lessons from aviation, climate & open-source governance 👇 dx.doi.org/10.2139/ssr... 320
Daniel S. Schiff @dschiff.bsky.social · 11/03/20264/5 Auditors face regulatory ambiguity, data governance gaps, and interdisciplinary friction between tech, legal, and leadership teams. Yet they're ecosystem builders—translating vague laws into actionable frameworks and pushing organizations toward better AI governance. 100
Daniel S. Schiff @dschiff.bsky.social · 11/03/20263/5 Key finding: Most audits focus narrowly on technical metrics. Broader impacts on vulnerable communities? Often sidelined. Public reporting of audit results? Almost nonexistent. Transparency and stakeholder engagement remain major gaps. 📈 100
Daniel S. Schiff @dschiff.bsky.social · 11/03/20262/5 What's driving AI auditing growth? 🔹 Regulation (EU AI Act, NIST frameworks) 🔹 Reputation management (avoiding biased AI headlines) 🔹 Competitive strategy (trustworthy AI advantage) The ecosystem spans internal teams, Big Four firms, specialized startups. @purduepolsci.bsky.social 120
Daniel S. Schiff @dschiff.bsky.social · 11/03/20261/5 We interviewed 34 AI ethics auditors across 23 organizations in 7 countries. Published in Big Data & Society. @bigdatasociety.bsky.social The field borrows from financial auditing: planning, validating, analyzing risks, reporting. But it's still figuring out what success looks like. 📊 100
Daniel S. Schiff @dschiff.bsky.social · 24/02/2026#5 Finance and Information industries dominate demand, with AI ethics/governance roles growing fastest there. Highly regulated sectors can't afford ethical lapses as AI adoption scales 🏦 100
Daniel S. Schiff @dschiff.bsky.social · 24/02/2026The AI job market is evolving beyond coding. Employers now demand AI ethics and governance skills at unprecedented rates. Our analysis of 4M+ job postings from 2018-2023 reveals what's driving this shift 🧵 doi.org/10.1109/TTS... 121
Daniel S. Schiff @dschiff.bsky.social · 17/02/20263/7 ✅ Results show AILIT-S delivers: • ~5 minutes completion time (vs 12+ for full version) • 91% congruence with comprehensive assessment • Strong performance for group-level analysis Trade-off: slightly lower individual reliability (α = 0.61 vs 0.74) 100
Daniel S. Schiff @dschiff.bsky.social · 17/02/20262/7 📊 AILIT-S covers 5 core themes: • What is AI? • What can AI do? • How does AI work? • How do people perceive AI? • How should AI be used? Special emphasis on technical understanding—the foundation of true AI literacy. 100
Daniel S. Schiff @dschiff.bsky.social · 17/02/20261/7 ⚡ The challenge: Existing AI literacy tests take 12+ minutes, making them impractical for large-scale assessment. Our solution distills a robust 28-item instrument into 10 key questions—validated with 1,465 university students across the US, Germany, and UK. 100
Daniel S. Schiff @dschiff.bsky.social · 11/02/2026🚧 Major challenges we identified: • Keeping up with AI's rapid evolution • Teaching abstract concepts to diverse audiences • Shortage of trained educators • Misalignment between teaching goals & assessment methods 110
Daniel S. Schiff @dschiff.bsky.social · 11/02/2026🔑 Key finding: The best programs go beyond "rules for algorithms." They tackle societal issues—bias, fairness, privacy, social justice. Higher-ed leads with comprehensive curricula, but K-12 efforts are still catching up. 100
Daniel S. Schiff @dschiff.bsky.social · 11/02/2026📚 We analyzed content, pedagogy & assessment practices across AI ethics education (2018-2023). The results? A field full of promise but grappling with fundamental challenges in what to teach, how to teach it, and whether students are actually learning. 100
Daniel S. Schiff @dschiff.bsky.social · 06/02/20263/7 Using clustering techniques, we identified 3 student profiles: 🚀 AI Advocates (48%): Tech-savvy, confident, excited 🤔 Cautious Critics (21%): Skeptical, low confidence, minimal use ⚖️ Pragmatic Observers (31%): Neutral attitudes, moderate interest 100
Daniel S. Schiff @dschiff.bsky.social · 06/02/20262/7 Key findings suggest: ✅ Using AI tools (like ChatGPT) boosts interest ✅ Positive attitudes predict higher engagement ✅ Interest acts as the bridge connecting attitudes, literacy, and confidence Our validated path model: 100
Daniel S. Schiff @dschiff.bsky.social · 27/01/20264/7 📚 Over 70% of students across all countries haven't taken an AI-related course. Germany leads with 26.5% participation, but that's still inadequate. Most learning happens informally through tools like ChatGPT, but few dive deeper into foundational concepts. 100
Daniel S. Schiff @dschiff.bsky.social · 27/01/20262/7 📊 Germany leads in actual AI knowledge, outscoring UK and US students. The reason? Better integration of AI coursework. But the US shows highest self-confidence in AI skills, while UK students express the most skepticism toward AI. 100
Daniel S. Schiff @dschiff.bsky.social · 27/01/2026Are university students ready for an AI-driven world? 🤔 Our study of 1,465 students across Germany, UK, and US reveals significant gaps in AI literacy among future professionals. Published in Computers in Human Behavior: Artificial Humans 🧵 www.sciencedirect.com/science/art... 111
Daniel S. Schiff @dschiff.bsky.social · 20/01/20265/7 Baseball Hall of Fame (2009-2016): Writers debated PED-linked players. SCA uncovered growing rift: ⚾ Pro-forgiveness coalition ⚾ Purist coalition Schism reinforced decision silos over time 100
Daniel S. Schiff @dschiff.bsky.social · 20/01/20264/7 Wikipedia 2013: Visual Editor rollout sparked editor rebellion. SCA revealed two warring coalitions: 🛠️ Technical design focus 🌍 Cultural norms focus Leadership missed these divisions—strategy disaster followed 100
Daniel S. Schiff @dschiff.bsky.social · 20/01/20262/7 SCA works like smarter preference-based clustering. Groups actors by maximizing shared utility. Result? Clear maps of internal divisions, even with sparse data 📊 100
Daniel S. Schiff @dschiff.bsky.social · 14/01/20268/9 🚨 By 2022, partisan cleavages hardened: • Republicans: Pro-AI in defense, law enforcement • Democrats: Focused on risks, equity concerns This mirrored broader ideological divides over regulation and government intervention. 100
Daniel S. Schiff @dschiff.bsky.social · 14/01/20264/9 🔑 Trigger 1—Problem definitions: Early AI framing was broad: transparency, privacy, ethics. But when linked to racial equity or redistribution, partisan divides flared. • Dems: Equity-focused reforms • GOP: Industry self-regulation 100
Daniel S. Schiff @dschiff.bsky.social · 14/01/20263/9 🤔 But by 2022, bipartisan windows narrowed. We identify 4 key triggers of polarization: 1️⃣ Competing problem definitions 2️⃣ Divergent policy tools 3️⃣ Stakeholder dynamics 4️⃣ Strategic "subsystem shopping" 100
Daniel S. Schiff @dschiff.bsky.social · 14/01/20262/9 💡 Early wins focused on "soft governance"—transparency requirements, research initiatives—mostly avoiding polarizing debates. 100
Daniel S. Schiff @dschiff.bsky.social · 09/01/20264/4 The path forward 🛤️ We need to: • Integrate ethics into core STEM curricula • Leverage peer-based learning (it works!) • Connect coursework to real challenges • Rethink "STEM readiness" Technical competence without social consciousness isn't enough. @purduepolsci.bsky.social @GRAILcenter.bsky 020
Daniel S. Schiff @dschiff.bsky.social · 09/01/20263/4 Students described their STEM courses as: • Too technical (little ethics integration) • Disconnected from societal dimensions • Career-focused rather than impact-focused Meanwhile, STEM professionals shape our future—from AI to climate tech ⚡ 110
Daniel S. Schiff @dschiff.bsky.social · 09/01/20261/4 📊 The data are stark: • Professional Connectedness scores dropped significantly (5.65 → 5.43, p < 0.001) • Students increasingly prioritized salary over societal impact • Self-efficacy to drive social change declined Published in International Journal of STEM Education 100
Daniel S. Schiff @dschiff.bsky.social · 04/08/20255/7 📈 Finding 2 (The Catch): This public influence ONLY holds for the innovation frame. When the public discusses AI's economic potential, policymakers listen. When the public discusses AI ethics or security, we see no statistically significant influence on policymakers. 100
Daniel S. Schiff @dschiff.bsky.social · 04/08/20254/7 📈 Finding 1: Public attention does predict policymaker attention. The time-series analysis (ARIMA + VAR) shows that a one standard deviation increase in public tweets about AI is associated with a 22.4% increase in Congressional messaging on AI that same week. 100
Daniel S. Schiff @dschiff.bsky.social · 04/08/20252/7 I focused on three dominant ways people frame AI: 📈 Innovation: AI as a driver of economic growth & productivity. 🙏 Ethics: AI's impact on fairness, rights, bias, and safety. ⚔️ Competition: AI in the context of the US-China race. 100
Daniel S. Schiff @dschiff.bsky.social · 11/07/20258/12 Crucially, this latter effect was especially pronounced for non-White students, suggesting peer interaction can be a key driver of equity-related development. Another note: Women have higher SR scores than men consistently 110
Daniel S. Schiff @dschiff.bsky.social · 11/07/20254/12 Worse, when asked about future job priorities, the importance of "salary" went up, while the importance of "helping people" went down. 110
Daniel S. Schiff @dschiff.bsky.social · 11/07/20253/12 We used the Generalized Professional Responsibility Assessment (GPRA) to measure changes in their attitudes. 📉 Finding 1: Stagnant attitudes & shifting priorities. Over two years, students' social responsibility scores remained flat. 110