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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

The takeaway is not that one policy tool solves AI governance. Public managers need clearer guidance about which values policy should protect and where current texts remain silent. Open access: doi.org/10.1111/pad... #AIGovernance #PublicAdministration

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

The dominant primary strategies were information/capacity building (45%) and direct services/public projects (43%); regulation/enforcement accounted for 10%. Yet regulatory documents were more likely to explicitly address public values.

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

Military policies generally addressed public values at lower rates. The largest gaps were for individual rights (30.6% of nonmilitary policies vs. 20.2% of military policies) and the common good (27.8% vs. 17.9%).

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

Seven values were not explicitly addressed at all: responsiveness, local governance, citizen centeredness, a positive workplace, innovation, moral standards, and professionalism. The gaps are especially notable for the relationship between administrators and citizens.

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

Only 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).

Bar charts showing how often 21 public values appear in 120 sections or subsections of United States federal AI policy from 2020 through 2023.

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

We 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.

Conceptual framework: governance strategies address risks, risks lead to harms that governance seeks to mitigate, and protecting public values extends the focus of AI governance.

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Daniel S. Schiff @dschiff.bsky.social · 15/09/2026

New in Public Administration: with Ogadinma Enwereazu, @kaylynjschiff.bsky.social, @tylergirard.bsky.social, and Alexander Wilhelm, we examine which public values appear in US federal AI policymaking, and which are missing. 🧵 doi.org/10.1111/pad...

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Reposted by Daniel S. Schiff

The Governance and Responsible AI Lab (GRAIL) @grailcenter.bsky.social · 07/08/2026

Exciting expansion for AI governance data! 🌐 The AGORA archive—a collab between GRAIL and CSET at Georgetown—is broadening its multi-university partnership. MIT's AI Risk Initiative and Carnegie Mellon's EncyclopAIdia boost coverage of AI policies. 🔗 Read more: cset.georgetown.edu/article/expa...

cset.georgetown.edu

Expanded Collaboration to Boost Critical AI Governance Data and Tracking Tool | Center for Security and Emerging Technology

CSET is partnering with top universities to expand AGORA, its living collection of 1,000+ AI-related laws, regulations, and standards.

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Daniel S. Schiff @dschiff.bsky.social · 02/05/2026

Join us at AIES2026. And please support the community as a PC or Senior PC member!

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Reposted by Daniel S. Schiff

Lee Rainie @lrainie.bsky.social · 02/04/2026

NEW REPORT from the Imagining the Digital Future Center: In 160+ impassioned essays, global experts note AI is quickly becoming the invisible operating system of society. They urge an institutions-first strategy to help support human resilience. imaginingthedigitalfuture.org/reports-and-...

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

8/9 Strengthen auditing: • Independent accreditation bodies • Transparent audit results • Practical tools bridging principles-practice gap

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

7/9 Make governance agile: ✅ Living documents evolving with tech ✅ Rapid-response taskforces ✅ Regulatory sandboxes for testing

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

6/9 Layered adoption approach by orgs: • High-level frameworks (NIST AI RMF) • Domain guidelines (IEEE 7010) • Operational tools (model cards) Promotes scalability and adaptability.

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

5/9 🚀 The solution: Human-centered, harmonized, adaptive governance. A roadmap: global collaboration among ISO, IEEE, OECD. Use crosswalks to map overlaps and reduce redundancy.

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

4/9 Challenge 3: The "15 competing standards" problem. Proliferation creates decision paralysis. Organizations face patchwork frameworks without clear guidance.

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

3/9 Challenge 2: Voluntary standards without enforcement. Organizations cherry-pick compliance, creating superficial audits instead of meaningful accountability.

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

2/9 Challenge 1: Translating fairness, transparency into actionable standards is hard. These values are context-dependent. Standards risk oversimplifying or "ethics-washing" - leaving structural inequities unaddressed.

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Daniel S. Schiff @dschiff.bsky.social · 24/03/2026

1/9 🤔 Core problem: AI advances rapidly, but governance lags in fragmented systems. Challenges: • Redundancy & overlap • Decision paralysis • Ethical concepts lost in translation • Geopolitical divides

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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...

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Daniel S. Schiff @dschiff.bsky.social · 18/03/2026

4/4 AI governance will shape society—addressing inequality and climate impact if done right, entrenching power imbalances if done wrong. What trade-offs matter most to you? For policy practitioners and researchers. @purduepolsci.bsky.social @GRAILcenter.bsky.social dx.doi.org/10.2139/ssr...

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Daniel S. Schiff @dschiff.bsky.social · 18/03/2026

3/4 My analysis suggests hybrid models combining: • Centralized safety oversight • Decentralized innovation spaces • Broad societal goal integration Experimentation across jurisdictions will be essential. Forthcoming in Handbook on the Global Governance of AI.

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Daniel S. Schiff @dschiff.bsky.social · 18/03/2026

2/4 Current AI governance shows fragmentation everywhere: • EU centralizing with AI Act • US pursuing decentralized approaches • Monitoring ranges from strict to voluntary • Tech firms drive decisions while public input lags The pace of AI development only complicates things further.

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Daniel S. Schiff @dschiff.bsky.social · 18/03/2026

1/4 I examined 4 governance models to understand the possibilities: ✈️ Aviation: Centralized, safety-focused, but expensive 🌱 Organic standards: Decentralized, private-led, inconsistent 🌍 Climate policy: Shared, flexible, weak enforcement 💻 Open-source: Collaborative, adaptive, fragile

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Daniel S. Schiff @dschiff.bsky.social · 18/03/2026

AI 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...

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

5/5 The stakes: AI influences fundamental life outcomes. Without robust, transparent audits, we risk perpetuating harms and undermining trust. For governance folks: What's your biggest auditing challenge—technical gaps, regulatory clarity, or stakeholder engagement? doi.org/10.1177/205395

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

4/5 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.

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

3/5 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. 📈

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

2/5 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

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

1/5 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. 📊

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Daniel S. Schiff @dschiff.bsky.social · 11/03/2026

AI systems decide who gets hired, who gets loans, who receives healthcare. But who's auditing the AI? 🤖 Our new study explores the emerging field of AI ethics auditing—the people and processes trying to make AI accountable. @grailcenter.bsky.social doi.org/10.1177/205... 🧵

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

Published in Hastings Center Report. @purduepolsci.bsky.social @GRAILcenter.bsky.social onlinelibrary.wiley.com/doi/abs/10.... With Daniel Susser, Sara Gerke, Laura Y. Cabrera, I. Glenn Cohen, & team

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

Synthetic data should complement real-world data, not replace it. The choice ahead: Will we use this technology to bridge healthcare gaps or deepen inequities? For governance teams & researchers working on AI in healthcare—curious what you're seeing? #SyntheticData #AIinHealthcare #Bioethics

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

We argue synthetic data isn't a magic fix—it's a powerful tool that demands robust safeguards 🛡️ Key needs: • Standards for accuracy & reliability • Privacy protections • Transparent policies • Continued investment in diverse, real-world datasets

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

But the risks are real: • Accuracy issues for rare disease algorithms • Potential privacy leaks despite synthetic nature • Bias amplification from flawed source data • Regulatory gaps exploiting "non-identifiable" status • Justice concerns about sidelining real-world diversity

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

What synthetic data promises: • Privacy protection through artificial datasets • Inclusive modeling of rare diseases & underserved groups • Enhanced AI training capabilities • Scalable research opportunities The potential is substantial ⚡

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

Enter synthetic data: AI-generated datasets that mimic real-world patterns without containing actual patient information Sounds perfect—private, inclusive, scalable. But our analysis in Hastings Center Report reveals significant ethical complexities 🚨

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

The challenge: Healthcare research is data-rich but insight-poor 📊 Privacy laws, demographic gaps, and underrepresentation of rare conditions prevent researchers from fully utilizing available EHRs, public datasets, and lab studies

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Daniel S. Schiff @dschiff.bsky.social · 03/03/2026

Synthetic data promises to revolutionize healthcare research—solving privacy issues, modeling rare diseases, expanding equity. But it's also an ethical minefield that demands careful navigation 🧵 onlinelibrary.wiley.com/doi/abs/10....

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#8 For policy practitioners, governance teams, and org leaders: curious what you're seeing in your hiring? Paper below 👇 @purduepolsci.bsky.social @GRAILcenter.bsky.social doi.org/10.1109/TTS...

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#7 AI ethics and governance aren't "nice-to-haves"—they're becoming non-negotiable pillars of responsible AI development. As industries adopt AI at scale, these roles will define how society benefits from this technology ⚖️

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#6 What's driving alignment? New AI regulations demand compliance. Employers recognize public trust is critical for AI adoption. Universities race to create relevant programs. More than 100K professionals needed annually

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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 🏦

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#4 Demand is surging 🚀 AI ethics roles grew from 35K in 2018 to 109K in 2022. Governance roles hit 96K in 2022. Even as overall AI hiring dipped in 2023, these roles remained stable. Results suggest sustained market need

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#3 Key finding: AI ethics ≠ AI governance. Employers seek distinct skills: 🔹 Ethics: Data privacy, bias mitigation, critical thinking 🔹 Governance: Risk management, policy development, leadership Both require interdisciplinary knowledge

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#2 Our study analyzed 4.4M+ AI-related job postings to uncover trends in demand for AI ethics (fairness, transparency) and AI governance (regulatory compliance, risk management) skills. Published in IEEE Transactions on Technology and Society

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

#1 We're seeing an "AI skills gap"—a shortage of professionals equipped with both technical expertise AND the ability to handle ethical dilemmas and regulatory challenges. AI is transforming industries, but with great power comes great responsibility 📊

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Daniel S. Schiff @dschiff.bsky.social · 24/02/2026

The 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...

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Daniel S. Schiff @dschiff.bsky.social · 17/02/2026

6/7 🔬 Next steps: Validation beyond Western university samples, workplace applications, and cross-cultural AI literacy research. With Arne Bewersdorff and Marie Hornberger. Thanks to Google Research for funding a portion of this work @purduepolsci.bsky.social @GRAILcenter.bsky.social

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