In Practice Brief | From Regulation to Responsibility: Embedding Accountability by Design
In-Practice Briefs are TL Advisory’s applied governance series—concise, research-based analyses connecting institutional leadership, technology, and accountability. Each brief distills emerging policy trends into practical frameworks for mission-driven organizations.
This inaugural brief, From Regulation to Responsibility, establishes the foundation of TL Advisory’s approach: translating evolving regulatory standards into governance structures that sustain trust over time.
Each In Practice Brief from TL Advisory examines governance through three stages—Perspective, Practice, and Proof—guided by the principles of Inquiry, Integrity, and Impact. Together, they form a continuous cycle that translates understanding into accountability and accountability into measurable trust.
Executive Summary
Artificial intelligence now shapes decisions in nearly every regulated sector—from university admissions and hospital diagnostics to consumer lending and government procurement. Yet even as innovation accelerates, the governance structures that sustain trust have lagged behind. Around the world, governments are racing to establish new guardrails: the European Union’s AI Act, the Organisation for Economic Co-operation and Development’s (OECD) AI Principles,¹ and the U.S. National Institute of Standards and Technology’s (NIST) AI Risk Management Framework.² Still, rules alone cannot produce responsibility. Compliance defines the floor; governance builds the foundation.
At TL Advisory, we view governance not as a reaction to technology but as the design principle that makes innovation sustainable. This inaugural Executive Brief examines how leaders in education, finance, government contracting, and healthcare are moving from regulation to responsibility—embedding accountability by design into their institutions.
Regulation defines boundaries. Governance defines behavior.
I. Perspective | Regulatory and Policy Considerations
Each industry interprets responsible innovation through its own framework. Yet across domains, three elements recur: clarity of authority, transparency of process, and continuity of review. Four sectors illustrate how regulation can evolve into governance.
Education — Mission Alignment and Data Ethics
The U.S. Department of Education (DOE) released its Guiding Principles for Artificial Intelligence in Education (2024),³ emphasizing fairness, transparency, and informed adoption. These are not technical standards but leadership commitments—ensuring AI deployment aligns with institutional mission and community values. When universities establish cross-functional AI councils and publish transparency reports, they transform compliance exercises into trust-building practices. Responsibility becomes culture when ethics are documented, discussed, and designed into daily decisions.
Finance and Consumer Protection — Fairness and Explainability
The Office of the Comptroller of the Currency (OCC) and the Federal Trade Commission (FTC) have advanced guidance on algorithmic fairness and model-risk management.⁴ ⁵ The OCC’s 2023 bulletin highlights that automation without accountability can erode prudential oversight, while the FTC underscores the importance of explainability in consumer decisions. Their message applies far beyond finance: institutions that adopt AI must be able to trace how decisions are made and demonstrate that those decisions are fair. Explainability is not a regulatory artifact—it is a governance discipline. Whether managing student analytics or clinical data, leaders who demand transparency from systems and vendors preserve both accountability and public confidence.
Government Contracting and Public Procurement — Stewardship Through Transparency
Public-sector innovation depends on public trust. One federal model—the Buy AI Responsibly Framework (2025), developed by the Office of Management and Budget (OMB) and the General Services Administration (GSA)—outlined how agencies could integrate governance into AI acquisition through planning, vendor transparency, and periodic evaluation.⁶ While the current federal posture emphasizes accelerated adoption, the framework’s core principles remain instructive for any organization seeking to embed accountability into innovation. Together with the NIST AI Risk Management Framework, it illustrates how governance design can translate risk management into operational trust.
Healthcare — Data Integrity and Patient Trust
Healthcare offers perhaps the clearest demonstration of accountability by design. The Health Insurance Portability and Accountability Act (HIPAA) establishes the baseline for data privacy, while the U.S. Food and Drug Administration’s (FDA) AI/ML Action Plan (2021) sets expectations for transparency and validation.⁷ Hospitals have built on these requirements by forming AI ethics boards that evaluate algorithms before deployment and publish safety reviews afterward. These boards exemplify continuous governance: policies that live, evolve, and learn alongside technology. What began as compliance now functions as institutional conscience.
Across all four industries, regulation serves as a catalyst—but leadership determines whether the result is bureaucracy or trust. The organizations making progress treat governance as ongoing practice rather than static program.
II. Practice | Governance and Strategic Integration
Regulation defines external obligations; governance integrates them internally. Fragmented oversight and vendor dependence remain common failures. Institutions often delegate responsibility to compliance or IT teams without connecting those functions to strategic core. The corrective is integration. Cross-functional AI governance councils—comprising legal, technology, academic, and ethics perspectives—review new tools, recommend controls, and issue periodic public updates. These mechanisms create governance continuity, ensuring accountability survives leadership transitions and technological change alike. Integration, not compliance, is the metric of responsible innovation.
III. Proof | Comparative Insight
Globally, governance is converging toward a shared vocabulary of accountability. The OECD’s AI Principles and the emerging International Organization for Standardization (ISO) standard for AI management systems (ISO 42001) both emphasize documentation, explainability, and proportionality. These same elements now appear in sectoral frameworks across the United States:
Universities publish AI-use registers to maintain transparency with students and faculty.
Financial institutions conduct fairness audits under OCC and FTC guidance.
Public agencies continue to draw on earlier procurement models to inform risk reviews and public reporting.
Hospitals integrate algorithmic audits into quality-assurance programs.
Different settings, same conclusion: transparency is not ancillary to governance—it is governance.
IV. Recommendations | Algorithmic Due Diligence Cycle
TL Advisory’s Algorithmic Due Diligence Model provides a structure for embedding accountability into operations. The model rests on a continuous four-step cycle:
Stakeholder Mapping — Identify who is affected by AI deployment, who oversees it, and who benefits from its outcomes. Governance begins with clear visibility of roles and relationships.
Interpret and Internalize — Translate external frameworks (DOE, OCC, FTC, OMB, FDA) into internal principles.
Implement — Establish governance bodies empowered to evaluate new tools and report directly to executive leadership.
Iterate — Conduct annual impact reviews, publish summaries, and update governance structures as technology evolves.
The result is a feedback loop that transforms policy into practice and keeps accountability proportional to innovation.
V. Leadership as Governance
Technology moves faster than law—but not faster than leadership. From classrooms to clinics, banks to public agencies, the same truth holds: oversight works only when it is designed in from the start. Leaders who embrace governance as a design discipline—not a compliance burden—ensure that innovation strengthens, rather than strains, institutional trust. As regulatory frameworks evolve, they will continue to rely on examples set by organizations that lead with transparency and integration. Accountability is the architecture of trust—and the enduring measure of good governance.
That is the work TL Advisory was created to advance.
Download the In Practice Brief (PDF)
Sources & Citations
¹ Organisation for Econ. Co-operation & Dev., “OECD AI Principles” (2019), https://oecd.ai/en/ai-principles.
² Nat’l Inst. of Standards & Tech., “Artificial Intelligence Risk Management Framework 1.0” (2023), https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf.
³ U.S. Dep’t of Educ., “Guiding Principles for AI in Education” (2024), https://tech.ed.gov/files/2024/04/AI_Guiding_Principles.pdf.
⁴ Office of the Comptroller of the Currency, “OCC Bulletin 2023-24: Model Risk Management Principles for AI” (2023), https://occ.gov/news-issuances/bulletins/2023/bulletin-2023-24.html.
⁵ Fed. Trade Comm’n, “Using Artificial Intelligence and Algorithms” (2021), https://www.ftc.gov/business-guidance/blog/2021/04/using-artificial-intelligence-algorithms.
⁶ Office of Mgmt. & Budget & Gen. Servs. Admin., “Buy AI Responsibly Framework” (2025), https://ai.gov/buy-responsibly-framework/.
⁷ U.S. Food & Drug Admin., “Artificial Intelligence and Machine Learning (AI/ML) Action Plan” (2021), https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device.