Responsible AI in Practice: Ethics, Governance and Regulation Inspired by Jacques Pommeraud

The global conversation on artificial intelligence has shifted rapidly from pure innovation to responsible innovation. In a recent interview presented by the Cercle de Giverny, Jacques Pommeraud explores how organizations can harness AI while staying aligned with ethical principles, sound governance and fast-evolving regulatory demands.

This article distills the key themes from that discussion and connects them with best practices in AI ethics, AI governance, AI regulation and real-world implementation. If you are a business leader, policymaker or practitioner, you will find practical guidance on how to turn Responsible AI from a slogan into a concrete operating model that supports trust, performance and long-term value creation.

Why Responsible AI Matters Now

AI is no longer confined to labs or experimental pilots. It drives decisions in credit scoring, recruitment, healthcare, public services, security and everyday consumer experiences. As Pommeraud highlights, this scale and speed of adoption brings both exceptional opportunities and serious responsibilities.

The key societal risks repeatedly emphasized in Responsible AI debates include:

Bias and discrimination that may unfairly impact certain groups.
Lack of transparency and opaque decision-making in critical domains.
Privacy and data protection concerns when large datasets are used.
Accountability gaps when it is unclear who is responsible for AI decisions.

Addressing these risks is not just about compliance. Done well, Responsible AI:

Builds trust with customers, citizens and regulators.
Reduces operational and legal risk.
Improves model quality and robustness over time.
Strengthens your organization’s reputation as a reliable innovator.

Core Ethical Principles of Responsible AI

Across frameworks promoted by regulators, standard setters and industry bodies, a consistent set of ethical pillars appears. The interview with Jacques Pommeraud echoes these foundations and stresses their relevance for both corporate and public sector adoption.

Fairness and Bias Mitigation

Fairness means AI systems should not systematically disadvantage individuals or groups on the basis of protected attributes, such as gender, ethnicity, disability or age, unless there is a clear, lawful and justified reason.

Practical actions include:

Checking for representativeness and quality of training data.
Running bias tests on model outputs for different demographic groups.
Implementing policies to avoid proxy variables that can indirectly encode sensitive information.
Documenting trade-offs between different fairness metrics and business objectives.

Transparency and Explainability

Users, customers, citizens and regulators increasingly expect to understand how AI shapes outcomes that matter to them. Transparency has several layers:

System-level transparency about where and how AI is used.
Model transparency regarding data sources, assumptions and limitations.
Individual explanations for decisions that affect specific people.

Pommeraud underlines that explainability is not only a technical challenge. It is also an organizational capability: teams must learn to communicate AI decisions in language that different stakeholders can understand and act upon.

Privacy and Data Protection

AI depends on data, but data belongs to people or organizations who expect control and safeguards. Responsible AI requires:

Lawful and transparent collection and use of data.
Clear purpose limitation and minimization of unnecessary data.
Appropriate security and access controls throughout the lifecycle.
Data governance practices that respect local and international privacy regulations.

Accountability and Human Oversight

An AI system cannot be held accountable; the organizations and people who develop, deploy and manage it can. Accountability means that roles and responsibilities are clearly defined, and that there are mechanisms to detect, escalate and remediate issues when they arise.

Effective oversight involves:

Maintaining a clear chain of responsibility for each AI use case.
Ensuring human-in-the-loop or human-on-the-loop review where appropriate.
Providing avenues for appeal and redress when individuals contest AI decisions.

Safety, Robustness and Reliability

Responsible AI systems should perform reliably, even under stress or in changing environments. This principle is key in safety-critical domains like healthcare, transport, energy and public safety.

Conduct rigorous testing before deployment.
Monitor for drift in data and performance over time.
Prepare fallback procedures when models fail or behave unexpectedly.

A Risk-Based Approach to AI Governance

One of the central ideas emphasized in the Cercle de Giverny discussion is that AI governance should be risk-based. Not every AI use case carries the same level of impact, so controls should be proportional to potential harm and societal stakes.

Mapping Use Cases and Risk Levels

Start by building an inventory of AI systems and classifying them according to their potential impact on individuals, society and the organization. Typical dimensions include:

Impact on rights and opportunities (for example, access to jobs, credit, healthcare or public benefits).
Operational and safety risks (physical harm, financial loss or major service disruption).
Reputational and compliance exposure (media scrutiny, regulatory sanctions or public trust).

The table below illustrates how organizations can align risk levels with governance requirements.

Risk level	Typical examples	Recommended governance controls
Low	Content recommendation, internal productivity tools	Basic documentation, opt-out options, light monitoring
Medium	Customer support automation, marketing personalization	Formal risk assessment, bias checks, human escalation paths
High	Credit scoring, hiring, medical decision support, public services eligibility	Multi-disciplinary review, strict testing, continuous monitoring, enhanced human oversight

Designing an AI Governance Operating Model

Pommeraud advocates for structured governance that fits existing corporate or public-sector decision-making. Instead of creating isolated structures, Responsible AI should be woven into enterprise risk management, compliance and digital transformation initiatives.

Typical elements of an AI governance operating model include:

An AI ethics or Responsible AI committee that sets policy and reviews high-risk use cases.
Clear roles and responsibilities for data scientists, engineers, business owners, legal and compliance teams.
Standardized templates and checklists for risk assessments, model documentation and approvals.
A central AI register listing key systems, purpose, data sources and risk ratings.

Building a Responsible AI Framework: Practical Steps

Transforming ethical principles into day-to-day practice requires a combination of technical and organizational safeguards. The discussion led by the Cercle de Giverny highlights the need for concrete, repeatable processes that teams can follow.

Technical Safeguards

Technical measures help embed Responsible AI directly into development, deployment and operations.

Data governance for training and test sets, including quality checks, lineage tracking and consent management.
Model risk assessment templates that flag sensitive features, potential harms and necessary controls.
Bias and fairness testing tools integrated into model evaluation pipelines.
Explainability techniques such as feature importance analysis or local explanations where appropriate.
Monitoring and logging to detect performance degradation, anomalous behavior or misuse.

Organizational Safeguards

Even the most advanced technical solutions cannot replace clear policies, training and culture.

Responsible AI policies that set expectations for all AI projects, aligned with regulations and corporate values.
Training and awareness for leadership, developers and business users on ethics, bias, privacy and regulatory expectations.
Stakeholder engagement, including feedback loops with users, customers, employees and impacted communities.
Vendor and third-party management, ensuring that external AI tools meet the same Responsible AI criteria as internal systems.
Incident response playbooks describing how to react if an AI system causes harm, produces biased outcomes or suffers a security breach.

Metrics for Fairness, Transparency and Accountability

What gets measured gets managed. Pommeraud underscores that organizations should not rely solely on ad hoc judgement. Instead, they need metrics and indicators that track whether AI systems perform in line with ethical and regulatory expectations.

Measuring Fairness

No single fairness metric fits all contexts, but organizations can adopt a small set that reflects their use cases. Examples of what teams often evaluate include:

Differences in error rates (false positives, false negatives) between demographic groups.
Differences in approval or selection rates across protected attributes.
Consistency of model performance across regions, segments or time periods.

These metrics should be reviewed regularly, not only at launch. As data and behavior evolve, fairness profiles can shift, especially in dynamic environments.

Measuring Transparency and Explainability

Transparency is harder to express as a single number, but organizations can track:

Whether each high-risk model has complete documentation (purpose, training data, known limitations).
The proportion of models that provide individual-level explanations when making decisions about people.
Feedback from users on whether explanations are understandable and actionable.

Tracking Accountability and Governance

To ensure that governance is more than a paper exercise, organizations can monitor indicators such as:

The percentage of high-risk use cases reviewed by a governance body before deployment.
The number and severity of incidents or complaints related to AI systems.
Response times for incident investigation and remediation.

Responding to Regulatory Pressures

Regulatory expectations around AI are rising across regions. Initiatives in fields such as data protection, product safety, financial services and digital regulation increasingly reference AI-specific obligations. The themes discussed by Jacques Pommeraud align closely with this trend: organizations must be ready for a world where Responsible AI is a legal requirement, not just a nice-to-have.

To prepare, organizations can:

Map existing AI use cases to current and emerging regulations applicable in their markets.
Integrate AI risk into compliance and legal reviews, rather than treating it as a separate topic.
Ensure documentation and traceability of design choices, datasets and testing, creating an audit trail.
Adopt a privacy-by-design and ethics-by-design mindset from project inception.

Public-sector bodies face an additional responsibility: they often set the tone for society. By building strong AI governance into public services, they can model best practices for the wider ecosystem and support citizen trust.

Engaging Stakeholders and Society

Responsible AI is not only a technical or legal project; it is a social project. The Cercle de Giverny interview emphasizes the importance of dialogue and co-construction with stakeholders who are affected by AI systems.

Effective stakeholder engagement can include:

Consultations with employees and frontline staff when introducing AI-driven tools that affect their work.
Input from customers, patients, citizens or users about transparency and fairness expectations.
Discussions with civil society organizations, academics and experts to stress-test assumptions and identify blind spots.

This type of engagement is not just risk mitigation; it is a powerful way to design better, more accepted solutions and to anticipate questions regulators and the public may raise.

From Principles to Practice: An Action Plan for Leaders

The Responsible AI agenda can feel overwhelming, but Pommeraud’s guidance, framed by the Cercle de Giverny discussion, can be distilled into a practical roadmap. The goal is to move quickly from abstract principles to concrete, measurable progress.

A 10-Step Responsible AI Roadmap

Take stock: Build an inventory of AI systems and classify them by business criticality and societal impact.
Define principles: Adopt a clear set of Responsible AI principles covering fairness, transparency, privacy, safety and accountability.
Assign ownership: Nominate an executive sponsor and create or adapt a governance body to oversee AI risks.
Set policies: Develop Responsible AI policies, guidelines and approval workflows that apply to all new AI projects.
Introduce safeguards: Implement technical and organizational controls proportional to the risk level of each use case.
Measure impact: Define metrics for fairness, transparency and incident management; include them in regular reporting.
Train teams: Provide training for data scientists, engineers, product owners and executives on ethics and regulation.
Engage stakeholders: Create channels for users, employees and external stakeholders to give feedback on AI systems.
Prepare for regulation: Monitor relevant regulatory initiatives and ensure your governance framework can adapt.
Iterate and improve: Review the Responsible AI program at least annually, learning from incidents, audits and new standards.

Responsible AI, when treated as a strategic investment rather than a constraint, becomes a powerful driver of sustainable innovation, trust and long-term competitiveness.

Conclusion: Turning Responsible AI into a Competitive Advantage

The interview with Jacques Pommeraud, presented by the Cercle de Giverny, reinforces a crucial message for our time: AI ethics, governance and regulation are not barriers to progress. They are the foundations for AI that genuinely benefits organizations, citizens and society.

By adopting a risk-based governance model, engaging stakeholders, implementing robust technical and organizational safeguards, and aligning with emerging regulatory frameworks, organizations can:

Innovate with confidence and speed.
Protect the people and communities they serve.
Strengthen trust with regulators, partners and the public.
Build AI capabilities that stand the test of time.

Responsible AI is not a one-off project. It is a continuous journey that blends technology, ethics, governance and societal dialogue. Leaders who commit early and act decisively will not only reduce risk; they will shape the standards by which successful AI adoption is measured in both the corporate and public sectors.