Responsible AI in Practice: Insights from Jacques Pommeraud’s Cercle de Giverny Interview

The rise of artificial intelligence is no longer a distant prospect. It is a daily reality, reshaping how governments design policy, how companies compete, how developers build products, and how citizens experience public and private services. In this context, Responsible AI has moved from a niche topic to a boardroom, cabinet and engineering priority.

In an in-depth, on-camera interview presented by the Cercle de Giverny and shared on YouTube, Jacques Pommeraud explores how Responsible AI, ethics and governance can be translated into concrete decisions and operating models. The discussion touches on transparency, accountability, regulation, corporate responsibility, implementation challenges, stakeholder engagement and practical frameworks that leaders can use today.

This article distills those themes into an actionable guide for policymakers, business leaders, developers and civil-society actors who want to adopt AI in a way that is both ambitious and responsible.

Why Responsible AI Is Now a Strategic Imperative

Responsible AI is often framed as a defensive exercise about risk and compliance. The conversation around Jacques Pommeraud’s interview shows a more expansive perspective: when done well, Responsible AI is a strategic enabler of innovation and trust.

• Trust as a competitive advantage– Organizations that can demonstrate robust governance, fairness and transparency are more likely to win the confidence of customers, regulators, employees and partners.
• Faster, smoother adoption– Clear safeguards and accountability reduce internal resistance, making it easier to rollout AI in sensitive domains such as finance, health, public services or HR.
• Future-proofing against regulation– As regulatory frameworks emerge, a solid Responsible AI foundation reduces the cost and disruption of compliance later.
• Better products and policies– Ethical reflection often uncovers blind spots, leading to more inclusive, robust and user-centric AI systems.

In the Cercle de Giverny discussion, Responsible AI is positioned not as a brake on innovation, but as the operating system that makes sustainable, large-scale adoption possible.

How Jacques Pommeraud Frames Responsible AI

Across the interview, several recurring ideas emerge about how Responsible AI should be approached in practice:

• It is a leadership issue, not just a technical issue. Executive teams and public decision-makers must set direction, values and incentives, rather than delegating everything to data scientists or legal teams.
• It must be embedded into existing governance. Responsible AI is most effective when integrated into risk management, compliance, product development and HR processes, instead of living in isolation as a separate initiative.
• It is inherently cross-disciplinary. Sound AI governance requires the combined input of law, ethics, engineering, security, operations and domain experts.
• It must be tangible. Principles alone are not enough; organizations need concrete processes, controls, metrics and decision rights.

With that framing, we can translate the interview’s themes into an actionable multi-pillar approach.

Pillar 1: Governance and Accountability

Governance is the backbone of Responsible AI. It answers the fundamental questions: Who decides what can be deployed, who is accountable when something goes wrong, and how are trade-offs arbitrated?

Key elements of effective AI governance

• Clear ownership– Every significant AI use case should have an identified owner responsible for performance, ethics, compliance and risk.
• Oversight bodies– Many organizations establish an AI ethics committee or AI governance board that brings together business, technical, legal and societal perspectives.
• Decision gates– Formal checkpoints in the AI lifecycle (design, training, testing, deployment, monitoring) where risks are assessed and go or no-go decisions are made.
• Documented policies– Written standards on topics such as data sourcing, model validation, human oversight, and escalation procedures when incidents occur.

For policymakers, governance means defining who is responsible for AI decisions in public agencies and how citizens can seek redress. For companies, it means clarifying how responsibility is shared across business units, central AI teams and leadership.

Pillar 2: Ethics and Human-Centric Design

Responsible AI is ultimately about people. Ethics is not limited to abstract principles; it is expressed in concrete design choices that affect users, employees and communities.

Key ethical dimensions

• Fairness and non-discrimination– Minimizing unjust bias across attributes such as gender, ethnicity, age, disability or socio-economic status.
• Human autonomy– Ensuring that AI augments rather than replaces meaningful human judgment in critical decisions, especially in areas like health, justice, credit or employment.
• Safety and well-being– Avoiding harmful outcomes, psychological harm, or unintended use cases that put individuals or groups at risk.
• Inclusion– Involving diverse stakeholders in the design and testing process to surface issues that homogeneous teams might miss.

From principles to practice

A human-centric approach to AI design can include:

• Impact assessments– Structured analysis of potential risks, affected groups and mitigation measures before deployment.
• Ethical design reviews– Dedicated sessions where multidisciplinary teams challenge assumptions and anticipated impacts of AI features.
• User testing with affected communities– Not only usability testing, but also open conversations about perceived fairness and trust.

These practices align with the interview’s emphasis on taking Responsible AI out of the abstract and grounding it in real user experiences.

Pillar 3: Transparency and Explainability

Transparency is a recurring theme in Responsible AI discussions, and it appears prominently in the Cercle de Giverny conversation as well. Transparency builds trust and enables both oversight and accountability.

Transparency for different audiences

• For users and citizens– Clarity on when AI is being used, what it does, what data it uses and what their options are (for example, seeking human review).
• For internal stakeholders– Documentation that allows product managers, legal teams, auditors and executives to understand how models were built and tested.
• For regulators and auditors– Evidence of compliance with legal and ethical requirements, including risk assessments, monitoring logs and incident reports.

Explainability in practice

Explainability does not always mean exposing every line of code or model parameter. Instead, it focuses on offering meaningful explanations at the right level of detail for each audience. Examples include:

• Simple model cards that summarize model purpose, training data, known limitations and recommended use cases.
• Decision summaries that show key factors influencing an outcome, especially when decisions affect rights or access to services.
• Technical interpretability tools that help data scientists diagnose bias and unexpected model behavior.

By investing in transparency and explainability, organizations not only comply more easily with emerging regulations but also strengthen user confidence in AI-driven services.

Pillar 4: Risk Management and Regulation Readiness

Responsible AI sits at the intersection of ethics, technology and law. As regulations evolve, organizations that proactively manage AI risk gain a clear head start.

Core components of AI risk management

• Risk classification of use cases– Not all AI systems carry the same level of risk. High-risk applications (for example, those impacting fundamental rights) should face stricter controls, testing and human oversight.
• Continuous monitoring– AI models can drift over time as data and context change. Ongoing monitoring and periodic revalidation are essential.
• Incident response– Clear processes to detect, report and address issues such as biased outcomes, security breaches or unexpected use patterns.
• Regulatory scanning– Tracking legal developments and aligning internal standards with upcoming or existing requirements.

For policymakers, the challenge is to design balanced regulation that protects citizens while preserving room for innovation. For businesses and developers, the imperative is to build governance structures that are flexible enough to adapt as the legal landscape evolves.

Pillar 5: Stakeholder Engagement and Societal Impact

AI systems do not exist in isolation. They interact with complex social, economic and cultural contexts. The interview underlines the importance of dialogue with stakeholders beyond the organization’s walls.

Who needs to be at the table?

• Users and customers– To surface expectations, concerns and potential misuse scenarios.
• Employees– Especially those whose roles are transformed by AI-powered tools or automation.
• Civil-society organizations and advocacy groups– To highlight broader societal impacts, especially for vulnerable communities.
• Regulators, policymakers and standard-setters– To align on good practices and contribute to emerging norms.
• Academic and research communities– To validate methods and stay informed about state-of-the-art techniques and risks.

Structured stakeholder engagement can take the form of advisory councils, public consultations, co-design workshops or independent audits. The benefit is not only risk mitigation but also better products and services informed by real-world needs and perspectives.

Implementation Challenges: From Principles to Practice

Almost every organization now has some form of AI or data ethics principles. The recurring challenge, echoed in the Cercle de Giverny discussion, is how to turn those principles into daily practice.

Common obstacles

• Fragmented responsibilities– Ethics, compliance, IT, data science and business units may each own a piece of the puzzle, but no one orchestrates the whole.
• Limited awareness– Teams building or purchasing AI systems may not fully understand ethical and regulatory expectations.
• Pressure for speed– Intense competition can incentivize rapid deployment over careful assessment.
• Lack of tools and metrics– Without practical checklists, templates and indicators, principles remain aspirational.

Levers to overcome these challenges

• Leadership sponsorship– Clear messages from top management or public leaders that quality, ethics and compliance are non-negotiable.
• Training and capacity building– From executive briefings for decision-makers to hands-on workshops for developers and product teams.
• Integration into existing processes– Embedding AI checks into procurement, product gating, risk committees and performance reviews.
• Simple, repeatable tools– Standardized templates for impact assessments, model documentation, and approval workflows.

Addressing these challenges transforms Responsible AI from a one-off project into a sustainable capability that scales with the organization.

Building a Responsible AI Governance Framework

One practical takeaway from the interview themes is the importance of a structured governance framework that aligns roles, processes and artefacts. While each organization will tailor its model, many rely on a few core components.

Illustrative Responsible AI governance structure

This kind of framework makes it easier to operationalize the themes underlined in Jacques Pommeraud’s discussion: clarity, accountability and a shared language for Responsible AI.

What Policymakers, Business Leaders, Developers and Civil Society Can Do Now

The Cercle de Giverny interview speaks to a broad audience. Each group can take concrete steps to advance Responsible AI in their sphere of influence.

For policymakers and regulators

• Develop risk-based regulatory approaches that calibrate obligations based on the potential harm of AI use cases.
• Encourage transparency and auditability requirements that are specific enough to be useful, yet flexible for different technologies.
• Invest in public-sector AI capabilities so that agencies can procure, oversee and evaluate AI systems effectively.
• Engage with businesses, researchers and civil society in open consultation processes when shaping AI policy.

For business leaders and corporate boards

• Make Responsible AI a standing agenda item in strategy and risk committees.
• Appoint an executive sponsor for AI and define clear roles for ethics and governance.
• Integrate AI considerations into enterprise risk management, internal audit and compliance programs.
• Signal to teams that balancing innovation with responsibility is a performance expectation, not an optional extra.

For developers, data scientists and product teams

• Embed fairness, robustness and privacy checks into model development workflows.
• Use documentation practices such as model cards and data sheets to make systems understandable and auditable.
• Collaborate with legal, ethics and domain experts from the earliest stages of design, not just before launch.
• Advocate for realistic timelines that allow for proper testing and risk mitigation.

For civil-society organizations and researchers

• Monitor AI deployments and surface real-world impacts on communities, especially those at risk of exclusion or discrimination.
• Contribute to voluntary standards, benchmarks and evaluation methods for Responsible AI.
• Engage in constructive dialogue with both public and private actors to co-design solutions.
• Educate citizens about their rights and options when interacting with AI-powered systems.

Practical Recommendations Checklist

To make the interview’s themes actionable, the following concise checklist summarizes key steps for Responsible AI adoption.

Strategy and governance

• Define a clear vision for how AI supports your mission or business strategy.
• Approve a Responsible AI policy that aligns with your values and regulatory context.
• Set up an AI governance structure with defined roles, decision rights and escalation paths.

Design and development

• Conduct ethical and societal impact assessments for significant AI initiatives.
• Adopt data quality and bias mitigation practices from early prototyping stages.
• Include affected users and stakeholders in co-design and testing where feasible.

Deployment and monitoring

• Establish monitoring indicators for performance, fairness and unintended consequences.
• Maintain human oversight for high-stakes decisions and define review mechanisms.
• Implement feedback and complaint channels for users and employees.

Culture and capabilities

• Offer training on Responsible AI to leadership, product teams and technical staff.
• Encourage a culture where raising concerns about AI risks is valued and protected.
• Continuously update practices based on new research, standards and regulations.

Key Takeaways from the Cercle de Giverny Discussion

The interview with Jacques Pommeraud, hosted by the Cercle de Giverny, reinforces a central message: Responsible AI is not an obstacle to innovation, but the foundation for sustainable, trusted and impactful AI adoption.

• Responsible AI requires clear governance, ethical reflection and practical tools, not just high-level principles.
• Transparency, accountability and stakeholder engagement are essential to earning and maintaining trust.
• Regulators, businesses, developers and civil-society actors each have complementary roles in shaping how AI serves society.
• The organizations that act now to build robust Responsible AI frameworks will be better positioned to innovate confidently as technology and regulation evolve.

By turning the insights from this discussion into concrete governance, design and cultural practices, leaders across sectors can harness AI’s potential while honoring ethical, legal and societal expectations.