Responsible AI in Practice: Insights from the Jacques Pommeraud – Cercle de Giverny Conversation

The rapid rise of artificial intelligence has pushed Responsible AI from a niche topic to a board-level priority. In conversations such as the one between Jacques Pommeraud and the Cercle de Giverny, technologists, policymakers and business leaders converge on a central question: how do we capture AI's benefits while staying ethical, compliant and trustworthy

This article distills the key themes that matter most for modern organizations: AI ethics principles, governance frameworks, AI regulation, practical measures for design and deployment, bias mitigation, explainability, stakeholder engagement and risk management. It is written for executives, public decision‑makers and practitioners who want to move from high‑level discussion to concrete action.

1. What Is Responsible AI and Why It Matters Now

Responsible AI refers to the development, deployment and use of AI systems in ways that are ethical, legally compliant, safe, transparent and aligned with human values. It is not a single tool or checklist; it is a way of designing and governing AI across its entire lifecycle.

For leaders, Responsible AI is no longer a "nice to have". It is a direct driver of value and risk:

• For businesses: it protects brand trust, reduces legal and operational risk, and accelerates sustainable adoption of AI across the enterprise.
• For policymakers: it anchors innovation in clear rules and safeguards, enabling economic growth while protecting citizens.
• For technologists: it sets expectations and standards for how models are built, tested and monitored.

Done well, Responsible AI unlocks a powerful combination: faster innovation with fewer surprises. Done poorly, it can lead to bias, opaque decisions, regulatory sanctions and public backlash.

2. Core Ethical Principles Behind Responsible AI

Although organizations and jurisdictions use different wording, most AI ethics frameworks converge on a common set of principles. These principles provide a north star for policy, design and governance.

Turning principles into practice is where AI governance becomes critical.

3. Governance Frameworks for AI Ethics

AI governance connects high‑level values to day‑to‑day decisions about models, data and deployments. Effective frameworks are usually multi‑layered: they cover strategy, policies, processes, tools and culture.

3.1 Key components of an AI governance framework

• Strategy and risk appetite: a clear statement of why the organization invests in AI, what types of applications it will avoid, and how much risk it is willing to accept in different domains.
• Policies and standards: written rules on topics such as data usage, model development, documentation, monitoring, incident response and third‑party tools.
• Processes and workflows: repeatable steps for idea intake, risk assessment, technical review, sign‑off and periodic re‑evaluation of AI systems.
• Roles and responsibilities: defined responsibilities across business owners, data scientists, engineering, legal, compliance, security and internal audit.
• Controls and tools: concrete mechanisms such as model risk checklists, bias testing pipelines, access controls, logging and audit trails.
• Monitoring and continuous improvement: metrics, dashboards and review cycles that detect drift, emerging risks and regulatory changes.

3.2 Governance structures that work in practice

Organizations that progress fastest on Responsible AI tend to adopt cross‑functional structures that combine strategic direction with operational depth. Common patterns include:

• AI ethics or Responsible AI committee bringing together business, technical, legal, compliance and sometimes external voices to review sensitive use cases and set policy.
• Model risk management function to independently review high‑risk models, challenge assumptions and sign off on deployment.
• Product‑embedded AI leads who translate corporate policies into concrete guardrails and practices in each business line.
• Central enablement teams providing templates, toolkits, training and reusable components for explainability, privacy and security.

The exact shape varies by sector and size, but the common thread is clear: governance is shared, not siloed.

4. Navigating AI Regulation and Policy

AI regulation is evolving rapidly. Legislators and regulators are designing rules to manage risks, increase transparency and ensure accountability, while still encouraging innovation. Leaders need a forward‑looking view rather than reacting only when laws are finalized.

4.1 Global trends in AI regulation

Across jurisdictions, several trends are visible:

• Risk‑based approaches: many frameworks focus on the level of risk an AI system poses to safety, fundamental rights or critical infrastructure, with stricter obligations for higher‑risk uses.
• Transparency and documentation: increasing requirements for technical documentation, data governance records, and user‑facing disclosures when people interact with AI systems.
• Human oversight and redress: expectations that individuals can contest or appeal high‑impact automated decisions, and that human reviewers are empowered and trained.
• Data protection and security: alignment with existing privacy and cybersecurity regulations, including controls on training data, retention and access.
• Sector‑specific rules: more granular guidance emerging in areas such as financial services, healthcare, employment and public sector use.

4.2 What organizations should do now

Even as legal details evolve, organizations can take concrete steps that reduce regulatory risk while improving internal discipline:

• Build and maintain an AI system inventory: know where AI is used, what data it touches, and what decisions it influences.
• Classify use cases by risk level: consider impact on safety, rights, reputation, financial stability and regulatory scrutiny.
• Introduce AI impact assessments: structured reviews that identify potential harms, affected groups and necessary mitigations before deployment.
• Align with widely referenced principles and frameworks such as risk management standards and international AI ethics guidelines, which often inform future regulation.
• Stay close to regulators and industry bodies through consultation, feedback and participation in working groups where possible.

5. Designing Responsible AI Systems

Responsible AI is easiest to achieve when it is embedded from the start of the AI lifecycle, not added as a patch at the end. The following practices align design choices with AI ethics and governance expectations.

5.1 Problem definition and use case selection

• Clarify purpose and value: what problem are you solving, for whom, and why is AI the right tool
• Check for sensitive domains: recruitment, lending, healthcare, policing and access to public services usually warrant stricter controls.
• Identify impacted stakeholders: including end‑users, non‑users indirectly affected, employees and vulnerable groups.
• Define success beyond accuracy: include fairness, robustness, interpretability and user satisfaction as design objectives.

5.2 Data sourcing and preparation

• Legitimate basis and consent: ensure data is collected and used in line with privacy and sectoral laws.
• Data minimization: only collect and retain what is needed for the intended purpose.
• Bias and representativeness checks: examine whether datasets under‑represent or misrepresent certain populations or conditions.
• Data quality and lineage: document sources, transformations and known limitations so they are transparent to downstream teams.

5.3 Model development and evaluation

• Model choice: select architectures that balance performance with explainability, latency, resource use and operational complexity.
• Fairness metrics: incorporate appropriate fairness or disparity metrics relevant to the use case and legal context.
• Robustness testing: test behavior under distribution shifts, edge cases and potential adversarial inputs.
• Documentation: maintain clear documentation of design decisions, training configurations, and evaluation results.

5.4 Bias mitigation in practice

Bias in AI does not automatically mean malicious intent. It typically emerges from data patterns, historical inequities or modeling choices. Effective mitigation is iterative and multi‑layered:

• Pre‑processing: address imbalances in the training data by re‑sampling, re‑weighting or gathering additional data for under‑represented groups.
• In‑processing: use training techniques that constrain models to reduce unfair disparities while preserving overall utility where possible.
• Post‑processing: adjust thresholds or decision rules differently for groups when this is legally and ethically justified.
• Context‑aware analysis: engage legal and ethics experts to ensure fairness interventions do not conflict with non‑discrimination laws or policy norms.

5.5 Explainability and transparency

Explainability is central to both AI governance and user trust. The level of explanation needed depends on the context, but good practices include:

• Model‑level explanations: providing insight into which features generally influence predictions and how.
• Instance‑level explanations: giving users or reviewers a reasoned explanation for a specific outcome that affected them.
• Audience‑appropriate language: tailoring explanations for technical teams, regulators, executives and end‑users separately.
• User‑facing disclosures: making it clear when a system uses AI, what its limitations are, and what options users have for recourse.

6. Deploying and Operating AI Safely

Deployment is where AI moves from concept to real‑world impact. Responsible AI requires operational guardrails that are active every day, not only at launch.

6.1 Risk assessment and approvals

• AI risk assessments prior to go‑live, covering safety, bias, privacy, security and reputational impacts.
• Tiered approval flows so that high‑risk systems receive senior or cross‑functional review, while low‑risk experiments can move quickly with lighter oversight.
• Clear risk ownership for each deployed system, often anchored in a business owner rather than only technical teams.

6.2 Monitoring, feedback and incident response

• Continuous monitoring of model performance, data drift, input distributions and key risk indicators.
• User feedback loops so that employees and customers can report unexpected outcomes or errors.
• Incident management playbooks defining how to pause, roll back or reconfigure AI systems when something goes wrong.
• Periodic re‑validation at predefined intervals or after significant model or data changes.

7. Auditing and Assurance for AI Systems

Independent assurance is increasingly central to Responsible AI, especially for high‑impact or regulated use cases. Effective auditing combines technical depth with process and governance review.

7.1 Internal and external AI audits

• Internal audit and risk teams can evaluate whether AI projects follow the organization's frameworks, policies and controls.
• External assessments may be needed for compliance with sectoral rules or to build trust with partners and customers.
• Scope definition is key: audits can cover models, data, processes, documentation, security and governance structures.

7.2 Typical elements of an AI audit

• Use case review: purpose, context, stakeholders and risk classification.
• Data and model examination: sources, preprocessing, training, evaluation, fairness and robustness testing.
• Control design and effectiveness: access management, change management, monitoring and incident handling.
• Documentation and traceability: ability to reconstruct design decisions and demonstrate compliance.
• Governance alignment: adherence to internal policies and relevant external regulations or standards.

8. Stakeholder Engagement and Cross‑Sector Implications

Responsible AI is inherently multi‑stakeholder. It affects and involves more than data scientists and engineers; it shapes experiences and rights across society. Successful programs intentionally engage diverse perspectives.

Different sectors experience these dynamics in distinct ways:

• Finance: focus on model risk management, fairness in credit and pricing, and alignment with stringent regulatory expectations.
• Healthcare: emphasis on patient safety, clinical validation, informed consent and explainability to clinicians.
• Public sector: heightened scrutiny on transparency, non‑discrimination, due process and public trust.
• Industry and logistics: attention to safety, human‑machine collaboration and workforce impact.

9. Implementation Challenges — And How to Overcome Them

Building robust Responsible AI programs is rewarding but not trivial. Common challenges can, however, be turned into drivers of long‑term advantage when addressed proactively.

9.1 Lack of clarity and shared language

Challenge: different teams use terms like bias, explainability or risk in conflicting ways, creating confusion and slow decision‑making.

Practical response: co‑create a concise internal glossary and a simple Responsible AI policy that defines key concepts, roles and escalation paths. Train teams so language becomes consistent across the organization.

9.2 Tension between innovation speed and governance

Challenge: teams fear that governance will slow experimentation, while risk and compliance functions worry about ungoverned AI proliferation.

Practical response: adopt a tiered approach where low‑risk experiments enjoy lightweight controls, and only higher‑risk use cases trigger deeper review. Provide self‑service templates and guardrails so governance becomes an enabler rather than a blocker.

9.3 Data limitations and legacy systems

Challenge: existing data may be incomplete, biased or poorly documented, and critical processes may depend on legacy infrastructure.

Practical response: prioritize data quality and governance as foundational, invest in improved data lineage and cataloging, and start Responsible AI efforts on well‑understood domains before expanding.

9.4 Skills gaps and cultural change

Challenge: many organizations have strong technical talent but limited expertise in ethics, human‑centered design or regulatory interpretation related to AI.

Practical response: build multi‑disciplinary teams, offer targeted training, and bring in external expertise where necessary. Reward teams not only for model performance, but also for responsible behavior and compliance.

10. A Practical 10‑Step Roadmap to Responsible AI

To move from principles to impact, organizations can follow a pragmatic, phased roadmap. The steps below provide a structure that leaders can adapt to their context.

1. Establish executive sponsorship and clearly articulate why Responsible AI matters for your mission, customers and regulators.
2. Map your AI landscape by creating an inventory of existing and planned AI systems, along with their business owners.
3. Define your Responsible AI principles and risk appetite, aligned with applicable laws and sector expectations.
4. Set up governance structures such as an AI ethics committee, model risk function or equivalent cross‑functional body.
5. Develop policies, standards and templates for the AI lifecycle, including impact assessments, documentation and monitoring plans.
6. Embed Responsible AI into design and development with training, tools and coding standards for data and model teams.
7. Introduce risk‑based approvals and go‑live checklists that scale from prototypes to production systems.
8. Implement monitoring and incident response to detect drift, emerging biases or unexpected impacts quickly.
9. Engage stakeholders— employees, customers, regulators and partners — to gather feedback and iterate on your approach.
10. Review and improve continuously as technology, regulation and societal expectations evolve. Treat Responsible AI as an ongoing capability, not a one‑off project.

Conclusion: Turning Responsible AI into a Competitive Advantage

The discussions held in forums like the Cercle de Giverny underscore a growing consensus: AI will only be truly transformative if it is trusted. Trust does not emerge by accident; it is designed, governed and cultivated over time.

By embracing Responsible AI— grounded in clear ethics, robust AI governance, forward‑looking AI regulation awareness, effective bias mitigation and meaningful explainability— organizations can unlock AI's full potential while protecting people and preserving the legitimacy of innovation.

For technologists, this means building systems that are not just powerful, but also transparent, fair and safe. For policymakers, it means crafting frameworks that reward responsible behavior and provide clarity. For business leaders, it means treating Responsible AI as a core capability and a differentiator — a way to innovate boldly, earn trust and create lasting value in an AI‑driven world.