Building a responsible future with AI

Why do we need a responsible AI ?

Some technologies arrive like revolutions. Others slip quietly into our daily routines until one day, we realize they’ve transformed the way we work, decide, and collaborate. AI belongs to the second category. Not because it’s invisible, but because its true impact lies upstream, in the choices we make long before a model is deployed: what we protect, what we refuse, what we prioritize, and the future we choose to design together.

Working on responsible AI at Renault Group has taught me something essential: innovation only matters if it strengthens trust. So this article isn’t about tools or methods. It’s about the principles that guide us, the questions that keep us honest, and the collective commitment behind every AI project we build.

A transformation anchored in people

Our digital journey has accelerated dramatically since those first cases. Today, AI touches manufacturing, engineering, customer experience, procurement, design… and yet, the heart of this transformation is not the technology itself: it’s our people.

Since 2024, more than 46,000 colleagues have been trained in generative AI. Forty-six thousand. That’s not a statistic; it’s a cultural shift.
We built GenAI@Renault, our internal secure platform, because we wanted every employee - whether they code, negotiate, design, or repair - to shape this transformation safely and confidently.

I often see colleagues who, a year ago, barely dared to open a prototype tool, now challenging us with ideas we hadn’t even considered. That’s when I see the real impact of AI: when it becomes a language everyone can speak.

How responsible AI became our compass

AI pushes us forward, but it also forces us to ask ourselves the right questions.
What is fair? What is transparent? What protects people? What respects the planet?

At the end of 2023, we decided these questions needed a clear and structured answer. That’s how our Responsible AI framework was born: five pillars that guide every project, from early exploration to production.

1. Privacy and regulatory alignment

European regulations, especially the AI Act, set the rules of the game. We go further: beyond compliance, we aim for clarity. An AI that cannot be explained or audited doesn’t belong in our ecosystem.

2. Fairness and inclusion

AI learns from data, and data reflects the world, with all its imperfections.
A biased model can exclude, mislead, or endanger.
Our goal is simple: design AI that includes rather than divides. That asks the right questions. That reflects the diversity of the people it serves.

3. Transparency and explainability

I like to say that explainability is our “human interface.”
If we deploy an assisted-driving feature, the driver must understand why the car brakes, not guess. If a technician analyses a detection model, they must understand what it saw, not trust blindly.

4. Security and robustness

In a world where data is both invaluable and fragile, protecting it is non-negotiable.
We ensure our systems are resilient to misuse, attacks, or unintended consequences. AI should be a shield, not a vulnerability.

5. Environmental impact

This is often overlooked, but to me, it’s a moral imperative. Do we really need massive models when lighter, frugal ones do the job? Can we design intelligence that respects resources? Every unnecessary GPU hour leaves a carbon trace. So we start simple. We scale only when needed. And we choose low-carbon infrastructures whenever possible.

Because innovation shouldn’t cost the Earth - literally.

Responsible by design, not by revision

In the AI Center of Excellence, we treat responsible practice as a foundation, not a checkbox.

Our MLOps approach - our “assembly line” of AI - integrates responsibility from day one:

An AI charter to share more than just tools but a culture

Tools evolve quickly. Culture takes time.
That’s why we created a Responsible AI Charter, a practical playbook for project teams, and a short, accessible e-learning module on the AI Act. Our AI Charter has just been made available on our corporate website.

My dream is that anyone at Renault Group - whether they work in a factory, an office, or in the field - feels empowered to identify risks, ask the right questions, and shape better AI choices.

A responsibility we carry beyond our walls

If there’s one thing I’ve learned along this journey, it’s that no company can build responsible AI alone.
We learn from others, we share what we discover, and we hope to inspire in return.

Our ambition is clear: to become a reference for responsible innovation in our industry.
Not through grand declarations, but through concrete practices, governance, ambassadors, monitoring, and a continuous desire to do better.

I often get asked what “responsible AI” looks like in ten years. The truth is: it will keep evolving. But one thing won’t change: the conviction that technology must serve people, never the other way around.

And that’s the responsibility we carry every day.