Comment pouvez-vous gouverner et réglementer l’IA avec des normes explicables?

Explainable AI (XAI) is akin to opening the "black box" of AI, revealing the mechanisms behind the decisions and predictions. I once worked on a complex protein model where friends who are researchers struggled to trust its recommendations. Implementing XAI techniques turned out to be the bridge between the human experts and the machine, facilitating an understanding that transformed skepticism into collaboration. It’s a reminder that AI must speak the language of humans, not the other way around.

XAI as a technical research discipline is about tools and methods. However, I like to think of XAI as a larger concept, incuding social and ethical aspects. Ultimately, the goal of XAI practitioners should be to foster productive human-machine interaction through trust and transparency. AI models are hardly ever standalone applications. They are embedded in AI systems involving humans. The explainee and their needs are just as important as the structure and mathematics of the AI model itself.

Some argue that interpretability in AI is not needed because there are many things we don't fully understand and yet use productively, for example medication or ... our brain! However, I believe that AI is different because we actually have a realistic chance of opening many of these black boxes if we put in enough effort.

In a student project involving credit scoring, the importance of explainable AI became vividly clear to me. With substantial financial consequences at stake, transparency was paramount. By integrating XAI, we fostered trust not only among regulators but also customers. I realized that AI, no matter how advanced, must be like a good book - transparent enough to be understood, yet profound enough to solve complex problems.

Implementing XAI is a fine balancing act. On one project of my friend involving legal case predictions, finding the sweet spot between interpretability and performance was challenging. I helped to explore various interpretable models and post-hoc explanation methods before settling on an approach that preserved accuracy while shedding light on decision factors. It felt like translating a scientific text into layman's terms without losing its essence.

The most important thing to do first is to define who the typical explainee, i.e. the person receiving the explanation, is and then deriving requirements from that. Here are some things to look out for: - Is 100% correctness of the explanations required or are approximations or heuristics acceptable? - What is the minimum level of explanation detail that still leaves the explainee satisfied and enabled? - Does the explainee have the training to interpret the explanations correctly? This is just the beginning of a long list of questions you should attempt to answer before you even research specific methods. Don't create explanations that YOU like. Create explanations that have a positive impact on the explainee!

The real magic of explainable AI unfolded in a project where it helped mitigate potential legal risks. It was like having an articulate guide navigating through a dense legal jungle, making sure all regulations were observed. Beyond compliance, the transparency brought a social responsibility aspect, enhancing the organization's reputation. It was a powerful lesson in how ethics, innovation, and transparency can harmonize to create value.

With AI regulations taking form all around the world, XAI will be a key component of constructing AI systems that its developers and users trust. On its own, XAI can not magically turn an unintelligible black-box monster into a friendly glassbox companion. On the other hand, NOT leveraging XAI as part of a strategy to achieve regulatory approval would be foolish.

The journey towards implementing XAI in a fraud detection system from a former colleague was riddled with challenges. The trade-off between complexity and transparency was a continual tug of war. The need to tailor explanations to different stakeholders, from tech-savvy investigators to non-technical management, was akin to crafting a story that resonates with diverse audiences. It was a constant reminder that a one-size-fits-all approach can be a pitfall in explainable AI.