Understanding the Explainability of AI Systems: Requirements, Limits, and Implications

Have you ever wondered how AI reaches its decisions? This question is at the heart of a crucial challenge: enabling humans to understand the results of an Artificial Intelligence system. Explainable AI (XAI) techniques are designed to show the operating logic of an algorithm, providing clear explanations to users about how AI makes decisions. However, the journey to achieving explainability is fraught with complexities and challenges.

The Need for Explainability

In the realm of AI, explainability is defined by the ISO 22989 standard as the property of an AI system to express important factors influencing its results in a way that humans can understand. This goes beyond just the results, encompassing the behavior of the system as well. Transparency, a related concept, involves making appropriate information about the system available to stakeholders, including its characteristics, limitations, and design choices.

Interpretability, on the other hand, is about enabling a target audience to understand the system's behavior, with or without explainability methods. Despite the potential benefits, studies show that explainability does not always lead to trust and can sometimes result in mistrust.

Why Explainability Matters

Several reasons underscore the importance of integrating explainability into responsible AI:

Error Correction: AI systems can malfunction and produce erroneous results. Understanding why this happens is crucial for improving the system and effectively using AI results.
User Understanding: Users should be able to understand or obtain explanations of the results produced by systems that affect them.
Compliance and Accountability: Understanding how a system operates is essential for compliance and accountability, especially in cases of malfunction leading to accidents.
Bias Identification: Algorithms and learning data may contain social biases that need to be identified and eliminated. Explainability can help understand the origin of such biases.

The Limits and Risks of Explainability

While the goal is to instill trust and confidence in users, the relationship between trust and explainability is not straightforward. Some studies suggest that providing explanations can lead to a loss of trust or excessive trust, which can be detrimental. The context in which the system is used is crucial in determining the level of explainability necessary.

Moreover, explainability can introduce new risks, such as manipulation of explanations by malicious entities. This is akin to a nightclub bouncer providing a false reason for denying entry. Such manipulation can mask the true reasons behind decisions, creating a false sense of legitimacy.

Explainability also poses security risks. By understanding AI's reasoning, hackers can exploit system vulnerabilities. Additionally, the protection of proprietary algorithms is a concern, as explanations can lead to the theft of entire algorithms.

Legal Obligations for Explainability

From a regulatory perspective, explainability is included in several statutory texts. In the EU's General Data Protection Regulation (GDPR), transparency and the right to information apply when AI systems use personal data. The EU AI Act also mentions transparency and reporting obligations for high-risk systems.

In the healthcare sector, France's law on bioethics requires reporting obligations for AI designers and users. Similarly, the US and China have regulations promoting transparency and requiring explanations in specific sectors.

Conclusion

AI explainability is essential for expressing important factors influencing system results in a comprehensible manner. However, it is crucial to consider the limits in terms of trust and security. The context in which the system is used should guide the level of explainability necessary to achieve its objectives.

Key Takeaways

Explainability helps users understand AI decisions but can also lead to mistrust.
It is crucial for error correction, compliance, and bias identification.
Explainability can introduce risks of manipulation and security vulnerabilities.
Legal obligations for explainability vary across regions and sectors.
The context of use determines the necessary level of explainability.