Path To Gain Functional Transparency In Artificial Intelligence With Meaningful Explainability

Transparency requires multidisciplinary collaboration: The paper emphasizes that achieving functional transparency in AI cannot be solved by technologists alone. It requires coordinated efforts across computer science, ethics, law, and social science—suggesting that responsible AI development demands input from diverse expertise beyond engineering.

"Functional transparency" is essential for fairness evaluation: The paper identifies functional transparency as a "fundamental aspect" of algorithmic decision-making that allows stakeholders to understand how AI systems work and assess whether they are fair and accurate, particularly in high-stakes domains like targeted advertising and matchmaking algorithms.

User-centered design is critical: Rather than imposing transparency solutions from above, the paper proposes a "user-centered compliant-by-design" approach, suggesting that effective AI transparency must be designed with actual users' needs and comprehension in mind from the outset, not added as an afterthought.

Current transparency approaches face significant challenges: The paper acknowledges that achieving functional transparency in sophisticated AI systems "poses significant challenges that need to be addressed," indicating that existing methods for explaining AI decisions are inadequate for increasingly complex systems.

Transparency enables accountability and societal alignment: The paper frames transparent and explainable AI as essential for building systems that are "accountable, trustworthy, and aligned with societal values," positioning transparency not just as a technical feature but as foundational to responsible AI deployment.