Algorithmic Transparency
Definition: Algorithmic transparency means being able to see and understand how an AI system makes its decisions. It involves revealing what data the AI uses, how it processes that data, and why it produces certain results.
Example
If a court uses an AI tool to help assess the risk of reoffending, algorithmic transparency means being able to explain what factors the AI looked at and how it weighed them. Without that information, no one can know if the tool is fair or accurate.
Why It Matters?
Algorithmic transparency matters because it builds accountability and trust. In law, transparency helps lawyers and judges evaluate whether AI-assisted decisions are fair, unbiased, and based on sound reasoning. Without it, it becomes impossible to challenge or correct an AI’s mistakes, which can harm people’s rights and outcomes.
How is it different than Explainable AI?
Algorithmic transparency is not the same as explainable AI. Transparency focuses on showing what is inside the system, such as the data, logic, or training process. Explainable AI focuses on helping people understand why the system produced a specific result. Transparency is about access to information, while explainability is about making that information meaningful.
Analogy:
Transparency is like being able to pop the hood and see the engine so that you can inspect how it’s built.
Explainability is like having a dashboard that tells you what the car is doing right now to understand why it’s turning, braking, or accelerating.