Effective documentation of artificial intelligence (AI) models is essential for transparency and accountability. To be truly effective, the documentation must clearly explain how the model makes decisions and the social impacts of those decisions, including considerations of fairness, risks, security, and transparency. This documentation should be written in a way that is accessible to various stakeholders, including end-users, developers, and legal or policy professionals. But mainly it means anyone can understand it. For instance, it’s crucial for individuals to know which parts of the algorithm affect them, how they are impacted, and why specific decisions are made. This is especially important in high-stakes situations, such as algorithms that influence criminal sentencing or credit decisions.

Turning AI’s “black box” into an explainable system is challenging, particularly with complex models like neural networks. As AI advances, it is vital that models incorporate built-in mechanisms for self-explanation, such as visualizations or simplifications. These tools can help make complex models more interpretable. However, the challenge lies in balancing explainability with accuracy. Making a model explainable may reduce its complexity and potentially compromise its predictive power. Which makes it unattractive for companies trying to deliver fast and scale and grow without additional paper work and can mean Responsible AI is seen as “nice to have” but not fully implemented.

Sources

“What is explainable AI?” IBM https://www.ibm.com/topics/explainable-ai

“Why Should I Trust You?” Explaining the Predictions of Any Classifier by Marco Tulio https://arxiv.org/pdf/1602.04938