Decoding AI: Enhancing Transparency with Interpretability and Explainability

Understanding how AI systems make decisions is critical for accountability and mitigating negative impacts in a complex technological landscape. This module examines algorithmic transparency, introducing methods to interpret model outputs. Participants will explore both interpretable model development techniques and post-hoc explanation methods to clarify black-box models, making AI systems more transparent and comprehensible to stakeholders. Additionally, they will learn about emerging topics in AI transparency and the limitations of current approaches.