The podcast delves into the Explainable AI Layer of the Cognilytica Trustworthy AI Framework, discussing the importance of AI algorithms being able to explain decisions. It explores the significance of transparent AI systems in building trust, highlighting pitfalls of black box technology. The contrast between explainable algorithms and less transparent machine learning approaches is also emphasized.
Explainable AI Layer focuses on understanding system behavior to make black boxes less opaque.
Choosing white box algorithms can provide more explainable outcomes compared to black box algorithms.
Deep dives
Importance of Trustworthy AI
Building trustworthy AI is crucial for ensuring transparency and understanding in AI systems. The conversation around trustworthy AI and the need for explainable AI has been prominent in the past year. Trustworthy AI involves understanding the five layers of trustworthy AI, with a focus on explainable AI and interpretability to ensure humans can comprehend decision-making processes. Ensuring that AI systems offer understandability and root cause explanations when failures occur is essential for building trust in AI technology.
Challenges with Black Box Technology
Black box technology, such as deep learning, poses challenges as it lacks transparency in decision-making processes. Relying solely on black box technology can be risky as it hinders understandability and accountability in AI systems. Verifiable explanations of how machine learning systems operate are vital to establish trust, especially in critical sectors like healthcare and autonomous vehicles. Explainable AI provides a pathway to enhance transparency and accountability in AI decision-making.
Algorithm Explainability and Selection
Not all machine learning algorithms are inherently explainable, with black box algorithms like deep learning often providing opaque results. Choosing white box or glass box algorithms, such as linear models or decision tree-based models, can offer more explainable outcomes. While these models may not achieve top performance compared to black box algorithms, prioritizing explainability is essential in sectors requiring transparent decision-making processes.
The Explainable AI Layer of the Cognilytica Trustworthy AI Framework addresses the technical methods that go into understanding system behavior and make black boxes less so. In this episode of the AI Today podcast Cognilytica AI experts Ron Schmelzer and Kathleen Walch discuss the interpretable and explainable AI layer.
The Explainable AI Layer
Separate from the notion of transparency of AI systems is the concept of AI algorithms being able to explain how they arrived at particular decisions.