AI Today Podcast: Trustworthy AI Series: Explainable & Interpretable AI
Oct 20, 2023
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The podcast explores the importance of explainable and interpretable AI, including the need for verifiable explanations, algorithmic transparency, and debugging AI systems. It discusses the significance of understanding root cause explanations and the challenges in achieving explainability. The chapter also highlights the difference between black box and white box algorithms and the importance of choosing the right algorithmic approach. It concludes by discussing trustworthy AI resources, upcoming interviews, and listener feedback.
Explainable and interpretable AI is essential for building trustworthy AI systems and establishing trust.
Choosing the right algorithmic approaches, such as decision trees and linear models, can enhance explainability and trust in AI systems.
Deep dives
Importance of Explainable and Interpretable AI
Explainable and interpretable AI is a crucial aspect of trustworthy AI. Without understandability, trust cannot be established in AI systems. Verifiable explanations for how machine learning systems make decisions allow humans to be in the loop and hold someone accountable. While not all machine learning approaches are inherently explainable, it is essential to prioritize approaches that provide explanations on how outcomes were arrived from input data. This can include the use of more explainable algorithms like decision trees and linear models.
Challenges and Implications of Explainable AI
Explainable AI poses significant challenges as many algorithms, especially deep learning neural networks, are considered black box technologies. These algorithms produce opaque or unexplainable results, hindering trust and the ability to debug AI systems effectively. However, the concept of interpretability offers an alternative, providing a general understanding of the factors that contribute to decision-making. It allows for predicting outcomes based on the observed cause and effect relationship, even when the exact mechanism is unknown.
Algorithmic Choices for Explainability
Choosing the right algorithmic approach is crucial for achieving explainability. Not all algorithms are explainable, with some being inherently unexplainable, while others can produce more explainable results. Algorithms like decision trees and support vector machines offer direct means to explain how outcomes were derived from input data. While algorithms like deep learning neural networks are less explainable, it is important to consider algorithmic choices that align with the desired level of explainability, ensuring trust and understanding in AI systems.
AI systems have the potential to provide great value. But also the potential to cause great harm. Knowing how to build or use AI systems is simply not going to be enough. You need to know how to build, use, and interact with these systems ethically and responsibly. Additionally you need to understand that Trustworthy AI is a spectrum that addresses various aspects relating to societal, systemic, and technical areas.