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Jean-Marie John-Mathews, Ph.D.
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Exclusive interview: our first television appearance on AI risks & security
This interview of Jean-Marie John-Mathews, co-founder of Giskard, discusses the ethical & security concerns of AI. While AI is not a new thing, recent developments like chatGPT bring a leap in performance that require rethinking how AI has been built. We discuss all the fear and fantasy about AI, how it can pose biases and create industrial incidents. Jean-Marie suggests that protection of AI resides in tests and safeguards to ensure responsible AI.
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How to test ML models? #4 🎚 Metamorphic testing
Metamorphic testing are adapted to Machine Learning. This tutorial describes the theory, examples and code to implement it.
How to test ML models? #3 📈 Numerical data drift
Testing the drift of numerical feature distribution is essential in AI. Here are the key metrics you can use to detect it.
How to test ML models #2 🧱 Categorical data drift
Testing drift of categorical feature distribution is essential in AI / ML, requiring specific metrics
How to test ML models? #1 👉 Introduction
What you need to know before getting started with ML Testing in 3 points
Where do biases in ML come from? #7 📚 Presentation
We explain presentation bias, a negative effect present in almost all ML systems with User Interfaces (UI)
Where do biases in ML come from? #6 🐝 Emergent bias
Emergent biases result from the use of AI / ML across unanticipated contexts. It introduces risk when the context shifts.
Where do biases in ML come from? #5 🗼 Structural bias
Social, political, economic, and post-colonial asymmetries introduce risk to AI / ML development
Where do biases in ML come from? #4 📊 Selection
Selection bias happens when your data is not representative of the situation to analyze, introducing risk to AI / ML systems
Where do biases in ML come from? #3 📏 Measurement
Machine Learning systems are particularly sensitive to measurement bias. Calibrate your AI / ML models to avoid that risk.
Where do biases in ML come from? #2 ❌ Exclusion
What happens when your AI / ML model is missing important variables? The risks of endogenous and exogenous exclusion bias.
Where do biases in ML come from? #1 👉 Introduction
Research Literature review: A Survey on Bias and Fairness in Machine Learning
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What does research tell us about the future of AI Quality? 💡
We look into the latest research to understand what is the future of AI / ML Testing
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