Kilian Kluge & Isabel Bär
Model Governance and Explainable AI as tools for legal compliance and risk management
#1about 3 minutes
The challenge of operationalizing production machine learning systems
An AI-powered recruiting tool example illustrates the risks and complexities of deploying machine learning models beyond the notebook.
#2about 2 minutes
Four pillars for deploying successful machine learning systems
Successful long-term ML deployment requires a combination of MLOps, model governance, data governance, and explainable AI.
#3about 4 minutes
Understanding model governance and emerging legal frameworks
Model governance addresses legal compliance, like the EU AI Act's risk-based approach, and mitigates business and reputational risks.
#4about 7 minutes
Using MLOps infrastructure to implement model governance
The MLOps lifecycle, including artifact repositories and model registries, provides the technical foundation for proving performance and ensuring reproducibility.
#5about 4 minutes
Differentiating between model interpretability and explainability
Interpretability provides a technical understanding of model behavior for engineers, while explainability communicates decisions to non-technical stakeholders.
#6about 3 minutes
The four core principles of explainable AI
Explanations must be meaningful to the target audience, accurately reflect the model's process, and operate within the model's knowledge limits.
#7about 3 minutes
Applying explainable AI to a recruiting use case
Techniques like anchor explanations and counterfactuals can answer key HR questions about why a candidate was selected and how certain the model is.
#8about 1 minute
Auditing AI systems using MLOps and explainability
Combining MLOps infrastructure for reproducibility with XAI tools enables internal and external auditors to verify model decisions and compliance.
#9about 2 minutes
Conclusion and handling GDPR deletion requests
A discussion on maintaining reproducibility and compliance when faced with GDPR data deletion requests, emphasizing the importance of thorough documentation.
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