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.
Related jobs
Jobs that call for the skills explored in this talk.
Featured Partners
Related Videos
Explainable machine learning explained
Karol Przystalski
How AI Models Get Smarter
Ankit Patel
Open Source AI, To Foundation Models and Beyond
Ankit Patel, Matt White, Philipp Schmid, Lucie-Aimée Kaffee, Andreas Blattmann
Staying Safe in the AI Future
Cassie Kozyrkov
A walkthrough on Responsible AI Frameworks and Case Studies
Toju Duke
What non-automotive Machine Learning projects can learn from automotive Machine Learning projects
Jan Zawadzki
A hundred ways to wreck your AI - the (in)security of machine learning systems
Balázs Kiss
MLOps - What’s the deal behind it?
Nico Axtmann
From learning to earning
Jobs that call for the skills explored in this talk.


(Senior) Experte (w/m/d) Data & KI
B.Braun Melsungen AG
Melsungen, Germany
Senior
Python
Machine Learning
Security-by-Design for Trustworthy Machine Learning Pipelines
Association Bernard Gregory
Machine Learning
Continuous Delivery
Machine Learning Engineer, MLOps/GenAI, Engine AI Center of Excellence (AICE)
Amazon.com, Inc
Berlin, Germany
Machine Learning
Natural Language Processing
AI Engineer / Machine Learning Engineer / KI-Entwickler (m/w/d) - Schwerpunkt Cloud & MLOps
Agenda GmbH
Rosenheim, Germany
Intermediate
API
Azure
Python
Docker
PyTorch
+9





