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World Congress 2022 • Jun 15, 2022

Model Governance and Explainable AI as tools for legal compliance and risk management

Kilian Kluge , Isabel Bär

Meeting AI compliance isn't just a legal necessity; it is a concrete engineering problem. Learn how uniting MLOps with explainable AI protects your high-risk models from costly errors.

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#1 about 2 min

Transitioning machine learning models from notebooks to production

The key components required to successfully deploy and maintain machine learning systems long-term.

#2 about 2 min

Challenges of automated application screening in recruiting

Processing incoming job resumes with machine learning presents hidden risks despite a simple workflow architecture.

#3 about 3 min

Addressing real world data changes with robust MLOps

The flywheel effect of user data collection enables models to adapt to continuous distribution shifts in production.

#4 about 5 min

Navigating emerging AI legal frameworks and business risks

Draft legislation classifies common applications like employment filtering as high-risk systems requiring strict algorithmic compliance.

#5 about 7 min

Implementing legal compliance checks within MLOps architecture

Artifact repositories and model registries enable automated performance monitoring and continuous evaluation for required technical documentation.

#6 about 3 min

Interpreting complex model behavior without ground truth data

Analyzing statistical artifacts and debugging training pipelines helps engineers understand black box mappings on a structural level.

#7 about 2 min

Justifying AI decisions to employees and end customers

Non-technical users require tailored outcome explanations to trust automated decisions and maintain proper administrative oversight.

#8 about 3 min

Core requirements for generating meaningful system explanations

Model explanations must be truthful and acknowledge their own knowledge limits when evaluating unrepresented out-of-distribution inputs.

#9 about 3 min

Using feature importance and counterfactuals in hiring decisions

Explaining why a candidate was rejected helps human reviewers individually verify that the system weighted acceptable resume facts.

#10 about 4 min

Auditing machine learning pipelines under strict privacy constraints

Combining reproducible infrastructure with independent explainability components allows external authorities to successfully retroactively investigate algorithmic pipelines.

Matching moments

1:59 min

Explainability requirements for AI in regulated industries

Murli Mohan Srinivas Murli Mohan Srinivas · Europe 2026 Virtual

3:07 min

Enterprise challenges in compliance and generative AI operations

Daniel Tao +3 · World Congress 2024

1:25 min

Applying compliance frameworks and auditing logic to AI applications

Maish Saidel-Keesing Maish Saidel-Keesing · World Congress 2025

1:11 min

Enforcing human-in-the-loop governance for AI-generated models

Marcin Makowski Marcin Makowski · World Congress 2026 Europe

3:04 min

Building trust and transparency into AI interactions

Ekaterina Streltsova Ekaterina Streltsova · Europe 2026 Virtual

1:55 min

Implementing EU AI Act compliance via human-in-the-loop workflows

Simon A.T. Jiménez Simon A.T. Jiménez · World Congress 2025

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