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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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Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 24, 2026 · 15:30–16:00

Outdoor Stage

From Boardroom to Build Pipeline: What AI Governance Actually Looks Like in Practice

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Board Director & Technology Executive

Anita Ganti
Open session

World Congress 2026 North America

September 24, 2026 · 10:20–10:50

Stage 4

AI Decision Observability: Enabling Transparency and Trust in Intelligent Systems

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Open session

World Congress 2026 North America

September 24, 2026 · 16:50–17:20

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Who Tests the AI? Building Trustworthy AI Systems at Enterprise Scale

Him Raj Singh

Manager, Software Engineer at PayPal

Him Raj Singh
Open session

World Congress 2026 North America

September 25, 2026 · 10:20–10:50

Tech Leaders Stage

Democratizing AI: Why Open Models Are Essential for the Next Era

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NVIDIA Corporation, Developer Advocate -- Manager

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Open session

World Congress 2026 North America

September 24, 2026 · 12:15–12:45

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It's Not About the Models

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Bob Wambach
Open session

World Congress 2026 North America

September 25, 2026 · 09:00–09:30

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Building AI that fits your business

Benny Chen

Co-Founder of Fireworks

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