WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
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.

Pause
Mute Enter Fullscreen
#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

3:07 min

Enterprise challenges in compliance and generative AI operations

Daniel Tao +3 · WWC 2024

1:25 min

Applying compliance frameworks and auditing logic to AI applications

Maish Saidel-Keesing Maish Saidel-Keesing · WWC 2025

1:11 min

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

Marcin Makowski Marcin Makowski · WWC Europe 2026

1:55 min

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

Simon A.T. Jiménez Simon A.T. Jiménez · WWC 2025

2:24 min

History and growth of explainable artificial intelligence

Karol Przystalski · LIVE

2:32 min

Building user trust and finding insights via explainable AI

Bernhard Bernhard +1 · WWC 2025

More from World Congress 2022

What the heck do "declarative" and "reactive" actually mean? thumbnail
30:40
almost 4 years ago

What the heck do "declarative" and "reactive" actually mean?

André Kovac

The Design and Evolution of C++ thumbnail
1:01:15
about 4 years ago

The Design and Evolution of C++

Bjarne Stroustrup

But, you're not Facebook thumbnail
31:12
about 4 years ago

But, you're not Facebook

Kristijan Ristovski

Communicate efficiently with Software Architecture Diagrams thumbnail
28:11
about 4 years ago

Communicate efficiently with Software Architecture Diagrams

Simon Lasselsberger

Get Ready for Jakarta EE 10! thumbnail
42:53
about 4 years ago

Get Ready for Jakarta EE 10!

Ivar Grimstad

Keep your code refactorable thumbnail
26:46
about 4 years ago

Keep your code refactorable

Gerrit Stapper

Upcoming sessions on this topic

Open session

World Congress 2026 North America

AI Decision Observability: Enabling Transparency and Trust in Intelligent Systems

Amjad Shaikh, Soumil Mandal

Amjad Shaikh
Soumil Mandal
Open session

World Congress 2026 North America

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

Him Raj Singh

PayPal, Manager, Software Engineer

Him Raj Singh
Open session

World Congress 2026 North America

You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

Reinventing Testing Practices in the AI Era

Eric Deandrea

Java Champion & Senior Principal Software Engineer, IBM

Eric Deandrea
Open session

World Congress 2026 North America

Developer Liability in the AI Agent Era: Building Responsibly

Alla Barbalat

Freelancer Trade Show Spokesmodel and Tech Event Host

Alla Barbalat
Open session

World Congress 2026 North America

AI ROI: The Hard Unit Economics of AI-Native Engineering

Manu Gurudatha

Manu Gurudatha, VP of Engineering at PagerDuty

Manu Gurudatha

Related videos

Rethinking Recruiting: What you didn’t know about Responsible AI thumbnail
28:07
WWC 2024 • almost 2 years ago

Rethinking Recruiting: What you didn’t know about Responsible AI

Jaap Kersten, Vincent Slot

Explainable machine learning explained thumbnail
56:46
LIVE • about 3 years ago

Explainable machine learning explained

Karol Przystalski

Introduction to Responsible AI: Balancing Value and Risk thumbnail
31:21
Europe 2026 Virtual • about 1 month ago

Introduction to Responsible AI: Balancing Value and Risk

Seppe Housen

Responsible AI in Practice: Real-World Examples and Challenges thumbnail
29:35
WWC 2024 • almost 2 years ago

Responsible AI in Practice: Real-World Examples and Challenges

Björn Bringmann, Mina Saidze, Ray Eitel-Porter, Steffen Bosse

Three years of putting LLMs into Software - Lessons learned thumbnail
26:30
WWC 2025 • 12 months ago

Three years of putting LLMs into Software - Lessons learned

Simon A.T. Jiménez

Staying Safe in the AI Future thumbnail
27:26
WWC 2022 • about 4 years ago

Staying Safe in the AI Future

Cassie Kozyrkov

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.