Artificial intelligence is rapidly transforming the automotive industry, from ADAS and autonomous driving to predictive maintenance, quality control, and in-vehicle personalisation. As AI systems increasingly influence safety-critical and regulated decisions, one fundamental question sits at the center: how can we trust AI models enough to deploy them responsibly at scale?
To address this challenge, we are pleased to introduce an upcoming Explainable AI (XAI) masterclass, tailored specifically for automotive professionals who need to design AI models that are accountable, transparent, and trustworthy. This session explores the principles, techniques, and real-world practices required to move from opaque “black-box” models to AI systems that engineers, regulators, and customers can confidently trust.
Why Explainable AI Matters in the Automotive Industry
Automotive AI systems must meet exceptionally high standards for safety, compliance, and accountability. Explainable AI is essential to enable:
- Regulatory compliance (including emerging AI regulations and automotive safety standards).
- Traceability of decisions in safety-critical systems.
- Debugging and validation of complex machine learning models.
- Bias detection and mitigation in both data and predictions.
- Trust and acceptance from regulators, partners, and end users.
Without explainability, even high-performing models may be difficult or impossible to certify, validate, or deploy responsibly.
Online Masterclass
While the session is delivered online, its hands-on, practical, and in-depth approach goes beyond a traditional webinar. Participants will not only learn what Explainable AI is, but also how to apply it directly within real automotive use cases and development pipelines.
For this reason, we define the event as an online masterclass, combining depth and interactivity with the accessibility and convenience of a webinar format.
What you will learn
By the end of this session, participants will understand how to move beyond accuracy alone and design AI systems that are:
- Transparent: their behavior can be understood and inspected.
- Accountable: decisions can be justified and traced.
- Trustworthy: safe to deploy in real automotive environments.
In this session, participants will gain practical, actionable insights into:
- The foundations of Explainable AI (XAI) and model interpretability.
- The distinction between inherently transparent models and post-hoc explainability.
- Key XAI techniques for automotive applications (for example: feature attribution and local versus global explanations).
- How to design AI systems that are accountable by design
- Integrating explainability into development, testing, and validation workflows
- Supporting audits, documentation, and regulatory reviews using XAI
- Real-world automotive examples and lessons learned
The focus is firmly on practical decision-making, not abstract theory. Our Chief Data Scientist, Burcu Kolbay, will be answering questions at the end.
Who Should Attend
This masterclass is designed for professionals working with AI across the automotive ecosystem, including:
- AI and Machine Learning Engineers
- Data Scientists
- ADAS and Autonomous Driving Engineers
- Functional Safety and Compliance Managers
- R&D and Innovation Leaders
- Product Owners and Technical Managers
No legal background is required. Applied, engineering-focused perspectives take center stage.
Event Details
- Format: Online (live)
- Duration: 60 minutes
- Level: Intermediate to advanced
- Language: English
Registration
Join this masterclass to ensure your AI solutions are powerful, but also understandable, accountable, and trusted.
📨 Click here to register now to secure your place and help shape the future of trustworthy automotive AI.