Counterfactual explanations — answering the question “what would need to change for a different outcome?” — are among the most powerful tools in the Explainable AI toolbox. They bridge the gap between abstract model reasoning and actionable insights. In this talk, we go beyond conventional methods and explore how genetic algorithms can evolve counterfactuals that are both realistic and actionable, offering fresh ways to understand data and model behavior.

Drawing from real-world scenarios and code examples using the German Credit Risk dataset, we’ll demonstrate how to:
* Use genetic algorithms to search for minimal, plausible input changes that flip model predictions.
* Evaluate and constrain counterfactuals for realism and interpretability.
* Detect potential model flaws and dataset biases through systematic “what-if” analysis.

Key Takeaways:
* Generate counterfactual explanations with genetic algorithms to enhance transparency and trust.
* Reveal model weaknesses and dataset flaws through structured “what-if” analysis.
* Integrate counterfactual techniques into real-world AI workflows with practical Python examples.

Audience:
This session is ideal for data scientists, ML practitioners, and AI educators who want practical, optimization-driven tools for explaining black-box models. Whether you’re designing responsible models, auditing decisions, or teaching interpretability, you’ll leave with strategies to evolve your explanations — literally.

Level: Intermediate

Keywords: Explainable AI, Responsible AI, Counterfactuals, Genetic Algorithms, Model Interpretability, Python, Optimization

Based on my book 'Hands-On Genetic Algorithms with Python', 2nd edition