12-03, 18:00–19:30 (UTC), AI/ML Track
In this 90-minute workshop, machine learning engineers and data scientists will learn practical techniques for identifying and mitigating age bias in AI-driven hiring systems. We’ll explore fairness metrics like statistical parity, counterfactual fairness, and equalized odds, and demonstrate how tools such as Fairlearn, Aequitas, and IBM Fairness 360 can be used to monitor and improve model fairness. Through hands-on exercises, participants will walk away with the skills to evaluate and de-bias models in high-risk areas like recruitment.
AI tools used in hiring can unintentionally perpetuate discrimination in protected characteristics such as age, gender and ethnicity, leading to significant real-world harm. This workshop provides a practical, hands-on approach to addressing biases in machine learning models, using the example of AI-powered hiring tools. You’ll train a neural network on biased datasets, evaluate fairness metrics, and work with state-of-the-art tools like Fairlearn and Google’s What-If Tool to measure and mitigate bias. By the end of the session, participants will be equipped with the knowledge and tools to tackle bias in their own projects and ensure fairer AI systems.
No previous knowledge expected
John Sandall is the CEO and Principal Data Scientist at Coefficient.
His experience in data science and software engineering spans multiple industries and applications, and his passion for the power of data extends far beyond his work for Coefficient’s clients. In April 2017 he created SixFifty in order to predict the UK General Election using open data and advanced modelling techniques. Previous experience includes Lead Data Scientist at YPlan, business analytics at Apple, genomics research at Imperial College London, building an ed-tech startup at Knodium, developing strategy & technological infrastructure for international non-profit startup STIR Education, and losing sleep to many hackathons along the way.
John is also a co-organiser of PyData London, co-founded Humble Data in 2019 to promote diversity in data science through a programme of free bootcamps, and in 2020 was a Committee Chair for the PyData Global Conference. He is currently a Fellow of Newspeak House with interests in open data, AI ethics and promoting diversity in tech.