AI UK - Doing Better in Data Science – From Algorithmic Fairness to Diversity

Explore the critical intersection of diversity, fairness, and responsible AI in this thought-provoking panel discussion featuring leading researchers in data science and AI. Delve into the multifaceted challenges of bias in AI systems and the importance of diversity throughout the AI lifecycle. Gain insights on tackling issues ranging from data representativeness to algorithmic fairness, and learn how to foster more equitable access to AI interventions across society, science, and the economy. Engage with expert perspectives on improving diversity in data science teams and creating a more inclusive AI landscape. Discover actionable strategies for addressing data colonialism, group parity, and the risks associated with neglecting diversity in AI development. Leave equipped with a deeper understanding of how the AI and data science community can advance towards a more fair, transparent, and ethically-sound future.

Introduction
Welcome
Panelists
Concerns
Three points of reflection
Why diversity is important
Hal
The role of fairness in research
Diversity and transformation
Diversity and equality
Audience questions
Data diversity
Group parity
Improving diversity
Data colonialism
Reflection
Risk of not embracing diversity
What can we do
Risk