Session Outline
AI models often inherit biases from the data they are trained on, which can result in unfair or discriminatory outcomes. As AI becomes increasingly integrated into our daily lives, it is crucial to prioritise inclusion and equity in its development. This talk will explore the causes of AI discrimination, drawing on real-world examples and research findings, while also offering practical solutions to mitigate these issues. From policy recommendations to technical solutions, this talk will provide insights on how developers in the data sphere can build more inclusive AI systems.
Key Takeaways
- – Gain insights into the sources of bias in AI systems
- Discover best practices for incorporating inclusivity into AI development
- Discover how by asking questions we can minimise the bias in AI
