How Will AI Impact Gender Bias?

Our first topic of discussion explored the practical implications and real-world consequences of AI gender bias, highlighting how biased AI systems impact decision-making in areas such as hiring, finance and healthcare.

Our panel examined how specific applications of AI can amplify harm, the ways in which the current DE&I backlash may worsen gender bias in AI, and findings from a Harvard Business Review study on gender disparity in AI adoption.

Our second topic of this discussion examined whether AI bias is inherent in the source data or introduced during development, and the negative impacts of the disconnect between AI developers and end users.

Our panel explored gender bias in training data, why blaming the data alone is an insufficient argument and how the industry can take greater accountability in building fairer AI systems.

Our final topic of discussion focused on how AI can be harnessed to drive more equitable outcomes, exploring techniques for mitigating gender bias and the importance of interpretable AI systems.

Our panel highlighted the lack of bias testing in automated AI evaluation, as well as the critical role of executive leadership and boards in shaping AI development and minimising risks such as AI gender bias.

Finally, at our panel discussion, the audience explored critical questions on AI bias, from the causal links between biased training data and harmful outcomes to whether current legal frameworks are enough to counter gender bias.

They also debated whether AI merely reflects societal biases or actively amplifies them in unpredictable ways. These discussions highlighted the urgent need for transparency, accountability, and ethical AI development.