unbAIsed: reducing bias in AI for healthcare
AI has an astonishing potential to assist clinical decision-making and revolutionize global health, but this can only be possible if the use of AI in healthcare takes into account the needs of diverse populations. When AI is biased, it is prone to reinforcing inequality, which can lead to injustices in healthcare, making already vulnerable patients more susceptible to fatal outcomes and misdiagnoses.
In this session, we'll target some of the main challenges that need to be addressed in order to move towards fairness in AI for healthcare, including gender and racial bias, data gaps, and ethical concerns. We will divide into breakout groups, where we will brainstorm recommendations to solve these challenges. We will also explore how AI can help make invisible minorities visible and reduce inequalities in healthcare, and how open practices can be used in the fight against bias.