You might think that algorithms are blind to gender, race, age. Unfortunately they are not. The data that feeds them is a reflection of our current society. If men are consistently ranked higher for technical roles in an organisation, an algorithm based on existing employees data will be less likely to recruit women for those roles. If the data used for building a facial recognition algorithm uses no faces of black women it will be much worse at recognising black women.

As algorithms are more and more prevalent, understanding and minimising their biases becomes more and important.

This talk explores several approaches to algorithmic bias. Come and learn not just pitfalls but potential solutions to this 21st century problem.