Tech Track

Bias in algorithms and why should you care

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.

Raluca Crisan

Raluca is a co-founder at Etiq, a start-up that tackles unintended bias in automated decision making and she has spent the past 10 years working in data science and analytics across a variety of sectors. Raluca’s experience spans managing large teams to hands-on data product development. Raluca has a BA from Amherst College in Massachusetts, and an MA from University of York

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