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What inspires us

Academic Research

Our data science team is always learning and experimenting which means they are closely connected to other research in this area around the world.

Notes from the frontier: Tackling bias in AI (and in humans)

Notes from the frontier: Tackling bias in AI (and in humans)

Why we love it

Authors provide a succinct yet comprehensive overview of how biases can be baked into AI, ways to mitigate it and when developed well, how AI can help reduce human biases in decision making. It is written for a non-technical audience but includes a great list of original research work in the Endnotes section that anyone interested in further reading can access.

What I learnt from it

It is great to see our own experience building AI solutions in a domain (talent acquisition) where unconscious bias is commonplace, resonating well with what the authors describe. Especially in how AI can help bring human bias to light and steps to follow in building AI solutions with no measurable biases. Some of the key points include:

  • Having a clear and applicable definition of bias and fairness. This includes thinking about group vs. individual fairness, protected characteristics, predictive parity vs. error rate parity etc
  • Being aware of biases in data, data collection methods, and societally unacceptable correlations (algorithmic biases) learnt by algorithms, all of which are discoverable within a sound machine learning process
  • Using methods such as Local Interpretable Model-agnostic Explanations (LIME) to explain the outcomes of seemingly complex algorithms that act as black boxes.

Why it’s a must

As AI becomes pervasive, the topic of algorithmic bias and fairness has attracted lot of attention. This is a great short paper for decision makers, especially at the C-level to demystify the topic. I would say a must read for all CHRO’s exploring the use of AI in their workflows. Especially the five suggestions listed in the conclusion of the paper forms a framework to maximise fairness and minimise bias when using AI.

Who should pay attention

CHROs, Hiring managers, Business leaders

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