Breaking The AI Bias: How To Define Fairness To Deliver Fairer Models

At a basic level, AI learns from our history. Unfortunately, much of societal history includes some discrimination and inequality. It's therefore essential that data practitioners consider this in their work as AI built without acknowledgement of bias will replicate and even exacerbate this discrimination. This is particularly concerning when you consider the influence AI is already exerting over our lives. McKinsey's recent digital trust survey found that less than a quarter of executives are actively mitigating against risks posed by AI models (this includes fairness and bias).