What Does It Mean to Be Biased or Fair?

When you create or use technology, especially involving artificial intelligence or automation, it’s important to ask yourself questions of bias and fairness.

In the context of statistics, bias is a systematic deviation from the truth or error. From a social and legal perspective, researcher and professor Kate Crawford defines bias as, “Judgement based on preconceived notions or prejudices, as opposed to the impartial evaluation of facts.”

Fairness is defined as a decision made free of self-interest, prejudice, or favouritism. In reality, it’s nearly impossible for a decision to be perfectly fair. A panel at the Association for Computing Machinery's Conference on Fairness, Accountability, and Transparency in 2018 developed a list of over 21 definitions of fairness. If there are so many ways to think about fairness, how can you tell if humans or machines are making fair decisions?

To make a more informed decision, it’s fundamental to understand the impact of that decision. A decision that benefits the largest number of people still excludes a minority, which is unfair if that minority is often overlooked. You need to ask yourself: Are some individuals or groups disproportionately impacted by a decision? Does systemic bias in past decisions or inaccurate data make some groups less likely to receive a fair or impartial assessment? If the answer is yes, then you must decide if, and how, you should optimize to protect those individuals, even if it won’t benefit the majority.