The impact of bias on corporate decision-making tools can lead companies to miss out on hiring the best talent and bringing satisfied customers back for more paid intel
to help its customers assess artificial intelligence models for discrimination and suggest adjustments, while also assessing trade-offs in accuracy.However, tools alone won’t get the job done. As noted by Anna Bethke, head of Intel Corp.’s AI for Social Good initiative, “A common misconception is the belief that bias can be solved by algorithms, when bias is also a cultural issue that requires cultural responses like dialogue and debate.
For this reason, it’s critical that companies involve diverse human teams in their process: internal or external auditors whose incentives differ from those of the users of the algorithms and who can pose counterfactuals that challenge underlying assumptions and results. Ensuring human judgment remains integral can help developers both responsibly manage their AI tools and harness their benefits.
In applying a machine learning algorithm to a particular data set, businesses need to ask themselves: What demographic is intended to be captured by the data set? What characteristics may be over- or underrepresented, and is this model appropriate for this data set? If data are lacking in representation, what additional data are needed to balance the data set? If such data do not exist, consider alternatives, such as incorporating synthetically created data or addressing the original problem in...
Finally, selecting and scoping the right data set from a project’s outset also helps mitigate the risk of biased outcomes. Instead of opportunistically processing available data through deep learning models, executives should identify the specific business problem to be solved. Only then can their teams look for and scope the appropriate data set and, if one is not available, consider alternatives.Google researchers have proposed ato accompany every machine learning model.
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