In a previous article, we discussed some of the most pressing concerns in the field of AI, such as lack of human accountability for bad or unethical decision making by automated systems, and poor understanding of sources of bias, as well as how these issues are fueled by the over-reliance on “black box” methodologies and supposedly “objective” data. As consumers become increasingly more savvy about the impact that analytics have on their lives, and the industry turns its eyes towards both federal and self-regulation to provide a safety net against these concerns, it becomes imperative for any analytics enterprise to keep these issues on the forefront of their strategy as a way to prepare for shifts in infrastructure, methodologies and considerations that will have to be taken into account once the implications of these concerns have fully taken hold.
On this occasion I’d like to focus on one of the recommendations listed at the end of the aforementioned article; “Hire diverse teams.” The benefits of hiring diverse teams are already well understood; in Diversity Matters, a report published by McKinsey in 2015, it was found that companies in the top quartile of gender diversity were 15 percent more likely to have financial returns above their national industry median. When considering racial/ethnic diversity, this increased to 35 percent. Meanwhile, companies in the bottom quartile for both gender and ethnicity/race were statistically less likely to achieve above-average financial returns than the average companies in the dataset.