That's a really good point, and it's not just racial bias. It's gender bias as well. Even tech firms have been stymied in their efforts to level the playing field.
There are two things here, and one is garbage in, garbage out. The data that you use, if it's biased, is going to replicate bias. The second is making sure you have diverse teams that are sensitive to these issues.
A third thing is disclosure. I think disclosure is absolutely critical.
A fourth thing is “human in the loop”. I'm working with a number of public sector organizations, for example, that are experimenting with large-scale AI tools. One of the critical things is to do experiments where you compare the results you get from the AI-enabled processes to what you would get with humans. Then you try to figure out if AI is amplifying bias or reducing bias, because sometimes it will cut out the bias that's associated with “we play golf together” or “I went to Queen's University”.
There are huge opportunities for good and evil, in my view, but transparency, human in the loop and inclusion are fundamental principles.