Technologies that use artificial intelligence (AI) have become integrated into every part of human life, informing the news people see, the advertisements people are shown, and even the GPS directions people are given. The use of AI is expanding, and the powerful computers and complex algorithms behind these technologies are becoming increasingly advanced as companies rapidly invest in research and development around AI.

Soon, AI will be widely used to help diagnose diseases, drive cars, and police neighbourhoods. These uses may seem like futuristic fictions, but they already exist in the world (and are gaining momentum).

What if people learned, however, that diagnostic AI is less accurate for non-male genders? Or that self-driving cars are less likely to detect pedestrians with darker complexions? Or that predictive policing is more likely to negatively impact historically marginalized groups, such as BIPOC and LGBTQ+ communities, those living with mental illness, and/or those who are homeless or from low socioeconomic situations?

Would people be so quick to accept and adopt these technologies, no questions asked? Or would people treat AI as imperfect and fallible, like the people who create it?

Using a systems-based approach to analyze the AI technology lifecycle and ecosystem in which these technologies are embedded, this synthesis map illustrates the context, AI product lifecycle, and potential intervention points.