AI has immense potential. It can improve our productivity and also our predictions and decisions, which in turn can help reduce disparities. We all have unconscious biases that influence our choices and actions. It can be hard to understand how we’ve arrived at them and whether biases have played a role. Because AI is programmed and can be audited and changed, it can theoretically help us be more accurate and fairer.  But AI is built with data that has been generated from databases of existing information, such as images, texts, and historical data, so our biases become built-in. Its effects on marginalized groups are often unrecognized even as they are perpetuated, because the technology appears to be objective and neutral. Our report on AI research outlines what scholars have found about how AI can contribute to inequity and what can be done to mitigate it. 

It's tempting to think that an inclusive approach to analytics is just an add-on to a day-to-day job, or worse, isn't profitable. But those are all just myths, say GATE's Sarah Kaplan and Lechin Lu. Gender Analytics is a process to embed insights about gender and its intersections with race, ethnicity, disability, sexual orientation, Indigeneity and other factors to create inclusive product, service and policy design. Despite having a potentially significant impact on society, the idea of inclusive analytics is often misunderstood, though it’s recently risen to prominence as companies prioritize equity and diversity not just in their talent management but also in how they go to market. And there’s a significant upside to the shift: Applying a gender and inclusion lens to product and service development could result in new opportunities for businesses.

It would be difficult to find a field today where AI is not involved in some respect. It has become so ubiquitous that some researchers have suggested it is a new type of infrastructure. Rather than being physical and visible like roads, AI is often invisible, but it is nevertheless a moderator of social relations and organizational practices and actions — including the distribution of power. Social relations and values have long been reflected and reproduced in technology, and AI is no exception. But this also means that the enduring bias, discrimination and inequality that are deeply rooted in society may also be deeply rooted in this technology.

Today, AI is used by organizations across many sectors for a variety of purposes, from hiring employees, to assessing risk, to making investment recommendations, to recommending criminal sentencing. However, it is well-known that social relations and contexts are reflected and reproduced in technology, and AI is no exception: it has the potential to reinforce underlying biases, discrimination, and inequities. Although AI can be used to benefit marginalized groups, a concerted focus on equity in AI by businesses and governments is necessary to mitigate possible harms. Here we provide a resource for scholars and practitioners for viewing AI through the lens of equity, with the objectives of synthesizing existing research and knowledge about the connection between AI and (in)equity and suggesting considerations for public and private sector leaders to be aware of when implementing AI. The key Insight: AI is a double-edged sword, with potential to both mitigate and reinforce bias.