At the Grace Hopper Celebration this week, the topic of women in AI will be at the forefront as we celebrate the achievements of women in technology and take stock of where we need to continue to push for progress.
The pandemic has put a spotlight on the need for accelerated digital transformation efforts across every industry. In fact, more than half of the respondents in our 2021 AlixPartners Disruption Index shared that investment in new technology is their top strategy to counter the disruptive forces at play.
The chronic under-representation of women in the tech industry at large becomes an even greater concern in these times of disruption. A level deeper, and as highlighted in the Women’s Forum for the Economy & Society AI report, women hold just 22% of key positions within AI, such as developers and leaders. The picture is even bleaker in subsectors such as machine learning research, where, according to a report by Wired Magazine, just 12% of machine learning researchers are women.
Building toward gender parity is more critical than ever, not least after the events of the past 18 months, which have, according to the World Economic Forum, set back the Global Gender Gap by a generation. Women representation is so critical not only for business success – research indicates a strong link between gender equity and financial results – but also for the clear social implications in empowering women in the workforce as well.
Bias from the outset will damage data interpretation
Diverse perspectives and perceptions are vital to achieving the goals of AI and machine learning technologies. To truly overcome any biases in AI systems’ manipulation and interpretation of data, there is no room for inherent bias from the outset in the teams that develop such capabilities.
After stark introspection within the industry, it’s clear that action must follow. To achieve collective progress, the Women’s Forum has highlighted three key focus areas to move the needle for women in AI:
- Build internal foundations: this requires a better understanding of how AI impacts different genders and how institutions can promote AI diversity and inclusion by supporting outside organizations working in support of this goal
- Proactive recruitment: Starting at the source, filling the pipeline more diversely and inclusively will directly affect the journey towards greater gender parity, provided the right structures are in place to eliminate any employment biases
- Measure progress: In an industry driven by data, carefully conceived KPIs will hold our efforts to account and allow us to promote and celebrate success, which is likely to perpetuate greater interest in AI among women
There is a long road ahead, but this issue is more important than ever, the potential rewards for the AI sector are significant, and, put simply, we have reached a point where doing nothing is not an option.