We have all read stories of facial recognition software that fails to recognize dark-skinned faces, or robo-loan officers that deny mortgages to certain groups. As a growing body of research has made clear, algorithms created by non-representative groups have resulted in AI that perpetuates the inequities already prevalent in our society. As more companies rely more heavily on data and AI, these problems of algorithmic discrimination may only become worse.
Most companies know this by now. What they’re trying to figure out is: how can they avoid becoming yet another bad example?
The short answer is, thinking critically about the data you’re collecting and how you’re using it needs to be everyone’s job. Expanding the circle of who is in the room helping to question, build, and monitor algorithms is the only way that we will develop responsible AI. Doing that work requires data literacy — the ability to parse and organize complex data, interpret and summarize information, develop predictions, or appreciate the ethical implications of algorithms. Like math, it can be learned in beginner and advanced modes, spans multiple disciplines, and is often more practical than academic.
Building up data literacy in an organization can also help diversify the data teams who are at the forefront of making critical decisions about how data will be collected, processed, and deployed. The importance of diverse data teams is something I learned firsthand over more than a decade as a quant fund manager. It’s a commonly held belief that more diverse portfolios outperform because they reduce risk. But it is analogously true that diverse teams outperform because they reduce the risk of groupthink. By investing in data literacy across the enterprise, businesses can bring more divergent and creative perspectives to bear on both mitigating the risk of algorithmic bias — and identifying other efficiencies and opportunities that data can often reveal.
But a look at the data tells us that most companies are still struggling to build data literacy. Ninety percent of business leaders cite data literacy as key to company success, but only 25% of workers feel confident in their data skills. Not only that, but some estimates suggest that nearly nine in 10 data science professionals are white, and just 18% are women. Research from General Assembly indicates that when it comes to diversity, data science lags behind even other tech-oriented disciplines, like digital marketing and user experience design.
Why, despite the obvious need and increasing urgency, are we not teaching data literacy systematically and at scale? That’s the question that has animated my work for the past several years. At Correlation One, which I co-founded after leaving my fund in 2018, my team works with financial services firms and Fortune 500 companies to build more inclusive pipelines of data science talent. By helping employers from Target to Johnson & Johnson to the Government of Colombia assess the capabilities of their current workforce, and providing free training to aspiring data scientists (like our partnership with SoftBank and the city of Miami), we’ve gotten a front-row seat to better understand the urgent need for a more data-literate workforce, and helped companies put specific practices in place to make that goal a reality.
Here are some of the strategies we use. READ MORE
by Rasheed Sabar
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