How to embrace diversity in employment through AI
In conversations about diversity and inclusion in the workplace, the practical benefits of a diverse workforce can sometimes be lost in chatter. When that happens, it’s worth remembering: The 20 most diverse companies in the S&P 500 achieve higher long-term profitability than their less diverse counterparts. Companies in the highest quartile are more likely to outperform profitability, have superior value, and have good financial results because of diversity in leadership. By 2025, improving gender equality in the workplace alone could add $ 12 trillion to global GDP. Despite these obvious benefits, 48% of companies are either not on track to meet their diversity goals or have no goals at all.
How can struggling companies improve their diversity outcomes? The first step is to recognize human biases in employment. On average, recruits spend 7 seconds reviewing an individual resume. In that short amount of time, recruiters often rely on quick judgments that could be colored by similarity bias, contrasting effect, and more. While a human rights recruit may not have time to review each application in more detail, they should be aware of the subjectivity that human observers bring to the recruitment process.
Is software the solution? 90% of businesses and 68% of small businesses use candidate tracking systems, but recruitment software is not immune to bias either. Using software that relies on keywords in a resume search may reflect a candidate’s ability to write a resume with keywords, but not their actual qualifications. Searching for synonyms solves part of the problem, though not all. The case regarding the hiring of software that went wrong is Amazon. In 2018, Amazon discontinued its top-notch AI for hiring after learning to punish resumes that include the word “female” or the aforementioned women’s colleges. AIs are not programmed with human prejudice; they teach them by processing data and identifying patterns. If the specified data is biased, its results will reflect back bias. Amazon’s AI is qualified for a decade of resumes and hiring decisions. As a result, AI has picked up and exaggerated with existing biases in the screening process.
What can be done to improve the situation? 81% of HR professionals admit that their current practices are average or worse in the area of diversity. Many are not sure how to train AI without bias. Some ways to remove data bias that AI examines include collecting data from different industries, jobs, and candidates, removing factors such as age, gender, and names from the initial screening, and considering how well a candidate and company fit from both perspectives. Important steps to be taken on the human side are setting clear goals for improvement that can be monitored, establishing diversity training standards and learning opportunities from colleagues, strategic partnerships with external organizations, schools and colleges, and ensuring governance and policies to support diversity and inclusion. each level.
Overcoming employment bias can help a company thrive. And in the words of Lena Waitha, an Emmy-winning writer, producer and actress, “the only way you really see change is to help create it.”