The Use Of Artificial Intelligence In Business Codifies Gendered Ageism. How Do We Fix It? – Forbes

The Use Of Artificial Intelligence In Business Codifies Gendered Ageism. How Do We Fix It? – Forbes

In 2017, the National Bureau of Economic Research conducted a large study about age discrimination in hiring that confirms the prevalence of gendered ageism. “Based on evidence from over 40,000 job applications, we find robust evidence of age discrimination in hiring against older women, especially those near retirement age.” The call back rate for older women compared to their younger female counterparts was significantly lower despite the fact that the only difference in the resumes was their age. The evaluation of resumes like many other processes in business today is managed by technology, specifically artificial intelligence.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. More and more businesses are expanding their use of AI because it can significantly lower costs, increase efficiency and boost productivity. But as more organizations adopt AI and use data to make decisions like hiring, what are the consequences of not involving a diverse set of individuals, especially women, in the design process?

I reached out to a Stela Lupushor, founder of Reframe Work, to better understand how the lack of women involved in technology design affects the key decisions companies are making. Lupushor helped me understand how AI is used in the workplace today as well as how the design and the interpretation of data, if biased, influences key decisions about hiring, compensation, promotion, etc. that affect women in the workplace, especially older women.

According to Lupushor, AI provides automation of different tasks. An example is using AI to review resumes and identify candidates who profiles might be a match for a job opening based on the job description and key words in an applicant’s profile. It is usually quite simplistic and rigid since it lacks the nuances of context and secondary implications.

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AI augments human decision making as it enables significantly more complex actions. An example is looking at applicants and predicting their performance or likelihood of attrition, or recommending edits to the job description that will make it less biased toward people earlier in their career or certain characteristics that will more likely be represented in certain genders or under-privileged groups. There is great potential for the bias to interfere with the process.

“Most of the AI-based HR tools, especially in the context of HR processes use some form of Machine Learning, an approach that identifies patterns in training data which includes many past examples of tasks and past outcomes. It assumes these patterns will hold when applied to new cases. For example, a machine learning algorithm designed to predict high potential job candidates might look at historical records of previous hires and look for patterns in the types of hires and their characteristics that correlate better with performance.”

Lupushor added, “if you consider the differences in the lifecycle patterns of women, especially later in the career, the likelihood of someone who is over 45 years old, with multiple career gaps (to take care of children or aging parents) and multiple career pivots to gain necessary flexibility or support a partner’s career will rarely end up in the training data correlated with high performance.

In a way, AI is now being used to codify the biases we already have in society and it is a dangerous road, especially when it comes to employment decisions. Having women involved in the design process, deciding what training data is used and for what context, how it is processed and optimized, and interrogating the results for potential bias could bring the necessary counterbalance. Not to mention that involving women in such lucrative professions will provide the so-needed financial security and job stability.”

Bonnie Marcus: How can we use technology to affect positive change for older women in the workplace?

Stela Lupushor: Companies must examine all HR processes and understand the disparate impact and exclusion that might happen as a result of poorly designed processes or poorly executed policies. This is great news because there is plenty of data now to do this examination. There are also many technical solutions to address the downsides.

When hiring new people – what does your candidates pipeline contain? Are you intentionally inviting and selecting candidates from underprivileged or under-tapped groups? Are you looking at different sourcing channels and understanding the performance of each, and considering the unintended consequences of channel choices (i.e. if you are advertising your jobs on Snapchat your candidate pool will most likely be skewed towards younger, early in their career workers). How many diverse candidates make it to interviews and how many get down-selected (this could point to manager’s biases and inform you that a bias training might be needed). One of the great advantages of technology in the past 16-18 months is that it allowed for many to continue to work remotely and proved to the leadership that not every job needs to be done in person. This not only allows organizations to tap into talent that previously was not considered, but also brings more balance, especially for women, and gives them the ability to juggle work and family.

When it comes to development of talent – who are you investing in and are those disproportionately individuals considered “high potentials” which more likely will be again skewed towards younger generations. All age and gender groups need to be developed and invested in. The data in this case can be shedding the light on the bias. The technology can also be a wonderful enabler to scale the development programs from a resource intensive and high priced to a whole selection of online and asynchronous learning opportunities. 

Compensation practices and resulting pay disparity can be easily understood by looking at the compensation data between different groups and levels. Examining it over time will also shed the light on how your pay practices are applied and impact the income trajectory for different groups or segments of the workforce. 

Who are you retaining? Critically assess who is on the retention list and understand who are you predominantly optimizing for (which workforce segments end up being advanced or rewarded). Are you primarily retaining younger workers? Are there sufficient older workers, and especially older women to serve as role models or mentors to younger female employees? Similar to the hiring example, technology can enable organizations to retain women who need more flexibility in their schedules and allow them to work remotely or on part-time basis without stepping out of the labor market altogether. 

How flexible are your policies, schedules and benefits? Are you inclusive in your design? For example, by adopting parental leave you are enabling only those with children to take time off whereas adopting a caregiving leave will expand the number of workers who can take advantage of such leave.

Lastly – how are you structuring the ways of working? Are you using technology to enable team collaboration, transparency in communications, and are you sufficiently investing in the skills building to use such technologies. Many of the workplace tools were designed by engineers for engineers and might now be as easy to adapt without adequate training.

Despite the complexity of this problem, Lupushor added it’s important to note that the recently issued human capital disclosure requirements from SEC will encourage companies to bring some transparency to their workplace practices and outcomes, and such transparency will also bring the need to address the issues – a perfect example of how data and quantifying the impact of workplace practices can mitigate the inequities. 

Technology provides the tools to counter gendered ageism in workplace policies and practices. Leadership must provide the motivation and resources for change to happen.

Bonnie Marcus, M.ED, is the author of Not Done Yet! How Women Over 50 Regain Their Confidence and Claim Workplace Power and The Politics of Promotion: How High Achieving Women Get Ahead and Stay Ahead. An executive coach and speaker, Bonnie is also host of the podcast, Badass Women At Any Age.

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