202 years. According to a recent report by the World Economic Forum, unless we speed up the process, how long will it take to bridge the gender gap.
And looking at the figures, it makes sense. A recent study by Pew Research showed that the gender pay gap has narrowed, but remains relatively stable …
In one way or another, we learned how to make self-driving cars and smartphones with more computing than computers before, but we still don’t know how to push men and women alike. is.
Fortunately, social pressure and government leadership are increasing. Movements such as the Women’s March and #metoo highlight discrimination in the headlines, while many countries, including Denmark, the United Kingdom, France and Germany, now require companies to report gender pay differentials every year.
It is not just society and governments. With a lot of evidence for the bottom line benefits, diversity can bring in a company, looking to bridge the pay gap to attract more and more talent.
With so much will and encouragement to close the gap in the end, why are we still not making a profit? The emergence of new technologies may enable us to respond.
Why don’t you progress
In a strange turn of events, Google recently stated that it found a pay discrepancy that would actually pay salaries of male engineers in 2019. Rather than being a salary differential, the discrepancy set a large percentage of discretionary money. For women engineers. As a result, they corrected this error by increasing the expected salary for thousands of male Google employees.
He came from a company facing potential class action lawsuits by current and former engineers and was facing an investigation by the US Department of Labor for wages and promotions, and of course the new report raises eyebrows (and some In cases the eyes are wrapped).
Google data only compares equal pay to employees. According to the New York Times, one of the litigants who filed a lawsuit against the company claimed that it was ranked lower than the same experienced male engineers. Critics argue that the variance may actually be more experienced women who are employed at lower levels and then allocate more discretionary salaries to the apparent mistake in settlement.
Such examples clearly illustrate the rift in our data when it comes to resolving the gender pay gap.
One of the biggest problems companies face is showing signs of a difference, but not really having the insight to pinpoint the causes within their unique structural structure. Currently, most companies have their payroll and HR data on two different systems, meaning that discrepancies are not counted due to problems in recruitment or promotion structures.
Because of this lack of ideas, we see that many companies spend a lot of time and resources on strategies that simply do not work. But AI and machine learning may be able to provide a more nuanced view of what is actually going on.
Can AI and machine learning help bridge the gap?
Zara Nano, CEO of GapSquare, a software service that helps tackle the gender pay gap through artificial intelligence techniques and machine learning, made her point clear.
Often, companies make an almost obsessive push to solve pay-per-pay problems quickly. For example, some people will try to offer higher starting salaries to attract more female applicants in the short term, but in the end, if inequalities persist in settlement structures and later in promotions and wages, this will lead to long term There will be no solution.
“Instead, we need to help companies understand how to cover these discrepancies in a more sustainable way that does not harm either sex. It is not about stealing a slice of pie. He did all this about making the pie evenly.
Gapsquare technology allows companies to run payroll and HR data through a system. Using artificial intelligence technology and machine learning, they can integrate and analyze data together to provide expertise in three key areas:
Determine any current pay discrepancies based on gender, race, disability or other employee characteristics
Why these gaps exist based on a mixture of educational data and experience from the background
Opportunities for more data-driven decision making to reduce these gaps
Why these gaps exist based on a mixture of educational data and experience from the background
Opportunities for more data-driven decision making to reduce these gaps
As explained by Nanu, the company’s data is often based on “complex compensation and structure with more than a thousand different elements of compensation.” Less obvious factors such as housing and car allowances.
With this information, companies can focus on departments or any existing glass ceiling levels and use data-driven December.
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