Gender Equality as defined by WorldVision is “Girls are born with the same God-given rights as boys, and that needs to matter – everywhere. Societies with greater gender equality enjoy more sustainable development, faster economic growth and better prospects for their children. Yet in many places, discrimination and violence against girls and women is still rampant.”
Despite decades of progress, gender equality has not been achieved. Women continue to be marginalized on many levels, pay gaps, equal opportunities for promotion, and imbalances in society.
While progress has been made, the numbers from groups like UN Women research has over a billion women don’t have the same employment options as men. At the current rate, it will take about a century to close the global pay gap.
There are so many reasons why we need to get this right.
When women receive the same education and job opportunities as men, they can improve any organization they join.
There is no end of progressive research studies that show that diversity and inclusiveness of all types (gender, race, sexual identity, etc) increases an organization’s productivity and innovation. One University of California study looked at big companies in the state with some women in the top leadership positions. They found that they performed with more women in senior leadership positions than the companies with mostly men at the top.
The Sisterhood Of Two Broadway Golden Voices
EU’s Common Agricultural Policy Falls Short Of Sustainability Expectations
The McKinsey Gender and Diversity research completed in 2019 analysis found that companies in the top quartile for gender diversity on executive teams were 25 percent more likely to have above-average profitability than companies in the fourth quartile—up from 21 percent in 2017 and 15 percent in 2014.
The most important point however is that accelerating more diversity and inclusive and equity for women drives a bottom line improvement in economic performance. In OECD countries, if the female employment rates were raised to match Sweden, it would lead to a GDP increase equivalent to $6 trillion. Gender pay gaps end up costing the economy.
How AI can help close the Gender Equality Gap?
1.Using AI to detect and analyze unconscious bias in unstructured text.
AI change maker, The Text IQ Unconscious Bias Detector uses advanced capabilities for understanding, categorizing and analyzing unstructured information to detect patterns of bias in performance appraisal documents. The machine learning model identifies potential bias in categories such as race, gender, ethnic origin and age and provides detailed reporting on the types of language used by reviewers within different populations. Organizations can then mitigate this hidden bias through education, coaching and redesigned processes based on rigorous assessments and easy-to-understand examples.
2. Applicant Tracking Systems that Value Diversity and Equity
Ideal’s DEI Intelligence product has the ability to use AI and machine learning to enrich and infer demographic data from existing systems of record (payroll, applicant tracking and other human capital management systems) to reveal actionable insights throughout the talent lifecycle. HR professionals can use these insights to uncover potential inequities in talent decisions such as job hires, promotions, career paths, and more. Also essential is Ideal’s ability to report on DEI performance and progress at the organization-level, which will be beneficial for C-level executives.
3.Ryerson University – Diversity Institute Builds Robust Survey Tools and provides Gender Equity Gap Closure Solutions
The Diversity Institute, founded by Dr. Wendy Cukier, has a track record for innovating and developing solutions to support businesses to close the gender equity gap. Stories told by employees form a rich base for applying different machine learning methods to mine for sentiment, analyze text to form a cultural risk assessment are all powerful ways to help close the gender equity gap. You will want to follow Dr. Wendy Cukier’s research and practical gender equity solutions to help companies crack the UN sustainability goal #5 challenge.
4.AI Algorithms risk reinforcing cultural biases
Below is an excerpt from Stanford University Research paper discussing When good Algorithms Go Sexist (March, 2021)which is one of the best summaries I have found highlighting the risks of AI in perpetuating gender bias from large gender bias datasets.
For example, some 300 million fewer women than men access the Internet on a mobile phone, and women in low- and middle-income countries are 20 percent less likely than men to own a smartphone. These technologies generate data about their users, so the fact that women have less access to them inherently skews datasets. Even when data is generated, humans collecting data decide what to collect and how. No industry better illustrates this than health care (another industry with gender imbalance among leadership): Men and male bodies have long been the standard for medical testing. Women are missing from medical trials, with female bodies deemed too complex and variable. Females aren’t even included in animal studies on female-prevalent diseases. This gap is reflected in medical data.
Data that isn’t disaggregated by sex and gender (as well as other identities) presents another problem. It paints an inaccurate picture, concealing important differences between people of different gender identities, and hides potential overrepresentation or underrepresentation. For example, few urban datasets track and analyze data on gender, so infrastructure programs don’t often factor in women’s needs.
Even when representative data points do exist, they may have prejudice built-in and reflect inequities in society. Returning to the consumer credit industry, early processes used marital status and gender to determine creditworthiness. Eventually, these discriminatory practices were replaced by ones considered more neutral. But by then, women had less formal financial history and suffered from discrimination, impacting their ability to get credit. Data points tracking individuals’ credit limits capture these discriminatory trends.
Labeling of data can be subjective and embed harmful biases and perspectives too. For instance, most demographic data end up labeled on the basis of simplistic, binary female-male categories. When gender classification collapses gender in this way, it reduces the potential for AI to reflect gender fluidity and self-held gender identity.
Relatedly, the Gender Shades research project found that commercial facial-recognition systems used image data sets that lack diverse and representative samples. These systems misclassified women far more often than men. In particular, darker-skinned women were misclassified at an error rate of 35 percent, compared to an error rate of .8 percent for lighter-skinned men.
Board directors and CEOs need to accelerate their knowledge of AI and appreciate how AI can be used to solve the number five sustainability challenge of closing the gap on gender equity. AI solutions can help detect unconscious bias, but AI can also pose a major risk to gender equity advancement, unless all companies designing and developing AI models audit carefully for data bias in their data sets. This is an area of major ethical and equity concern as more skilled data assembly experts to validate and even audit the data sets will increasingly be needed.
As board directors and CEO’s look to modernize their organizations, and much progress has been made on gender equity in many industries, there still requires a vigilant and progressive leadership priority of all companies, irrespective of their size.
The United Nations developed in 2015 the Sustainable Development Goals as an universal call to action to end poverty, protect the planet and improve the lives and prospects of everyone, everywhere. The 17 Goals were adopted by all UN Member States in 2015, as part of the 2030 Agenda for Sustainable Development which set out a 15-year plan to achieve the Goals.
To see the full AI Brain Trust Framework introduced in the first blog, reference here.
To learn more about Artificial Intelligence, and the challenges, both positive and negative, refer to my new book, The AI Dilemma, to guide leaders foreword.