From the way that airbags are designed in cars, to the lack of women included in medical research, we see the ways in which gender bias in these fields has negatively impacted women.
Gender bias is especially problematic in fields that are overwhelmingly made up of men, including computer programming. In our increasingly digitalized and technological world, we see gender bias playing out in another field our society that we are heavily reliant upon, the world of IT. AI and computer programs which run on algorithms surround us, and as it turns out, many of them contain the gender biases and other biases we as humans have.
AI’s Gender Bias
AI and computer programs are posed as being advantageous to humans because they don’t contain the flaws that we as humans have. This includes things such as bias and being irrationally influenced by emotions. However, what happens when the people creating the programs unintentionally include their own biases they may contain? The consequences of this have been documented in many different examples.
Amazon’s Facial Recognition Program
One of these examples which received considerable attention was Amazon’s facial recognition program. In research that was carried out by MIT and Standford University, they found that Amazon’s facial recognition program misidentified white men less than 1% of the time. What was the rate of misidentification among black women with the same program? Nearly 35%.
This was highly problematic, especially considering the fact that police departments around the US were making use of Amazon’s facial recognition programs. It should be noted that Amazon has stopped sharing its program with police forces for now in the wake of protests and pushbacks after the killing of George Floyd.
We Rely on Machine Learning
AI and machine learning programs are crucial in our day to day lives. Without going into depth, machine learning is the process of a computer algorithm analyzing massive amounts of data, and learning what types of recommendations and decisions to make based off this data. Facebook, Twitter, Google, and about every other major app we use in our day to day lives rely on machine learning.
Yet a recent investigation by the data annotation firm Scale AI found a large gap in a key machine learning program. Their investigation looked into an open-source dataset known as CoNLL-2003, which has been crucial in the development of machine learning and AI programs. What was the problem that they found? Many women’s names were missing from the dataset.
Women’s Names Missing
Male names were found to be mentioned five times more often than female ones. It was also 5% more likely to miss a new woman’s name that was run through the program than a man’s name.
One of the things that makes this problematic is the fact that CoNNL-2003 has been crucial in the development of other language programs which use AI. In fact, the program has been cited 2,500 times in research literature, which makes the implications of these findings hard to pin down exactly. What is clear though, is that male bias is being perpetuated in computer programs which rely on CoNNL-2003.
Unintentional Bias
Those who helped develop Amazon’s facial recognition program, and the CoNNL-2003 dataset did not intentionally create gender and racially biased programs. Yet it happened anyways, and these are the types of things that happen when you have a group of similar people and backgrounds working on a project. Unfortunately in this case, it was primarily white men behind these programs, leaving many other groups to face the consequences for their unintentional bias.
We Need Diversity
This underscores and highlights a critical point. The need for people of diverse backgrounds and experiences working together in a variety of fields, from computer programming, to venture capital.
Not only does this work help address these types of biases occurring and prevent them from happening, but it has also shown to improve outcomes. From having a more successful and profitable company, to creating more secure and long-lasting peace agreements, having a diverse group of people as part of the decision process leads to more successful outcomes.
We Are Seeing Progress
Luckily, we see business leaders, activists, and community groups coming together to try and get people of diverse backgrounds involved in different fields. This is also true in the field of computer programming. Girls Who Code is a global organization that tries to close the gender gap for computer programmers and create the next generation of woman engineers and computer programmers. This is a growing movement, and you can find even more organizations here who are doing similar and important work.
Although these problems still exist, we see an increased awareness and commitment to address these different gender biases and gaps. At SHE Community we work to support companies and businesses to create diverse and balanced organizations. Not only does this help create thriving businesses, but a thriving world. •
