A recent article in the New York Times highlighted the absence of women in the history of the modern artificial intelligence (AI) movement. This omission is not a new phenomenon and reflects a long-standing trend of women being overlooked in STEM fields. However, as women continue to make significant contributions to the AI industry, it is crucial that they are included in conversations about its development.
The history of computing itself reveals the important role women played in its early days. Before computers as we know them existed, the term “computer” referred to people who performed complex mathematical calculations, and many of these individuals were women. Ada Lovelace, an English mathematician, is often credited as the first computer programmer for her work on the analytical engine in the mid-1800s.
In the late 19th century, a group of about 80 women worked as computers at the Harvard Observatory, cataloguing and analyzing astronomical data. Their employment was cost-effective, as they were paid less than men or even volunteered. During both World Wars, women were hired to calculate artillery trajectories and were encouraged to take on computing roles in the absence of men.
Women also played a pivotal role in advancing NASA’s space projects in the 1960s. Katherine Johnson, a former NASA mathematician, was responsible for quality-checking the outputs of early IBM computers for an orbital mission in 1962. Despite their significant contributions, women in computing received little recognition or financial compensation compared to their male counterparts.
Today, computing and programming form the foundation of AI. The women who pioneered computing laid the groundwork for this field, and their work has now been replaced by machines capable of analyzing vast amounts of data. Women continue to make pioneering contributions to AI, such as Cassie Kozyrkov, Joy Buolamwini, and Mira Murati.
However, the AI industry remains heavily male-dominated. A study found that women made up just 12% of researchers published in leading AI conferences. The lack of gender diversity in AI has broader implications, as it can perpetuate biases and stereotypes. For example, research has shown that AI algorithms can reinforce sexist targeting in job ads and exhibit higher error rates in recognizing women, particularly those with darker skin tones.
To address this gender gap, it is essential to include women in AI training datasets and data-collection processes. The recent New York Times article exemplifies how media and industry contribute to a status quo that favors men. Recognizing and valuing women’s contributions to AI is crucial for breaking the glass ceiling and achieving gender equity in STEM fields.