What many consider the first published computer program was written by Ada Lovelace—a century before the advent of the modern computer. In 1842, she translated a paper on Charles Babbage’s Analytical Engine, adding notes that further elaborated on the machine’s capacity to be programmed for general-purpose (and not only number-based) computation, including an algorithm for the machine to compute Bernoulli numbers, which is often described as the first computer program. Where Babbage imagined the machine, Lovelace understood how to realize the machine’s functionality—and put it in writing. She is now recognized as a key figure in early computing, with visionary insights that came decades before their time. Lovelace credits her “poetical science” approach for her innovative thinking—blending creativity with the beauty of mathematics to conceptualize previously unimagined possibilities.  

In the early 1940s, the first general-purpose digital computer, the Electronic Numerical Integrator and Computer (ENIAC), was created to help the war effort. Complex computing then relied on workers—usually women—called human computers. Programming wasn’t yet a recognized field; it was considered menial work. Six human computers—Kathleen Antonelli, Betty Jean Bartik, Betty Holberton, Marlyn Wescoff Meltzer, Frances Spence, and Ruth Teitelbaum—were selected to ‘operate’ the ENIAC. In so doing, they created many fundamental programming techniques, and are considered some of the first programmers. Despite this, the ENIAC Six were left out of the media coverage of the 1946 demonstration that unveiled the computer to the public. They were erased from computer history until nearly 50 years later, when Kathy Kleiman researched and documented their stories with the ENIAC Programmers Project.  

In the early 1950s, computer programs were made up of binary code that had to be rewritten for each new task and machine. Grace Hopper, a mathematician and naval officer who started her computing career in 1944 working on the Harvard MARK I, wanted to make computers more accessible for non-experts. She pushed for word-based and machine-independent programming languages during the time she was hired to work on the UNIVAC I in 1949. Though her ideas were dismissed, Hopper persisted and invented the compiler: a linker that translates a programmer’s instructions into machine code. The compiler came to be crucial for programming. Hopper also led the teams that created many important early programming languages, like FLOW-MATIC and COBOL. Teamwork was vital; Hopper worked alongside programmers like Betty Jean Jennings and Jean Sammet, and, together, they laid the groundwork for the digital technologies we use today. 

With the prevalence of the ‘human computers’ of the previous decades, the preconceptions of the STEM field didn’t always look like they do today. The field of computer science gained its masculinist, individualist stereotype around the 1980s. When personal computers were launched to the market, many families defaulted to placing them in their sons’ rooms or allowing their sons more access to the machines. This meant that when it came time to apply for university, young men were entering computer science programs with a higher level of hobby expertise than their female counterparts, even if math and science test scores were equal. With their previous experience, boys were favoured by their professors, and the gap between boys and girls in computer science academia began to grow. According to the New York Times, “From 1984 onward, the percentage [of women] dropped; by the time 2010 rolled around, it had been cut in half. Only 17.6 percent of the students graduating from computer-science and information-science programs were women.” 

In 1987, American computer scientist Anita Borg, and twelve other women in her field, established Systers— an email list of women working in the ‘systems’ field. This community provided a space for women technologists to collaborate, share their experiences in the workplace, and share resources with each other. With this background on women’s STEM empowerment, Borg and her colleague Dr. Telle Whitney later established the Grace Hopper Celebration (GHC), the largest conference for women working in technology. Featured speakers include the likes of Joy Buolamwini, Timnit Gebru, Aisha Bowe, Melinda Gates, Serena Williams, and more. In 1997, Borg founded the Institute for Women and Technology (now: Anita B.Org) which continues to bolster women in technology fields, in the years since Bord passed away in 2003.

In a devastating act of violence, gunman Marc Lepine murdered 14 women and injured 10 others at the Université de Montreal’s École Polytechnique. This is a tragic example of the misogynistic responses to the advancement of women in engineering and technology. While sexism and misogyny can exert themselves more covertly through exclusion and bias against women, this event is an explicit instance of violence against women which sadly articulates the same biases in an overt and undeniably brutal way. As researchers working and living in the city of Montreal, December 6, now Canada’s National Day of Remembrance and Action on Violence Against Women, is an annual reminder that the achievements of women are won in spite of an often violent status quo which threatens to exclude them.  

We remember Geneviève Bergeron; Hélène Colgan; Nathalie Croteau; Barbara Daigneault; Anne-Marie Edward; Maud Haviernick; Barbara Klucznik-Widajewicz; Maryse Laganière; Maryse Leclair; Anne-Marie Lemay; Sonia Pelletier; Michèle Richard; Annie St-Arneault; Annie Turcotte. 

Judith “Saint Jude” Milhon is an important figure for understanding the countercultural application and advancement of computing. While much of this timeline focuses on events and figures in the dominant mainstream narrative of technology, Saint Jude and her cyberpunk collaborators demonstrate that the use of technology can be playful and embodied. The joys and pleasures of hacking, the thrill of cybersex, and the power of a woman at a keyboard are proof of this. As a lifelong proponent of social justice and collectivism, Saint Jude’s contributions to computing, including the Community Memory project (the first fully computerized bulletin board system), demonstrate that bottom up approaches to technology can be as powerful, if not moreso, than institutional approaches.  

While working as a professor of computer science, Fei Fei Li realized that, while many of her colleagues were working on improving algorithms, what actually needed was the data to make these algorithms work. So, she conceptualized ImageNet, a digital database for the training of visual object recognition software. First utilizing help from research assistants and then from Amazon Mechanical Turks, Li’s team hand-annotated 14 million images. Through this broad image-recognition training set, the ImageNet Challenge was created, which tested how well new algorithms fared at coding images in the ImageNet set. Li’s ImageNet has become the benchmark for the wide field of computer vision to this day. 

What started as an art project became pivotal research as Joy Buolamwini discovered that the facial recognition systems used by many of the top companies in the world were biased against dark-skinned women’s faces. According to her work, these facial recognition systems misidentified gender in 35 percent of darker-skinned women, compared to only up to 1 percent of lighter-skinned men. In 2018, Buolamwini co-authored “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification” with Timnit Gebru, another prominent AI researcher and activist who was ousted from Google for her criticism of big tech practices. With the establishment of Gebru’s Black in AI, Buolamwini’s Algorithmic Justice League, and other initiatives such as A+ Alliance, Open Data Chapter, and Women in AI, lots of work is being done to make AI equitable for all.  

From the mid-2010s, generative AI systems that are free for public use have been skyrocketing in popularity. Some of the most well-known are OpenAI’s DALL-E and ChatGPT—image and language models, respectively. These models, and the many like them, have allowed the average user to become more familiar with AI and generate their own art and writing for a myriad of uses. However, these powerful systems have the potential to create a lot of damage, as feminists have critiqued how AI image generation perpetuates gender stereotypes as well as their use in creating harmful ‘deepfakes.’ With these concerns, artists like Mimi Onuoha with “Missing Data” and Caroline Sinders with “Feminist Data Set” have been asking: who is included in the datasets of these models, and who is left out?