In September of 1995, the United Nations gathered in Beijing, China, for the Fourth World Conference on Women. There, 189 member states reaffirmed their commitment to the Convention on the Elimination of Discrimination Against Women (CEDAW) by developing and ratifying what is considered to be “the most progressive blueprint ever for advancing women’s rights”[i], the Beijing Declaration and Platform for Action. Women and the media were amongst the twelve critical strategic objectives identified. Recognizing the powerful impact that information communications technologies would have on our lives in the 21st century, conference delegates factored this into consideration. The Platform for Action affirmed a commitment to “Increase the participation and access of women to expression and decision-making in and through the media and new technologies of communication.”[ii] Twenty-seven years later, this humble, inspired initiative is under threat and Canada is leading its demise while hoping to establish an international model.
The future of communications in Canada is poised for an historic update known as Bill C-11, the Online Streaming Act, which will update the 30-year-old Broadcasting Act to include the digital realm. Framed as an initiative to promote Canadian content online, this legislation will allow the Government of Canada to control content discoverability on the internet in Canada, as it now does with traditional TV and radio. As currently written, Bill C-11 will grant the Canadian Radio and Telecommunications Commission (CRTC) unspecified regulatory powers over broadcasters.[iii] Unfortunately, the policies outlined in Bill C-11 make absolutely no acknowledgement of the largest, visible, marginalized group in the country – women. On October 25, 2022, the Senate of Canada passed the second reading of Bill C-11, the Online Streaming Act, moving one step closer towards female erasure and setting a dangerous precedence for both domestic and foreign legislation.
On February 7-8th, 2019, Ottawa hosted the International Meeting on Diversity of Content in the Digital Age. The meeting, co-hosted by the Department of Canadian Heritage and the Canadian Commission for UNESCO, gathered with 72 participants. Academic expert, Dr. Mira Burri, proposed actions for improving discoverability of national content through two main concepts: governance of algorithms, and governance through algorithms.[iv] According to their final report “Governance of algorithms, (…) would involve typical market regulations as well as various self- and co-regulation initiatives depending on the specific issues being targeted; and governance through algorithms, meaning targeted interventions with tools that would promote exposure to diversity of content by increasing the visibility and discoverability of certain types of content through editorial processes done by algorithms.”[v]
Canada’s Bill C-11 Discriminates Against Women in Three Different Ways: Lack of explicit inclusion, algorithmic biases, and industry imbalances
Consistent with Dr. Burri’s advice, Bill C-11 proposes the application of algorithms to elevate the discoverability of content, dictated by Heritage Canada, to curate our national identity. According to the Senate of Canada’s latest update “the Canadian broadcasting system ‘should … serve the needs and interests of all Canadians — including Canadians from racialized communities and Canadians of diverse ethnocultural backgrounds, socioeconomic statuses, abilities and disabilities, sexual orientations, gender identities and expressions, and ages.’”[vi] However, while claiming inclusivity for “all Canadians,” this bill has omitted both “women” and “sex(es)”, which will undoubtedly have dire future consequences on the rights, livelihoods, health, and well-being of generations of women and girls in Canada as it perpetuates female erasure.
Do Algorithms Discriminate Against the Female Sex? Yes, if their coders or their datasets do.
The proposed algorithmic regulations of the Online Streaming Act could be considerably more harmful to women than by merely neglecting our priorities. Algorithms are not neutral; they are inherently biased due to the nature of their human designers. Algorithms reflect the biases of their coders and the datasets that they are based on. As recent studies from the Alan Turning Institute have identified in their 2021 report “Where Are the Women? Mapping the Gender Job Gap in AI”[vii] algorithms are often designed using datasets collected predominantly from males, and frequently those algorithms contain both racial and sex-based discriminatory biases. Explicitly including “women” in Bill C-11 could help to offset those biases.
“Algorithmic identity politics reinstate old forms of social segregation—in a digital world, identity politics is pattern discrimination. It is by recognizing patterns in input data that artificial intelligence algorithms create bias and practice racial exclusions thereby inscribing power relations into media.”[viii]
In one disturbing example Ilinca Barsan, Director of Data Science at Wunderman Thompson Data, found that computer vision models, developed by Google, IBM, and Microsoft, were consistently better at identifying men in masks than women; disturbingly, they were more likely to identify masks as duct tape, gags or restraints when worn by women.[ix]
“Social media researcher and UCLA professor Safiya Noble has written most extensively on this topic. In her book Algorithms of Oppression, she points out that Google suggests racist and sexist search results are the user’s fault since they simply reflect our own cultural assumptions and previous search histories.”[x] The dark side of machine learning (ML), a type of artificial intelligence (AI), is that it reflects the way the world is, not how it should be.
The discoverability of online content by and for women is further impaired by the industry itself. The area of tech responsible for the proposed implementations of the Online Streaming Act is known as the ITC sector – Information and Communication Technology, which falls under the wider umbrella of STEM – Science, Technology, Engineering and Mathematics. According to the United Nations Department of Economic and Social Affairs report Progress on the Sustainable Development Goals – The Gender Snapshot 2022 “discriminatory norms and violence sideline women from fully entering the digital world”.
Furthermore: “Biased gender norms and stereotypes, embedded in curricula, textbooks, and teaching and learning practices, derail girls’ choices of what to study in school, and ultimately, their careers and employment opportunities as adults. Globally, young women outnumber young men in tertiary education. Yet women are a minority of students in STEM education, at only 35 per cent, and in information and communication technology studies, at just 3 per cent.”[xi]
While those are global numbers, according to Canadian non-profit organization Women in Communications and Technology / Les Femmes en Communications et Technologie:
“Companies in this sector face chronic shortages of skilled labour yet continue to accept a four to one male to female ratio as an acceptable status quo.”[xii]
In August of 2020, demarcating twenty-five years after the Beijing Declaration, the United Nations released a discussion paper entitled The Digital Revolution: Implications for Gender Equality and Women’s Rights 25 Years After Beijing. This report notes that “the underrepresentation of women in technical fields partakes in a feedback loop, amplifying gender bias in AI (Artificial Intelligence) and machine learning systems” [xiii].
Ratio of AI Researchers Around the World (Mantha and Hudson, 2018). Where are the women? Mapping the gender job gap in AI Policy Briefing – Full Report; The Alan Turing Institute
According to the Stanford Social Innovation Review:
“Many institutions make decisions based on artificial intelligence (AI) systems using machine learning (ML), whereby a series of algorithms takes and learns from massive amounts of data to find patterns and make predictions. These systems inform how much credit financial institutions offer different customers, who the health care system prioritizes for COVID-19 vaccines, and which candidates companies call in for job interviews. Yet gender bias in these systems is pervasive and has profound impacts on women’s short and long-term psychological, economic, and health security. It can also reinforce and amplify existing harmful gender stereotypes and prejudices”[xiv].
Industry expert Nicol Turner-Lee, who holds a fellowship at the Center for Technology Innovation at the Brookings Institution Think Tank in Washington D.C. “emphasizes that we need to think about who gets a seat at the table when these systems are proposed, since those people ultimately shape the discussion about ethical deployments of their technology”.[xv]
Despite frequent conflation of the terms “sex” and “gender” these key reports shine an important light on the inequities of the digital realm. What they don’t note, however, is that with an increasing number of males identifying as women, a gender-balanced team of ten programmers, with 5 men and 5 women, could in fact mean ten males and no females, which is why sex-disaggregated data is so crucial.
Is industry sex-disparity taken into account?
Given the omission of both “sex(es)” and women from the list of designated equity-seeking groups, and the inherent biases in algorithmic codes designed and driven by a male-dominated industry, the policies outlined in Bill C-11 constitute institutionalized sex-based discrimination against women, thus enabling the Government of Canada to further imbed inequality against the success and well-being of Canadian women. We, the females, are being erased from language and the law though policies enacted by a cabinet that dares to call themselves a “feminist government”.
Canadian women’s advocacy groups have called on the Senate of Canada to explicitly include “women” and “sex” in the list of designated groups that the new Broadcasting Act will include and prioritize.[xvi] [xvii] Unofficially, women are said to already be represented under “gender identities and expressions”, which are included in the bill. But we resent being defined according to gender rather than as a sex-based class. Gender is a subjective social construct, whereas sex is an objective material reality. It has never been clearer that we must communicate with our parliamentarians to address the conflation of sex and gender while affirming our collective status as members of the female sex, which is distinctively different from gender identity. Otherwise, this legislation could set a dangerous precedent for future legislation. And, as the government considers online harms in their upcoming legislation, they ought to closely examine the instrumental factors of bias addressed here.
In 2012 the National Post published an article by Gerald Caplan. He said “Despite the remarkable progress women have truly made in the past half-century, clawing for every inch of it, the struggle for women’s equality can never rest. It simply has too many enemies, always fighting to keep women in their place, where they belong, dead or alive. Young women who dismiss feminism as irrelevant or outdated are, I’m afraid, dead wrong. The struggle is never over.”[xviii] He was right; 12 years later not much has changed.
The despair and psychological damage grows deeper every day with each new piece of legislation that further accelerates female erasure into language and the law. The incalculable damage could have significant impacts on future generations of women and girls. And while the Government of Canada has been researching and developing this legislation since at least 2017, its impact on the female sex does not appear to have ever been a consideration despite the plethora of credible studies available. Furthermore, they have failed in their commitments to the United Nations to implement policies on our behalf. Perhaps they believe that including the word “gender” in legislation is somehow a reasonable substitute for performing a Gender Based Analysis + with sex-disaggregated data – it is not.
On November 23rd the Senate Committee on Transport and Communications will be meeting to examine Bill C-11 on a clause-by-clause basis. Our inclusion in the Online Streaming Act is paramount to achieving an equitable future in Canada. Only one more vote remains, by all senators, until Bill C-11 reaches Royal Assent thereby setting a precedent – of female erasure – for both Canadian and foreign legislation alike.
– Simone XX (November 2022)
References
[i] https://www.unwomen.org/en/digital-library/publications/2015/01/beijing-declaration Accessed September 2022
[ii] https://www.un.org/womenwatch/daw/beijing/platform/media.htm
[iii] https://sencanada.ca/en/sencaplus/news/the-online-streaming-act-in-the-senate/ Last updated November 10th, 2022, Accessed November 10th, 2022
[iv] https://cdec-cdce.org/wp-content/uploads/2020/12/The-challenge-of-discoverability_CDCE.pdf Page 21; Accessed November 12, 2022
[v] https://www.canada.ca/en/canadian-heritage/services/diversity-content-digital-age/international-engagement-strategy/report.html#a7 Accessed November 12, 2022
[vi] https://sencanada.ca/en/sencaplus/news/the-online-streaming-act-in-the-senate/ Last updated November 10th, 2022; Accessed November 10th, 2022
[vii] Where are the women? Mapping the gender job gap in AI Policy Briefing – Full Report Public Policy Programme Women in Data Science and AI project; Erin Young, Judy Wajcman, Laila Sprejer; The Alan Turing Institute. https://www.turing.ac.uk/sites/default/files/2021-03/where-are-the-women_public-policy_full-report.pdf
[viii] Pattern Discrimination, 2019; Authors: Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer, and Hito Steyerl https://www.upress.umn.edu/book-division/books/pattern-discrimination Accessed November 3rd, 2022
[ix] Research Reveals Inherent AI Gender Bia; Quantifying the accuracy of vision/facial recognition on identifying PPE masks; Author Ilinca Barsan, Director of Data Science, Wunderman Thompson Data https://www.wundermanthompson.com/insight/ai-and-gender-bias Accessed November 13, 2022
[x] https://theconversation.com/googles-algorithms-discriminate-against-women-and-people-of-colour-112516 Accessed November 4th, 2022
[xi] Progress on the Sustainable Development Goals: The gender snapshot 2022 https://www.unwomen.org/en/digital-library/publications/2022/09/progress-on-the-sustainable-development-goals-the-gender-snapshot-2022 Page 5. Accessed September 2022
[xii] Women in Communications and Technology https://wct-fct.com/en/advocacy/up-the-numbers
Accessed October 8th, 2022.
[xiii] THE DIGITAL REVOLUTION: Implications for Gender Equality and Women’s Rights 25 Years after Beijing; No. 36, August 2020; J Judy Wajcman, Erin Young, Anna FitzMaurice; pg. 15 https://www.unwomen.org/sites/default/files/Headquarters/Attachments/Sections/Library/Publications/2020/The-digital-revolution-Implications-for-gender-equality-and-womens-rights-25-years-after-Beijing-en.pdf
[xiv] https://ssir.org/articles/entry/when_good_algorithms_go_sexist_why_and_how_to_advance_ai_gender_equity By Genevieve Smith & Ishita Rustagi Mar. 31, 2021; Accessed October 8, 2022.
[xv] Why algorithms can be racist and sexist; A computer can make a decision faster. That doesn’t make it fair.
By Rebecca Heilweil Feb 18, 2020 https://www.vox.com/recode/2020/2/18/21121286/algorithms-bias-discrimination-facial-recognition-transparency Accessed November 3rd, 2022
[xvi] https://sencanada.ca/Content/Sen/Committee/441/TRCM/briefs/TRCM_SM-C-11_WDICanada_e.pdf
[xvii] https://sencanada.ca/Content/Sen/Committee/441/TRCM/briefs/TRCM_Brief_PDFQu%C3%A9bec_e.pdf
[xviii] https://www.theglobeandmail.com/news/politics/second-reading/honour-killings-in-canada-even-worse-than-we-believe/article1314263/ Accessed Aug 5, 2022
Brava! When my former husband was “diagnosed in the first appointment” as a transsexual, that meant one thing–recognizing in some way that humans are born into one of two sexes. Now morphed into “trans gender,” with 43, or is it 56, or however many QIAA possibilities, natal women are relegated to what, 1/56th of the identities in the human species? Despite the fact that a woman gestated and gave birth to every human on this Earth.
Ugh, more Can Con…if we wanted to see it, we know where it is.
Incredible, the hatred of women is so deep that those writing these policies can, with impunity, erase the existence of half the human race while the majority of those in power ignoring what’s happening or opportunistically going along with the destruction of girl’s and women’s future. The ignorance and hatred of women’s well-being by transcismen and women is breathtaking, their oblivious narcissism is no excuse for their sadistic selfishness.