✍️By Emma Edney from Encode Canada.
Emma is a BCL/JD McCall MacBain Scholar candidate at McGill University. Her interests include ethical issues surrounding personal information in technology and the impact of AI on litigation work. Emma is a writer at Encode Canada and a junior editor for the McGill Health and Law Journal.
📌 Editor’s Note: This piece is part of our Recess series, featuring university students from Encode’s Canadian chapter at McGill University. The series aims to share insights from university students on current issues in AI ethics. In this article, Emma Edney examines how graduates are navigating entering the working world in an AI age, commenting on the double-edged nature of using AI for applications and their discriminatory impacts on marginalised groups.
Photo credit: Marcus Winkler on Unsplash.
The job market crisis continues to be a growing concern for university graduates seeking employment (Statistics Canada, 2025). After investing years of study and income into their education, many are met with waves of rejection emails. In response, two-thirds of applicants are now turning to Artificial Intelligence (AI) to gain a competitive edge (Sweeney, 2025). However, if everyone relies on AI, standing out becomes even more difficult. As AI use grows, it creates a new hurdle for job seekers attempting to differentiate themselves, suggesting that companies should be the ones to alter their recruiting practices.
Standard automation has long been used to pre-screen applications, but AI has now replaced it as the primary hiring tool (Ouden, 2025). Recruitment automation refers to “the use of technology to streamline the talent acquisition process.” (McGrath and Downie, 2025) AI scans for matching keywords, prompting applicants to optimize their resumes to secure interviews (Wiles and Horton, 2025). Using AI can then become a double-edged sword. AI can be useful to land a greater chance of getting an interview, but it can also hinder if others are using the same tactic, making it harder to differentiate oneself. In fact, 76.6% of hiring teams regularly encounter AI-assisted applications (Tilo, 2025). So, is AI helpful for the job market? Based on this study from hiring teams, it depends. There is no “secret sauce” to getting hired, as factors like nepotism, education, or chance may also play a role. (Giannakas, Fulton, and Awada, 2017). Notably, 62% of non-customized AI-generated resumes are more likely to be rejected (Spencer, 2025). As I mentioned, recruiters use AI to help filter job applications through pre-screening. AI is a significant benefit for the hiring team because it can help reduce cost and time (Allal-Chérif, Aránega, Sánchez, 2021). In turn, it allows for recruiters to screen candidates from spending 45 minutes sifting through applications to only four minutes (Valdivieso 2024).
However, AI systems used in hiring have been shown to carry biases related to race and gender (Yarrow, 2025). There are significant barriers to using AI in the job market, particularly racial bias that can lead to unfair treatment of marginalized groups. For instance, AI systems learn from historical hiring data; they may replicate past inequitable patterns that favoured predominantly white candidates, reflecting the past when racial equality was taboo more than it is today. (Dadaboyev et al, 2025). In addition, gender bias against women came to light following the Amazon incident (Dastin, 2018). Amazon faced backlash when its AI recruiting tool, trained on 10 years of data showing more men hired than women, began favoring male applicants (Dastin, 2018). As a result, AI risks turning hiring into a checklist rather than looking at each candidate as a whole (Boyd, et al., 2025).
In Canada, this primarily impacts women and people of colour continue to face barriers in fields like science, technology, engineering, and mathematics (STEM) (Statistics Canada, 2026). As of 2022, more women are entering STEM programs, however, only 15% of engineering professionals are women (Ying Mo et al., 2025). Meaning, this reflects the job market still picking and choosing men over women, which AI will only make worse in the next few years. While initiatives like the Black Women Business Network project aimed to promote diversity in 2022, it may be disregarded as it did not consider the use of AI at the time (Statistics Canada, 2025). It remains unclear whether the shift of AI-driven recruitment screening will improve if companies train the bots themselves or if it will further entrench systemic bias.
Beyond the gender and racial bias implications, AI in hiring also raises serious ethical concerns about the use of personal information. Recruiters are expected to obtain consent and comply with Canadian privacy laws, which is why applicants may be given the option to opt in or out of AI-based screening (Hunkenschoroer and Luetge, 2022). However, that choice can feel misleading. Many applicants may feel pressured to click “yes” simply to remain competitive, even if they are uncomfortable with how their data will be used. After all, how would they know whether opting out reduces their chances? Imagine you are in debt and urgently needing a job, you might agree to conditions you would otherwise question just to secure the possibility of a paycheck.
Yet, the Canadian government has introduced stricter regulations to protect individuals’ personal information in the context of AI (Statistics Canada, 2026). Regulations exist on paper, but companies operating behind closed doors may not always follow best practices unless they are investigated or penalized. This creates uncertainty and distrust within the hiring process. Instead of relying so heavily on AI-filtered resumes and cover letters, companies could consider alternative recruitment methods that better assess candidates as individuals. For example, employers might replace cover letters with specific online assessments or quizzes that evaluate relevant skills for the job (Sautter and Zuniga 2018). They could also implement mandatory virtual conversations with current employees, giving applicants a fair opportunity to showcase their personality (Torres and Mejia, 2017). Some American companies, including Olive Garden, FedEx, and Sweet Loren’s, have already begun moving in this direction, signalling that human-centred hiring approaches are still viable (Burleigh, 2025).
To conclude, some American companies are leading by example, pushing back against the heavy reliance on AI in hiring and adopting more creative approaches to create a more level playing field for job seekers. While AI can reduce costs for employers, expedite response times, and help applicants tailor their cover letters, AI recruiting practices will exacerbate the disadvantage faced by women and people of colour. Although individuals can be protected by statutory legislation, will employers take the risk and continue to use AI regardless of the impacts, as profits matter more?