Entering or moving through the workforce, job searching can be both highly rewarding and unexpectedly frustrating. Few things lift the spirit like discovering the perfect new role, and few things deflate it more than never getting a response.
Some employers communicate quickly and openly, providing clear updates on progress. Others leave applicants waiting, offering no guidance and leaving many guessing about their status.
Ontario to require employers to respond to job applicants within 45 days
Well, here’s a shake-up for this job-hunting dilemma: Ontario is rolling out new rules that basically say, “no more cold shoulders.” Starting Jan. 1, 2026, companies with 25 or more staff must get back to applicants within 45 days of an interview. Bonus: They also have to inform you if the job is actually being filled and whether AI is involved in the screening process.
Job postings must now clearly state the salary or wage range (no wider than $50,000 unless the top end exceeds $200,000), include all forms of pay like bonuses and commissions, disclose whether AI is being used to screen or select applicants and confirm whether the role is a current vacancy.
Breaking the new hiring rules in Canada could cost your company a hefty $100,000, though first-time offenders are likely to only get a slap on the wrist or lower fines, according to multiple reports.
Years before AI started screening resumes, ghosting was standard practice. Applicants often go through several rounds of interviews only to hear nothing. Meanwhile, only a few lucky candidates get callbacks. AI is transforming how companies manage hiring, allowing them to evaluate and eliminate candidates almost instantly. Tasks that previously took days or weeks now occur in moments. With most major firms using AI at multiple stages, ensuring transparency and communication with applicants has become an even greater challenge.
AI increasingly used to scan resumes and shortlist candidates
Automated systems can scan hundreds, or even thousands, of resumes in minutes, analyzing key words, formatting patterns and subtle indicators of candidate fit based on historical hiring data. Machine learning algorithms learn from past hires to predict which applicants are most likely to succeed, flagging top prospects while filtering out the rest. Tools such as Hirevue, Peoplebox.ai and Recruitee are used to streamline this process, combining AI-powered screening with candidate scoring to make recruitment faster and more data-driven.
Even the most prepared applicants, who perform well in interviews, can sometimes disappear from the process without any explanation. Under the province’s updated legislation, companies must now disclose any AI involvement in recruitment, helping applicants understand what’s really happening behind the scenes.
When algorithms go wrong: The limits of automated recruitment
Machine learning can parse resumes, career paths and educational backgrounds in ways humans might miss, highlighting transferable skills in candidates who have taken unconventional routes. Advocates argue these tools can make recruitment more equitable by identifying hidden potential. Still, AI remains an imperfect judge; it can flag talent, but it cannot anticipate how a person will collaborate, adapt or innovate within a real-world environment faced with challenges and tasks.
Algorithms trained on historical hiring data also run the risk of perpetuating existing biases, unintentionally favoring certain demographics or backgrounds. For example, in 2018, Amazon scrapped an AI-powered recruiting tool that was found to be sexist against female candidates.
The system, first developed in 2014, was designed to automate hiring by evaluating resumes and assigning candidates ratings from one to five stars—similar to product reviews on Amazon’s platform. It was trained on resumes submitted to the company over a 10-year period, the vast majority of which came from male applicants.
As a result, the AI developed a preference for male candidates in technical roles, such as software developers, and penalized resumes that included terms like “women’s” or mentioned women-focused organizations. Even after revisions and updates to the system, programmers were unable to guarantee the elimination of this persistent bias.
Even when AI can detect transferable skills or patterns of success, it often struggles to assess softer qualities such as creativity, intuition or cultural fit. Overreliance on automated scoring could result in a technically “efficient” process that nonetheless misses the most promising or unique candidates.
Ontario’s new reforms: Your rights and what actions you can take
Ontario has passed several employment reforms this year, and, as of July, the legislature added more measures designed to give employees more clarity and control. Workers are now legally entitled to receive key employment information in writing before their first day, like legal names, starting wages, pay dates and hours of work. The goal is to address a system that allows bad actor employers to exploit jobseekers trying to enter the workforce.
The legislation is part of Ontario’s broader Working for Workers Acts aimed at modernizing labor laws and improving workplace standards. The new rules force companies to be transparent and accountable in recruitment, giving job applicants clear timelines and a say in their experience. Employers can no longer leave candidates in the dark, and the changes are designed to make the hiring process fairer and more predictable.
By mandating timely responses and transparency, the law gives light to more than compliance, it’s about treating candidates with respect. Job seekers gain clear answers, freeing them from uncertainty. While small in legislative terms, the impact on the millions navigating the job market is massive. With similar discussions underway in the U.S., Ontario’s approach could set a global precedent and force employers not just in Canada, but globally, to reconsider what equitable hiring looks like.
The Truth in Job Ads working group recently unveiled the Truth in Job Advertising and Accountability Act, a proposed federal law for U.S. employers. It would mandate that public job listings disclose hiring timelines, clarify if the role is newly created or a backfill and reveal posting frequency over two years.
Spearheading the effort is Eric Thompson, who, after losing his job in October 2024, has become a vocal advocate for transparency and fairness in hiring. CNBC reports that Thompson spends between 20 and 30 hours a week on advocacy efforts, including legislative outreach, and has had close to 30 meetings with congressional staffers from across the aisle to enact transformative change.
How to beat the bots: A guide to AI-driven recruitment
Algorithms now control the first cut of most hiring processes, silently deciding which candidates move forward and which vanish without a trace. Thousands of resumes can be scanned in minutes, yet the story behind each application, the skills, achievements and potential, can often be lost.
The rise of AI is inevitable, but job seekers can take steps to regain control: tailor resumes to include both important keywords and narrative context, highlight transferable skills, and research whether a role still involves human interviews. Ontario’s new transparency rules provide a blueprint: they require employers to disclose when AI is used and how decisions are made, giving candidates concrete insights into what the machines are looking for and what’s in it for them.
AI can scan resumes, detect patterns and highlight potential top candidates, but it can’t judge creativity, intuition or resilience. To get noticed, target roles where humans still make the call, follow up directly with recruiters and prepare for interviews as if the algorithm is only the first hurdle. Demonstrate skills no machine can evaluate and you’ll have an edge over purely automated selection.
Photo courtesy of fizkes/Shutterstock
