Vivi Koroma Kala has stopped counting how many jobs she has applied for. The marketing professional, who has 16 years of experience under her belt, was made redundant in July and did what anyone in her position would do: she updated her CV, polished her cover letter, and started applying for roles with the hope that her skills and experience would speak for themselves. Two months later, after countless revisions, endless drafts, and a steady stream of rejection emails, she is still unemployed. She has three children, and the redundancy money is running out. “It’s so disheartening,” she says. But it isn’t just the rejection that stings. It’s the way the rejection arrives. The silence, the speed, the vague generic wording. Vivi has begun to suspect that, in many cases, there is no human on the other side of the screen at all—just an algorithm sorting through applications, scoring candidates, and deciding who gets a chance and who doesn’t. And for Vivi, that feels less like a technological convenience and more like being erased. Every morning she wakes up to check her inbox, hoping for a response, only to find another automated “Unfortunately…” waiting for her. It’s a deeply lonely experience, especially when you have a family depending on you and your savings shrinking by the day. She isn’t asking for pity. She just wants to be seen. She wants her years of experience to matter. She wants to know that someone, somewhere, actually read her application and made a considered decision. Instead, she finds herself wondering whether she is being judged by a machine that knows nothing about her, her career, or her capacity to grow.
The experiences that led Vivi to this conclusion are chilling in their efficiency. One Friday afternoon, just before the application deadline, she applied for a role at a large bank. By Saturday morning, she had already received a rejection email. Not a day passed. Not even a proper working day. It was, she says, obvious that no human had ever laid eyes on her application. Another time, at a different large organisation, Vivi made it through a five-part hiring process spread across ten days. She passed seamlessly from one stage to the next, and her interactions with prospective employers were warm and encouraging. It seemed like a real connection had been made. Then, at the final hurdle, she was rejected—because, she claims, of just one answer she gave during the initial screening test. “So, one data point outweighed everything that people in the room had seen,” she says. That single moment made her realise how fragile and opaque the entire process had become. She began to notice patterns in the automated rejections: the generic phrasing (“However, on this occasion…”), the timestamps arriving suspiciously quickly, the complete absence of specific feedback. “It’s so obvious that nobody, no human, had assessed my skills,” she says. “You start to see the patterns from these obviously AI-generated emails. You can’t improve as a candidate when nobody actually tells you what happened. You just apply again and again, and you hit this wall. It’s really dehumanising, and it does knock your confidence. Especially when you know a machine is on the other side of that decision.” For Vivi, the most damaging part is not the rejection itself—it’s the invisibility. There is no explanation, no feedback, no chance to learn or grow. There is only a door slamming shut, over and over again, with no voice behind it.
It is no secret by now that companies are increasingly turning to artificial intelligence to help them manage the enormous volume of applications they receive. A 2023 study by the Institute of Student Employers found that around a third of UK employers use AI in some way when hiring new staff, and that number is only going up. For many organisations, AI is seen as a way to save time, cut costs, and streamline a process that can otherwise overwhelm human resources teams. But for candidates like Vivi, the rise of AI in recruitment has created a new kind of maze—one where you can never be quite sure who or what is evaluating you, and where the rules of the game are invisible. Vivi is not anti-technology. She uses AI herself as a tool, and she understands that employers are under immense strain, especially when they receive hundreds or thousands of applications for a single role. But she believes that efficiency cannot come at the cost of humanity. “It doesn’t mean that employers can hide behind a machine,” she says. “It’s their responsibility at the end of the day to be really transparent on how they use these tools, and I haven’t seen that so far.” That lack of transparency is deeply unsettling. It leaves candidates in the dark, unable to know whether they were judged fairly, whether their skills were truly evaluated, or whether they were dismissed by an algorithm that had already made up its mind before they even pressed submit. And for Vivi, there is another concern that makes the situation even more urgent: the possibility that AI, despite all its claims of objectivity, is quietly perpetuating discrimination. “Maybe not on purpose, but these algorithms can reflect discrimination, and that’s something I really, really think employers should be wary about,” she says.
Her suspicions are not unfounded. Recent research has begun to shine a light on exactly how AI is reshaping the job market, and the results are troubling. A study of 2,000 UK jobseekers conducted by the non-profit People Like Us and Censuswide found that some applicants are rejected within minutes of submitting an application—sometimes before the closing date has even passed. The same study found that applicants from non-white backgrounds appear to be rejected at a disproportionate rate compared with their white counterparts. They apply for a third more roles, and yet they are rejected faster: 41% receive a rejection within an hour of applying, compared with 32% of white applicants. Even more concerning, jobseekers from non-white backgrounds are significantly more likely—70% versus 57%—to remove elements of their identity from their CVs in a bid to get past the bots. This is not just about changing a surname or removing a photo; it is about erasing parts of who you are just to get a foot in the door. AI is trained on enormous amounts of publicly available data and text, and if that data contains historic biases, the algorithms can inadvertently learn to reinforce those same biases. Even if hiring managers have no intention of discriminating, the machines they rely on might be doing it for them. This phenomenon is nothing new, and it predates the AI boom. Candidates from non-white backgrounds have long reported having to edit their applications, sometimes anglicising their names to improve their chances. Sevjan Melissa Pem, who previously wrote for Metro, described how she was consistently rejected for jobs for five months across a range of industries. But when she started using her more “socially acceptable, white-sounding middle name” on her CV and application forms, the interviews began to roll in. “I scheduled four interviews in one day; one company phoned to set it up the same day I applied. It was almost too easy,” she said. “There was only one explanation for what happened with my job hunt: outdated, accepted, inherent racism.” Comedian Jenan Younis had a similar experience when she tested whether her name was affecting her bookings. Over the course of a year, she emailed comedy promoters alternating between “Jenan Younis” and a more anglicised pseudonym, “Janine Young,” using the exact same CV and clips. The results were stark: “Janine” received replies from 81% of promoters, while “Jenan” received just over 12%. The same person, the same talent, the same material—only the name changed.
Vivi, however, cannot afford to wait any longer for the perfect job. She has launched her own consultancy project while freelancing to supplement her income, determined to keep moving forward even as the rejections pile up. But she also wants other applicants to know that they are not powerless. One of the most important lessons she has learned, she says, is that candidates can ask companies to give them human feedback if they receive what they believe is an AI-generated rejection. “I didn’t know that I could ask for a human to have a look at my application,” she says. “And I don’t think I’m unusual in that. I’ve spoken to many people, and a lot of people are facing the same thing.” To help address this growing crisis, People Like Us has launched a new national campaign called Reject the Rejections, aimed at exposing what it calls “automated discrimination” in UK hiring. The campaign, which launched on Wednesday, September 16, is designed to make hiring more equitable and to give rejected candidates a way to challenge the opaque systems that are increasingly deciding their futures. It has also created a free email template, developed with Planes Studio, that rejected candidates can use to ask whether automation was used on their application and to request a human review. It is a small but powerful step—a way of saying that candidates deserve more than a silent algorithmic no, that they deserve to be heard, and that the burden of fairness should not rest entirely on the shoulders of the people applying.
In many ways, Vivi’s story is a reflection of a much larger shift in how we work, how we apply for jobs, and how we are judged. The job market has always been competitive, but it has never been so impersonal. Applications that once passed through the hands of recruiters and hiring managers now flow through algorithms that sort, score, and discard applicants in seconds. For every candidate who celebrates a job offer, there are thousands more who receive rejection emails so generic they feel mass-produced. And when those rejections are generated by machines, the experience becomes even more isolating. It is easy to feel like a number, a data point, a checkbox that wasn’t ticked. But as Vivi’s experience shows, every application represents a real person with bills to pay, dreams to chase, and a life that extends far beyond a CV. The rise of AI in recruitment does not have to be a bad thing—it could be used to reduce bias, increase efficiency, and help candidates find the right roles. But without transparency, without accountability, and without a human touch to balance the technology, it risks becoming another barrier in a system that is already difficult to navigate. Vivi is not giving up. She continues to apply, to freelance, to build her consultancy, and to remind herself that her worth is not defined by an automated reply. But she also hopes that employers will listen, that the campaign will spark change, and that before too long, hiring will become more human again. Because in the end, the people on the other side of the screen deserve more than an algorithm’s verdict. They deserve to be seen.
