Employers in the United Kingdom are beginning to use AI recruitment software during the hiring process. Women seeking technical jobs are facing challenges getting their CVs past these automated tracking systems (ATS) due to unconscious bias. To optimise their CVs for AI recruitment software, they should adopt specific strategies.
UK companies are using AI recruitment software for two main reasons. There is an influx of applications, so manually combing through each of them would take a long time. Also, it makes the initial screening process easier for hiring managers, as they no longer have to check each CV for eligibility. 57% of hiring managers have said they are exploring or experimenting with AI, making this a more prevalent concern for applicants looking to pass the
initial scan.
ATS uses several criteria when evaluating applicants. The following are some specific focuses:
Women applicants face an even deeper disadvantage in ATS systems because AI models are typically trained on historical data. This data often reflects existing gender biases in technology fields, which can lead ATS systems to discard applications from women. It could flag references to women’s colleges or organisations, citing their gender as a legitimate reason to disqualify them.
However, well-designed AI can reduce these human biases. Programmers must train the AI to focus solely on skills and qualifications, ruling out gender altogether. Explainable AI allows developers to see why biases occurred, so adding a tool like this to the hiring process can help hire more women in the technology industry.
ATS scans documents for specific keywords, so ensuring applicants include them in CVs is an effective way to pass the initial screening. A method for finding keywords is to analyse the job description carefully and identify the most important words.
Ensure those words are in the CV a few times. AI should register those keywords and pass applicants on to a human recruiter.
Tailor CVs to fit specific job descriptions
It is generally a good idea to tailor CVs to fit job descriptions. Human hiring managers and AI software like to see how applicants match the initial job posting. One method is to use similar language from the job description.
Putting relevant experience and accomplishments at the top of the CV is another good way to pass the screening. Include less important information at the bottom or do not include it at all.
Choose proper formatting
Because AI reads clean, well-formatted text more effectively, applicants need to write their CVs in simple terms, without unnecessary features. If the machine sees too many images or tables it doesn’t understand, it may throw out the application before it reaches a human hiring manager. A good rule is to type out the CV in plain text first and then move it around to look aesthetically pleasing afterwards.
AI in recruitment is here to stay. While applicants can and should optimise their CVs to navigate applicant tracking systems successfully, the responsibility cannot sit with candidates alone.
Women bring critical skills, innovation and leadership to the technology sector. Ensuring their talent isn’t filtered out by poorly designed systems is not just a diversity issue.
By combining smart CV strategies with continued advocacy for transparent, bias-aware AI tools, we can move towards a hiring process that truly rewards skills and potential. The future of tech depends on inclusive recruitment practices, and that starts with both better systems and empowered applicants.
Devin Partida is Editor-in-Chief of ReHack Magazine at ReHack.com. Devin is especially interested in projects related to technology, startups, women in tech, FinTech and data security.
