Guidance

Reduce unconscious bias in CV screening

Published 4 March 2026

Applies to England, Scotland and Wales

Purpose of this action

This action aims to reduce unconscious bias when you are first screening the CVs of job applicants. It helps you assess applicants’ capabilities more fairly. Unconscious bias can happen when people:

• unknowingly make decisions based on assumptions
• allow personal experiences to get in the way of impartial judgement

Anonymising CVs and asking people to list their experience in years can help those who have taken a break from work – for example, for unpaid care. This approach ensures their relevant experience remains visible.

Benefits and evidence

Improving how you screen CVs can give you more applicants to choose from and make hiring fairer. It can also help bring your recruitment and inclusive policy goals closer together. 

Research shows that traditional screening can disadvantage qualified applicants because of their life circumstances. Studies find that people who show periods of unemployment are often less successful than those without gaps.^{[footnote 1]}

People returning to work after a career break of 12 months or more (sometimes called returners) may face specific barriers.^{[footnote 2][footnote 3]} A UK study of STEM professionals found that 53% of returners reported having experienced negative bias when applying for jobs due to a lack of recent experience.^{[footnote 4]}

As the majority of UK returners are women,^{[footnote 5]} career gaps for caring responsibilities can systematically disadvantage women. This can contribute to the gender pay gap.^{[footnote 6]}

Evidence from the US suggests that career gaps for childcare are sometimes penalised more than gaps for unemployment.^{[footnote 7]} Listing experience by years (for example, ‘4 years’) instead of specific dates (for example, ‘2016 to 2020’) may help reduce this disadvantage. This approach has been shown to increase the chance of an applicant getting an interview or job offer by 15%.^{[footnote 8]}

Anonymised CVs may be effective in reducing gender bias during screening. When gender is hidden, women may have an improved chance of being interviewed and hired.^{[footnote 9]} However, anonymisation is not a ‘one-size-fits-all’ solution. It may prevent you from using targeted recruitment campaigns to build a representative workforce.^{[footnote 10]} Check that changing your screening process does not conflict with other diversity actions before selecting this action.

Implementing this action

To help increase the number of women who apply successfully, you can redesign your CV format.^{[footnote 11][footnote 12][footnote 13]}

You can:

• offer a standard CV template that lets people list experience using a ‘number of years’ option 
• pair years of experience with role-specific competencies to make better hiring decisions
• use an anonymised application form and CV template
• enable a screening mode in your applicant tracking system to hide identifiable data
• explain the anonymisation process and how you will assess experience in your application guidance
• train HR staff and hiring managers on how to assess anonymised CVs

Using artificial intelligence (AI) responsibly

Artificial intelligence (AI) tools used for CV screening often learn from historical data and may disadvantage women.^{[footnote 14]} If past recruitment practices contain gender biases, AI may repeat them. Automated tools may also negatively score gaps in employment, which disproportionately affects women.^{[footnote 15]} 

Prospective employers are responsible for ensuring that recruitment is non-discriminatory under the Equality Act 2010. You must review and adjust AI outputs to ensure they do not break these laws.

See the Department for Science, Innovation and Technology’s guidance on responsible AI in recruitment for more information.

Tracking progress

You might want to consider tracking the progress of this action by measuring:

• the proportion of recruitment campaigns that use an anonymous application form and CV template
• the breakdown of applicants who are shortlisted (or not shortlisted), invited for interview and offered the role by sex – including the combination of sex and other characteristics (such as ethnicity or disability status) to highlight specific trends for different groups of men and women
• surveys from candidates about their recruitment experience

Where possible, you should compare any data you gather with ‘baseline’ data from previous recruitment campaigns.

Data privacy

Some or all of the equality information you collect is likely to be ‘special category personal data’, meaning it has special legal protections. 

Ensure that you are complying with the UK’s data protection legislation when you collect and analyse employees’ data.

Get advice and approval from your organisation’s privacy or data protection expert before you start.

1. Baert S (2018). Hiring discrimination: An overview of (almost) all correspondence experiments since 2005. In Audit studies: Behind the scenes with theory, method, and nuance. ↩

2. Career Returners (2025). Career Returners Indicator.  ↩

3. The Behavioural Insights Team (2025). How to improve gender equality in the workplace: actions for employers.  ↩

4. STEM Returners (2025). The STEM Returners Index. ↩

5. Office for Equality and Opportunity (2024). STEM ReCharge programme evaluation.  ↩

6. Government Equalities Office (2018) The gender pay gap in the UK: evidence from the UKHLS ↩

7. Weisshaar K (2018). From opt out to blocked out: The challenges for labor market re-entry after family-related employment lapses. American Sociological Review, 83(1), 34-60.  ↩

8. The Behavioural Insights Team (2025). How to improve gender equality in the workplace: actions for employers.  ↩

9. Aslund O and Nordstrom Skans O (2007). Do anonymous job application procedures level the playing field?, Working Paper, No.2007:31, IFAU - Institute for Labour Market Policy Evaluation ↩

10. Young Women’s Trust (2020). Equality at work? Positive action in gender segregated apprenticeships.  ↩

11. The Behavioural Insights Team (2025). How to improve gender equality in the workplace: actions for employers. ↩

12. The Behavioural Insights Team (2021). Facilitating return to the labour market with a novel CV format intervention.  ↩

13. Behaghel L, Crépon B, and Le Barbanchon T (2015). Unintended effects of anonymous résumés, American Economic Journal: Applied Economics, 7(3), 1–27. ↩

14. The Alan Turing Institute (2021). Where are the women? ↩

15. Fuller, J. B., et al. (2021). Hidden Workers: Untapped Talent. Harvard Business School Project on Managing the Future of Work and Accenture. ↩