{"id":16877,"date":"2023-08-17T06:07:15","date_gmt":"2023-08-17T06:07:15","guid":{"rendered":"https:\/\/www.impactmania.com\/im\/?page_id=16877"},"modified":"2026-03-31T09:42:17","modified_gmt":"2026-03-31T09:42:17","slug":"ai-hiring-eu","status":"publish","type":"page","link":"https:\/\/www.impactmania.com\/im\/ai-hiring-eu\/","title":{"rendered":"AI Hiring Program (EU)"},"content":{"rendered":"
<\/div><\/div><\/div><\/div>
<\/span><\/div><\/div><\/div><\/div><\/div>
<\/div><\/div><\/span>

REPORT<\/h3><\/span>
<\/div><\/div><\/div>

Read Full Report<\/a> | Presentation to the EU-wide committee (video<\/a>)<\/strong><\/p>\n

As Artificial Intelligence (AI) tools increasingly shape the hiring landscape, a critical question arises: do these tools contribute to the widening labor gap between open positions and qualified, yet non-traditional, applicants? The current EU labor market is dealing with a significant imbalance of job vacancies and skilled job seekers. Especially, females and migrants find themselves over-represented in surplus occupations involving clerical, design, and hospitality jobs. Jobs involving tech professionals, construction workers, and medical specialists are short staffed. Moreover, widespread hiring practices including algorithmic hiring tools screen out many qualified candidates and artificially constrain the talent pool that companies consider, essentially \u201chiding\u201d prospective workers. We conducted qualitative and quantitative experiments to test algorithmic hiring tools such as Applicant Tracking Systems (ATS). The report also includes learning from the industry’s leading talent tech companies and recommendations on how to minimize bias in algorithmic systems.<\/p>\n

This 3-year project was part of the FINDHR initiative (consortium of a dozen universities and institutions across Europe) and funded by the EU Horizon grant as part of: https:\/\/cordis.europa.eu\/project\/id\/101070212<\/a>.<\/p>\n<\/div><\/div><\/div><\/div><\/div><\/div>

<\/div><\/div><\/span>

PROGRAM<\/h3><\/span>
<\/div><\/div><\/div><\/div><\/div>

Help Minimize Bias and Discrimination in AI Hiring<\/h3>\n<\/div>

Jos\u00e9 never received an invitation to a job interview until he changed his name to Joe(1)<\/span>. Today, algorithmic hiring is touted as a fair system, sourcing and selecting candidates based on merit; nothing could be further from the truth. Ten years after the Business Insider article about Jos\u00e9 was published, name discrimination is still rampant, \u201capplicants with ethnic names are 57% less likely to be considered for leadership roles\u201d<\/em> (2)<\/span>.<\/p>\n<\/div>

<\/div><\/div>

You are invited to help minimize and mitigate bias and discrimination in algorithmic hiring!<\/strong><\/p>\n

impactmania<\/em> is part of the European Union\u2019s initiative Fairness and Intersectional Non-Discrimination in Human Recommendation<\/a><\/em>.<\/p>\n

We are collecting and testing resumes in AI systems to:<\/p>\n