In Simple Words
Imagine a digital matchmaker that instantly sifts through thousands of applicant profiles to recommend top fits, like a streaming service recommending movies based on actual viewing preferences rather than genre covers. However, if the recommendation engine is trained on biased past ratings, the engine will stop recommending minority content unless explicitly recalibrated to prioritize diverse options while maintaining fit quality.
Job seekers in an online labor market
- Study comparison group
- Group used for comparison
- Algorithmic resume-writing help
- 8% higher chance of being hired
The synthesis reports that algorithmic resume-writing assistance increased hiring probability by 8% in an online labor market. It is a different evaluation from the algorithm diversity study discussed nearby; this figure does not claim a diversity improvement.
See source →Research question
Traditional public employment matchings suffer from high manual search costs and inefficient screening, while automated algorithmic screening tools risk amplifying historical human biases against underrepresented demographic groups if trained on biased historical hiring data.
Participants
Job seekers and employers participating in large online labor markets and field evaluation settings.
Study Design
Randomized evaluations testing machine learning recommendation engines and resume writing assistance tools against standard search interfaces, alongside algorithm re-design extensions.
Results
Algorithmic recommendation of workers to employers improved recruit pool quality and increased overall hiring. Algorithmic writing assistance on job seekers' resumes boosted hiring probability by 8 percent. Initial hiring algorithms selected far fewer Black and Hispanic candidates, but re-engineered algorithms successfully increased demographic diversity while maintaining high hiring rates.
Takeaway
Deploy audited, diversity-recalibrated machine learning matching algorithms within public job placement platforms to automate candidate screening and resume preparation support.
Source and citation
See source →Evidence in Labor Market Policies and Implications for Brazil: Job Search Assistance
Copyright © 2023 Inter-American Development Bank.
Full source notice
Copyright © 2023 Inter-American Development Bank ("IDB"). This work is subject to a Creative Commons license CC BY 3.0 IGO (https://creativecommons.org/licenses/by/3.0/igo/legalcode). The terms and conditions indicated in the URL link must be met and the respective recognition must be granted to the IDB. Further to section 8 of the above license, any mediation relating to disputes arising under such license shall be conducted in accordance with the WIPO Mediation Rules. Any dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the United Nations Commission on International Trade Law (UNCITRAL) rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this license. Note that the URL link includes terms and conditions that are an integral part of this license.