Category: Research

🗣️ Please consider donating your (anonymized) CV to advance research into bias mitigation in algorithmic hiring!

With Randstad we are part of a consortium of research institutions (e.g., University of Amsterdam, Radboud Universiteit, Universitat Pompeu Fabra), civil society organizations (e.g., AlgorithmWatch), and companies (e.g., Adevinta) under the EU-funded FINDHR research project.

The FINDHR project aims to:
1️⃣ create new ways to measure algorithmic bias,
2️⃣ propose technical implementations for bias mitigation strategies, and
3️⃣ meaningfully incorporate human expertise
in algorithmic hiring systems (i.e., job/job seeker recommender systems).

To achieve these ambitious goals, the project requires real CVs and résumés. For that reason, FINDHR has initiated a CV donation campaign, where you’ll be able to donate your (anonymized) CV with just a few clicks. These donated CVs will be used to generate a dataset of realistic-but-fake synthetic CVs, that will serve as the basis for studying and developing bias and bias mitigation in job/job seeker recommender systems.

Your donated data will be safe: stored securely, can be deleted/withdrawn at any time upon request, and only accessible to authorized persons in the FINDHR research project who are required to sign confidentiality agreements.

Please consider donating your CV to accelerate research into bias and bias mitigation strategies for algorithmic hiring systems! For more details, check the donation campaign’s FAQ (or ping me!).

Donate your CV with just a couple of clicks here: findhr.eu/datadonation!

🎉 A little success to share: three of our former data science interns at the Data Science chapter at Randstad Groep Nederland have written and published their master theses at our upcoming RecSys in HR Workshop; an academic workshop that revolves around AI in HR, which is part of an ACM International Conference on Recommender Systems (the AI systems used for matching; whether it is Netflix movies to users, or in our case; jobs to job seekers).

As always, the work of the students is pretty technical, but I will go ahead and try to provide little human-understandable summaries below.

Explainable Career Path Predictions using Neural Models

Roan Schellingerhout worked under supervision of Volodymyr Medentsiy on Explainable Career Path Prediction using Neural Networks, where he trained deep neural networks on our own talent work history data, to create a tool that can help consultants or talents to predict possible career switches, given as input a talent’s work history. The predictions are visually explained, in the sense that the underlying reasons for proposing a certain job are provided. Roan tested these visualizations on consultants, and found consultants generally like them.

End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates

• A. M. Arafan, D. Graus, F. P. Santos, and E. Beauxis-Aussalet, “End-to-end bias mitigation in candidate recommender systems with fairness gates,” in Recsys in hr’22: the 2\textsuperscriptnd workshop on recommender systems for human resources, 2022.
  [Bibtex]

  @inproceedings{arafan2022end,
  author = {Arafan, Adam Mehdi and Graus, David and Santos, Fernando P. and Beauxis-Aussalet, Emma},
  title = {End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates},
  year = {2022},
  booktitle = {RecSys in HR’22: The 2\textsuperscript{nd} Workshop on Recommender Systems for Human Resources},
  numpages = {8},
  location = {Seattle, WA, USA and Online},
  series = {CEUR Workshop Proceedings},
  url = {https://ceur-ws.org/Vol-3218/RecSysHR2022-paper_6.pdf},
  month={9}
  }

Adam Arafan worked under supervision of myself on “End-to-End Bias Mitigation in Candidate Recommender Systems with Fairness Gates,” in his thesis he experimented with making the SmartMatch Talent Recommender more fair (at the level of gender), either by changing the “input” of the algorithm (for example; by balancing male and female candidates in the training data), or by changing its “output” (for example: for a given list of candidates, go through the list to make sure the top 10 has a 50/50 balance between male and female candidates). His work is novel because these type of “bias mitigation” strategies have been studied in isolation, but never together.

Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks

• B. Platten, M. Macfarlane, D. Graus, and S. Mesbah, “Automated personnel scheduling with reinforcement learning and graph neural networks,” in Recsys in hr’22: the 2\textsuperscriptnd workshop on recommender systems for human resources, 2022.
  [Bibtex]

  @inproceedings{platten2022automated,
  author = {Platten, Benjamin and Macfarlane, Matthew and Graus, David and Mesbah, Sepideh},
  title = {Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks},
  year = {2022},
  booktitle = {RecSys in HR’22: The 2\textsuperscript{nd} Workshop on Recommender Systems for Human Resources},
  numpages = {10},
  location = {Seattle, WA, USA and Online},
  url = {https://ceur-ws.org/Vol-3218/RecSysHR2022-paper_1.pdf},
  series = {CEUR Workshop Proceedings},
  month={9}
  }

Ben Platten worked under supervision of Sepideh Mesbah on Automated Personnel Scheduling with Reinforcement Learning and Graph Neural Networks, in which he experimented with “reinforcement learning” (a specific machine learning paradigm) which in theory suits the challenging task of scheduling well. He experimented on a toy problem to assess that, indeed, the method seems to work quite well.

See the full list of accepted papers here: https://recsyshr.aau.dk/accepted-papers/.

And stay tuned for the pre-prints, which I’ll share as soon as they’re available!