Panel on AI and Inclusivity in the Labor Market

Update: unfortunately but understandably (in light of the pandemic), this conference has been post-poned until further notice.

December 9th I will join a panel discussion on AI and equal opportunities in the labor market (together with Siri Beerends & Rina Joosten-Rabou), at a conference organized by WOMEN INC. An offline event in Utrecht (Corona volente)!

More details and RSVP (in Dutch) here: Congres | “Hey Siri: Vind een geschikte kandidaat”

RecSys in HR Workshop Recording available

For those who missed it — like myself 😏 — we have now published the full recording of our Recommender Systems in Human Resources (RecSys in HR) Workshop, which was held on October 1st, in conjunction with the ACM RecSys Conference in Amsterdam.

Our workshop included two keynotes, eight paper presentations, breakout sessions, a virtual panel on the topics of the upcoming EU framework on AI, Fair & inclusive HR Tech, and how to “activate hidden workers.” See the full program here.

So, if you have 4h33m to spare, see the full recording below! 

(or use the convenient YouTube chapter to jump through the program 😅)

Many thanks to all co-organizers, contributors, paper authors, and participants, both virtual and in-person! And hopefully we’ll see each other at the second edition ✌️.

AI & Data Science MSc thesis internship projects @ Randstad Groep Nederland

Another academic year, another (short)list of potential projects. Are you a final-year AI or data science student, interested in doing an internship with us? Reach out! First, read below why you would want to join us, and scroll further down for the following project descriptions:

  1. Job Description Generation (NLP)
  2. Conversational/QA approaches for resume information extraction (NLP)
  3. Segmentation of resumes (NLP, CV)
  4. Synthetic data for bias mitigation in recommender systems
  5. Career pathing
  6. Algorithmic planning
  7. Open project: IR, RecSys, NLP, or fair AI
Continue reading “AI & Data Science MSc thesis internship projects @ Randstad Groep Nederland”

Two papers accepted at the RecSys in HR Workshop!

Happy to have learned we have two papers accepted at the first Recommender Systems in Human Resources Workshop, co-located with ACM RecSys 2021! These papers are the first academic publications of the Data Science Chapter at Randstad Groep Nederland!

  • [PDF] M. de Groot, J. Schutte, and D. Graus, “Job posting-enriched knowledge graph for skills-based matching,” in Recsys in hr 2021, Amsterdam, Netherlands, 2021.
    author = {de Groot, Maurits and Schutte, Jelle and Graus, David},
    title = {Job Posting-Enriched Knowledge Graph for Skills-based Matching},
    year = {2021},
    booktitle = {RecSys in HR 2021},
    numpages = {9},
    location = {Amsterdam, Netherlands},
    address = {Amsterdam, Netherlands},
  • [PDF] D. Lavi, V. Medentsiy, and D. Graus, “Consultantbert: fine-tuned siamese sentence-bert for matching jobs and job seekers,” in Recsys in hr 2021, Amsterdam, Netherlands, 2021.
    author = {Lavi, Dor and Medentsiy, Volodymyr and Graus, David},
    title = {conSultantBERT: Fine-tuned Siamese Sentence-BERT for Matching Jobs and Job Seekers},
    year = {2021},
    booktitle = {RecSys in HR 2021},
    numpages = {8},
    location = {Amsterdam, Netherlands},
    address = {Amsterdam, Netherlands},

Curious to know what they’re about? I tweet better than I blog 👇

Stay tuned for pre-prints! See the other accepted papers here.

Disclaimer: yes, I co-organize the workshop, but I was not involved with reviewing/decisions, we have a great (and independent) Program Committee for that!

Webinar on governing AI

Together with Helen Hulsker I joined of a webinar on governing AI by Dataiku, read the blurb here:

There are many obstacles for Data Scientists in effectively governing the use of AI such as, overcoming the bias inherent in historical data, agreeing on the boundaries of governing AI with the business, and perhaps most importantly, ensuring the adoption of good AI practices across the organisation.

So join Randstad and Dataiku as we set the stage around implementing AI in a high-risk domain from a technical/practical and jurisdictional perspective.

Check out the recording at BrightTALK!

Keynote on Data Science in HR at FEAST Workshop (@ ECML-PKDD 2021)

Excited to be giving a keynote at the International Workshop on Fair, Effective And Sustainable Talent management using data science (FEAST workshop), part of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021).

I’ll give a talk about some of our recent data science projects Randstad Groep Nederland, spanning from our job/candidate recommender systems, work on bias and bias mitigation, knowledge graphs, and deep embeddings for learning to match candidates to vacancies. 

Forget the Trolley Problem; Pragmatic and Fair AI in the Real World

Update: my article has been published on TowardsDataScience, selected as an editor’s pick, and highlighted in TowardsDataScience’s weekly newsletter “The Variable”!

The AI doomsday scenarios, ignited by books such as The Filter Bubble (2011) and Weapons of Math Destruction (2016), are slowly being superseded by more pragmatic and nuanced views of AI. Views in which we acknowledge we’re in control of AI and able to design them in ways that reflect values of our choice.

This shift can be seen in the rising involvement of computer scientists, e.g., through books such as The Ethical Algorithm (2019) or Understand, Manage, and Prevent Algorithmic Mitigate Bias (2019), books that describe and acknowledge the challenges and complexities of algorithmic fairness, but at the same time offer concrete methods and tools for more fair and ethical algorithms. This shift can too be seen in that the methods described in these books have already found their ways into the offerings of all major cloud providers, e.g., at the FAccT 2021 Tutorial “Responsible AI in Industry: Lessons Learned in Practice” Microsoft, Google, and Amazon demoed their fair AI solutions to the multidisciplinary audience of the FAccT community.

The message is clear: we can (and should!) operationalize algorithmic fairness.

Continue reading “Forget the Trolley Problem; Pragmatic and Fair AI in the Real World”

Co-organizing “RecSys in HR” workshop at RecSys 2021!

We received news that our workshop proposal “RecSys in HR: Workshop on Recommender Systems for Human Resources” was accepted for inclusion in the 15th ACM Conference on Recommender Systems (RecSys 2021) program! That means we’ll be running a full-day workshop with (research and position) papers, keynotes, and a panel (all TBD) during the conference which will be held in Amsterdam, 27th September-1st October 2021.

We wrote this workshop proposal with Toine Bogers (Aalborg University), Mesut Kaya (Aalborg University), Katrien Verbert (KU Leuven) and Francisco Gutiérrez (KU Leuven), at the initiative/idea of Toine, who virtually approached me in RecSys 2020’s :-D. Toine and Mesut work on a large research project with Denmark’s largest online recruitment portal, JobIndex.

For now, check out our stunning stub page at and stay tuned for updates!

Algorithmic bias and bias mitigation talk at the anti-discrimination hackathon

That was fun! The Online Anti-Discrimination Hackathon ran last weekend, a hakacthon co-organized by Ministerie van OCW, Inspectie SZW, Ministerie van BZK, and Hackathon Factory which revolved around “gender discrimination in data collection and labeling for automated assessment and selection of candidates.” Having ample experience in designing and developing AI-powered candidate selection systems, and the risks of algorithmic bias, I was happy to contribute to this hackathon with a few colleagues at Randstad Groep Nederland in several ways.

still of me giving the talk in front of a blue wall

Talk on Algorithmic Bias Mitigation in Automated Recruitment

First, I gave a (virtual, pre-recorded) talk on algorithmic matching, algorithmic bias, and bias mitigation in the domain of automated recruitment. More specifically, I shared how and where we use AI and recommender systems to facilitate job and candidate matching at Randstad Groep Nederland, and more generally about the challenges of bias and the opportunities of bias mitigation. I showcased both examples of misuse of AI, which results in discriminatory systems, and examples of how AI can be used to actively reduce or mitigate bias in the recruitment process. See the recording of my talk below:

In addition, me and a colleague joined live interactive roundtable sessions during the hackathon, and we brought a panel of four subject-matter experts for one-on-one sessions with hackathon teams and participants.

Read more

Joined the DDMA AI Committee

I recently joined the Artificial Intelligence committee of the Data Driven Marketing Association (DDMA), which aims to promote the use of responsible AI for marketing and other forms of interaction with customers. Read the small introductory post published by the DDMA below (in Dutch)

David Graus, Randstad Groep Nederland, Commissie AI: “AI biedt enorme kansen om filterbubbels te doorbreken en bias te reduceren”

David heeft een achtergrond in zoekmachinetechnologie waarmee hij inmiddels een carrière heeft opgebouwd gericht op de ontwikkeling van personalisatie- en aanbevelingssystemen. In zijn huidige rol geeft David leiding aan de data scientists van Randstad Groep Nederland en is hij betrokken bij alle facetten van het bouwen van AI-systemen, van ideation tot aan het daadwerkelijk in productie brengen en monitoren van systemen. “Uit ervaring weet ik dat je als AI-ontwerper en -bouwer over de mogelijkheden beschikt om personalisatie- en aanbevelingssystemen in de basis ethisch verantwoord op te zetten. AI biedt daarom grote kansen om filterbubbels te doorbreken en bias te reduceren. Als lid van de Commissie AI hoop ik de sector hierbij te helpen.“