Making recruitment more inclusive

Delecraz, Sebastien; Eltarr, Loukman; Becuwe, Martin; Bouxin, Henri; Boutin, Nicolas; Oullier, Olivier

doi:10.1145/3524491.3527309

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…Recruitment and employment have always been important topics in HCI [12,15,40]. According to previous studies [21,26,73], technology (e.g., social media and automatic systems) has played a significant role in the recruitment and employment process.…”

Section: Technology Support For Recruitment and Job-seekingmentioning

confidence: 99%

“There is a Job Prepared for Me Here”: Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in China

Wang,

Hu,

Wen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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In recent years, the global unemployment rate has remained persistently high. Compounding this issue, the ageing population in China often encounters additional challenges in finding employment due to prevalent age discrimination in daily life. However, with the advent of social media, there has been a rise in the popularity of short videos and live-streams for recruiting ageing workers. To better understand the motivations of ageing job seekers to engage with these video-based recruitment methods and to explore the extent to which such platforms can empower them, we conducted an interview-based study with ageing job seekers who have had exposure to these short recruitment videos and live-streaming channels. Our findings reveal that these platforms can provide a job-seeking choice that is particularly friendly to ageing job seekers, effectively improving their disadvantaged situation. CCS CONCEPTS• Human-centered computing → Empirical studies in collaborative and social computing.

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Section: Technology Support For Recruitment and Job-seekingmentioning

confidence: 99%

“There is a Job Prepared for Me Here”: Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in China

Wang,

Hu,

Wen

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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“…Furthermore, Yang et al [44] pay attention to explicitly modeling the two-way selection intentions of the job providers and the job seekers, which difers from the previous unidirectional process or overall matching. Diferently, Delecraz et al [45] focus on preventing the pitfall of unfairness and discrimination as much as possible. Tey also introduce metrics to assess the level of unfairness.…”

Section: Job-resume Matchingmentioning

confidence: 99%

Exploring Internal and External Interactions for Semi‐Structured Multivariate Attributes in Job‐Resume Matching

Shao

Song

Zheng

et al. 2023

International Journal of Intelligent Systems

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Job-resume matching (JRM) is the core of online recruitment services for predicting the matching degree between a job post and a resume. Most of the existing methods for JRM achieve a promising performance by simplifying this task as a matching between the free-text attributes in the job post and the resume. However, they neglect the contributions of the semistructured multivariate attributes such as education and salary, which will result in an unsuccessful prediction. To address this issue, we propose a novel approach to comprehensively explore the Internal and EXternal InTeractions for semistructured multivariate attributes in JRM, i.e., InEXIT. In detail, we first encode the key and the value of each attribute as well as its source into the same semantic space. Next, to explore the complex relationships among the multivariate attributes, we propose to hierarchically model the internal interactions among the multivariate attributes inside the job post and the resume, as well as the external interactions between the job post and the resume. In particular, a stepwise fusion mechanism is designed to respectively integrate the key embeddings and the source embeddings into the value embeddings so as to clearly indicate the key and the source of the value. Finally, we employ an aggregation matching layer to predict the matching degree. We quantify the improvements of InEXIT against the competitive baselines on a real-world dataset, showing a general improvement of 4.28%, 4.10%, and 3.56% over the state-of-the-art baseline in terms of AUC, accuracy, and F1 score, respectively.

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“…• True Positive Rate Parity (TPRP) (Delecraz et al, 2022): TPRP in candidate recommendation for a given binary protected attribute A ∈ {a 1 , a 2 } and recommendation list Q u for job u is defined as…”

Section: Fairness and Unfairness Metricsmentioning

confidence: 99%

“…The responses are registered in terms of values from 1 (disagree) to 5 (agree) for questions related to each factor. • Dataset Bias Metrics (Delecraz et al, 2022): Statistical parity (SP) and Disparate impact (DI) metrics are used for measuring the bias in datasets with respect to binary attribute A ∈ {a 1 , a 2 }. Here, ρ(u, x) = 1 denotes that candidate u is qualified for a job post x randomly sampled from the set of job posts:…”

Section: Fairness and Unfairness Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives

Kumar,

Grosz,

Rekabsaz

et al. 2023

Front. Big Data

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Recommender systems (RSs) have become an integral part of the hiring process, be it via job advertisement ranking systems (job recommenders) for the potential employee or candidate ranking systems (candidate recommenders) for the employer. As seen in other domains, RSs are prone to harmful biases, unfair algorithmic behavior, and even discrimination in a legal sense. Some cases, such as salary equity in regards to gender (gender pay gap), stereotypical job perceptions along gendered lines, or biases toward other subgroups sharing specific characteristics in candidate recommenders, can have profound ethical and legal implications. In this survey, we discuss the current state of fairness research considering the fairness definitions (e.g., demographic parity and equal opportunity) used in recruitment-related RSs (RRSs). We investigate from a technical perspective the approaches to improve fairness, like synthetic data generation, adversarial training, protected subgroup distributional constraints, and post-hoc re-ranking. Thereafter, from a legal perspective, we contrast the fairness definitions and the effects of the aforementioned approaches with existing EU and US law requirements for employment and occupation, and second, we ascertain whether and to what extent EU and US law permits such approaches to improve fairness. We finally discuss the advances that RSs have made in terms of fairness in the recruitment domain, compare them with those made in other domains, and outline existing open challenges.

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Making recruitment more inclusive

Cited by 7 publications

References 36 publications

“There is a Job Prepared for Me Here”: Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in China

“There is a Job Prepared for Me Here”: Understanding How Short Video and Live-streaming Platforms Empower Ageing Job Seekers in China

Exploring Internal and External Interactions for Semi‐Structured Multivariate Attributes in Job‐Resume Matching

Fairness of recommender systems in the recruitment domain: an analysis from technical and legal perspectives

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