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

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

A Language Model based Job Recommender

Matching candidates to job openings is a hard real world problem of economic interest that thus far de es researchers' attempts to tackle it. Collaborative ltering methods, which have proven to be highly e ective in other domains, have a di cult time nding success when applied to Human Resources. Aside from the well known cold-start issue there are other problems speci c to the recruitment world that explain the poor results attained. In particular, fresh job openings arrive all the time and they have relatively short expiration periods. In addition, there is a large volume of passive users who are not actively looking for a job, but that would consider a change if a suitable o er came their way. The two constraints combined suggest that content based models may be advantageous. Previous attempts to attack the problem have tried to infer relevance from a variety of sources. Indirect information captured from web server and search engine logs, as well as eliciting direct feedback from users or recruiters have all been polled and used to construct models. In contrast, this thesis departs from previous methods and tries to exploit resume databases as a primary source for relevance information, a rich resource that in my view remains greatly underutilized. Relevance models are adapted for the task at hand and a formulation is derived to model job transitions as a Markov process, with the justi cation being based on David Ricardo's principle of comparative advantage. Empirical results are compiled following the Cran eld benchmarking methodology and compared against several standard competing algorithms