Public opinion on recommender systems has become increasingly wary in recent years. In line with this trend, lawmakers have also started to become more critical of such systems, resulting in the introduction of new laws focusing on aspects such as privacy, fairness, and explainability for recommender systems and AI at large. These concepts are especially crucial in high-risk domains such as recruitment. In recruitment specifically, decisions carry substantial weight, as the outcomes can significantly impact individuals' careers and companies' success. Additionally, there is a need for a multi-stakeholder approach, as these systems are used by job seekers, recruiters, and companies simultaneously, each with its own requirements and expectations. In this paper, I summarize my current research on the topic of explainable, multi-stakeholder job recommender systems and set out a number of future research directions.

翻译：近年来，公众对推荐系统的看法日益谨慎。顺应这一趋势，立法者也对这些系统持更加批判的态度，从而催生了聚焦于推荐系统乃至整个人工智能领域隐私性、公平性与可解释性等方面的新法规。这些概念在招聘等高风险领域尤为重要。具体到招聘场景，决策影响重大，其结果可能显著影响个人职业发展与公司运营成效。此外，由于这类系统同时被求职者、招聘人员和企业所使用，各方皆有其特定需求与期望，因此需要采用多方利益相关者视角进行研究。本文总结了当前在可解释的多方利益相关者职位推荐系统领域的研究进展，并提出了若干未来研究方向。