创造健康的摩擦：确定职位推荐解释的利益相关者需求
Creating Healthy Friction: Determining Stakeholder Requirements of Job Recommendation Explanations
AI总结：
本研究通过混合方法用户研究评估了可解释职位推荐系统，发现真实解释未显著提升决策速度和准确性，但利益相关者更倾向于将其视为提供健康摩擦的决策支持工具。
摘要：
在招聘中越来越多地使用信息检索（主要通过职位推荐系统，JRSs），对求职者、招聘人员和公司产生了巨大影响。因此，在最近的立法中，此类系统被认定为高风险。这要求JRSs必须值得信赖且透明，允许利益相关者理解为何做出特定推荐。为满足这一要求，需要确定利益相关者的确切偏好和需求。为此，我们使用一项现实的、基于任务的混合设计用户研究（n=30）评估了一个可解释的职位推荐系统，在该研究中，利益相关者必须根据模型的解释做出决策。该混合方法评估包含两个客观指标——正确性和效率，以及三个主观指标——信任、透明度和有用性。这些指标对每位参与者评估两次，一次使用真实解释，一次使用随机解释。该研究包括在执行针对每个利益相关者群体调整的任务时，遵循出声思考协议的定性分析。我们发现，为利益相关者提供真实解释并未显著提高决策速度和准确性。我们的结果显示，在所有利益相关者类型的系统感知信任、有用性和透明度方面，真实解释优于随机解释的趋势不显著。我们确定，利益相关者更多地受益于将解释作为能够提供健康摩擦的决策支持进行交互，而不是如先前假设的劝导工具。
英文摘要：
The increased use of information retrieval in recruitment, primarily through job recommender systems (JRSs), can have a large impact on job seekers, recruiters, and companies. As a result, such systems have been determined to be high-risk in recent legislature. This requires JRSs to be trustworthy and transparent, allowing stakeholders to understand why specific recommendations were made. To fulfill this requirement, the stakeholders' exact preferences and needs need to be determined. To do so, we evaluated an explainable job recommender system using a realistic, task-based, mixed-design user study (n=30) in which stakeholders had to make decisions based on the model's explanations. This mixed-methods evaluation consisted of two objective metrics - correctness and efficiency, along with three subjective metrics - trust, transparency, and usefulness. These metrics were evaluated twice per participant, once using real explanations and once using random explanations. The study included a qualitative analysis following a think-aloud protocol while performing tasks adapted to each stakeholder group. We find that providing stakeholders with real explanations does not significantly improve decision-making speed and accuracy. Our results showed a non-significant trend for the real explanations to outperform the random ones on perceived trust, usefulness, and transparency of the system for all stakeholder types. We determine that stakeholders benefit more from interacting with explanations as decision support capable of providing healthy friction, rather than as previously-assumed persuasive tools.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
