BiasEye：面向公正候选人评估的偏见感知实时交互式材料筛选系统
BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment
AI总结：
本文提出BiasEye，一个偏见感知的实时交互式材料筛选可视化系统，通过机器学习建模个人筛选偏好来帮助用户识别和缓解认知偏见，用户研究表明其能有效提升决策者的偏见意识与决策信心。
摘要：
在通过材料筛选评估求职或学生招聘能力的过程中，决策者可能会受到固有认知偏见（如筛选顺序或锚定信息）的影响，从而导致评估结果不一致。为了应对这一挑战，我们对七位专家进行了访谈，以了解他们在筛选过程中面临的困难与支持需求。基于这些洞察，我们提出了BiasEye，一个偏见感知的实时交互式材料筛选可视化系统。BiasEye通过提高信息的可访问性和透明度来增强对认知偏见的意识。它还通过一种对个人筛选偏好进行建模的机器学习（ML）方法，帮助用户识别并缓解偏见。一项包含20名参与者的混合设计用户研究结果表明，与缺乏偏见感知功能的基线系统相比，BiasEye提高了参与者的偏见意识，并增强了他们对做出最终决策的信心。最后，我们讨论了机器学习和可视化在缓解人类决策任务中偏见的潜力。
英文摘要：
In the process of evaluating competencies for job or student recruitment through material screening, decision-makers can be influenced by inherent cognitive biases, such as the screening order or anchoring information, leading to inconsistent outcomes. To tackle this challenge, we conducted interviews with seven experts to understand their challenges and needs for support in the screening process. Building on their insights, we introduce BiasEye, a bias-aware real-time interactive material screening visualization system. BiasEye enhances awareness of cognitive biases by improving information accessibility and transparency. It also aids users in identifying and mitigating biases through a machine learning (ML) approach that models individual screening preferences. Findings from a mixed-design user study with 20 participants demonstrate that, compared to a baseline system lacking our bias-aware features, BiasEye increases participants' bias awareness and boosts their confidence in making final decisions. At last, we discuss the potential of ML and visualization in mitigating biases during human decision-making tasks.
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。
