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.

翻译：在通过材料筛选评估求职或招生能力的流程中，决策者可能受到固有认知偏差的影响（如筛选顺序或锚定信息），导致评估结果不一致。为应对这一挑战，我们通过访谈七位专家，深入理解他们在筛选过程中遇到的难点及对辅助支持的需求。基于这些洞见，我们提出BiasEye——一种具备偏差感知能力的实时交互式材料筛选可视化系统。该系统通过提升信息可及性与透明度来增强用户对认知偏差的觉察能力，同时借助机器学习方法建模个体筛选偏好，帮助用户识别并缓解偏差。一项包含20名参与者的混合设计用户研究结果表明，相较于缺乏偏差感知功能的基线系统，BiasEye显著提升了参与者的偏差觉察度，并增强了其最终决策的信心。最后，我们探讨了机器学习和可视化技术在人类决策任务中缓解偏差的潜在应用前景。