BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917590051323904 |
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| author | Liu, Qianyu Jiang, Haoran Pan, Zihao Han, Qiushi Peng, Zhenhui Li, Quan |
| author_facet | Liu, Qianyu Jiang, Haoran Pan, Zihao Han, Qiushi Peng, Zhenhui Li, Quan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_09148 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment Liu, Qianyu Jiang, Haoran Pan, Zihao Han, Qiushi Peng, Zhenhui Li, Quan Human-Computer Interaction 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. |
| title | BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.09148 |