Optimal information ordering for sequential detection with cognitive biases

Akl, Naeem; Tewfik, A.H.

doi:10.1109/eusipco.2016.7760281

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Material screening serves as the crucial initial step in assessing a candidate's qualifications. To enhance efficiency and fairness, various methods have been developed to optimize procedures such the holistic admission process [31] and information ordering [2], or automate particular tasks such as resume screening [47], assessment [34], and information extraction [30,35]. Natural Language Processing (NLP) [28] also has been used to detect and correct resume errors [41] and conduct rating classification while reducing human bias [3].…”

Section: Ai-enhanced Approaches For Materials Screening and Holistic ...mentioning

confidence: 99%

“…Given the substantial volume of applications, various automated techniques have emerged to assist in systematically and efficiently extracting and storing information. These techniques include academic exploration [25,54] and commercial solutions such as Daxtra 1 and Bello AI 2 . In terms of material screening, computer programs can provide a more objective and consistent assessment method based on predefined criteria [34].…”

Section: Introductionmentioning

confidence: 99%

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BiasEye: A Bias-Aware Real-time Interactive Material Screening System for Impartial Candidate Assessment

Liu,

Jiang,

Pan

et al. 2024

Proceedings of the 29th International Conference on Intelligent User Interfaces

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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. CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); Visualization; User studies.

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