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

arXiv: https://arxiv.org/abs/2402.09148 · HTML: https://mlsanity.com/paper/2402.09148/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Bias in the Loop: How Humans Evaluate AI-Generated Suggestions](https://mlsanity.com/paper/2509.08514/) (2025)

## Abstract (arXiv)

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.