Changyang He

Changyang He, Nina Baranowska, Josu Andoni Eguíluz Castañeira et al.

Post-market fairness monitoring is now mandated to ensure fairness and accountability for high-risk employment AI systems under emerging regulations such as the EU AI Act. However, effective fairness monitoring often requires access to sensitive personal data, which is subject to strict legal protections under data protection law. Multi-party computation (MPC) offers a promising technical foundation for compliant post-market fairness monitoring, enabling the secure computation of fairness metrics without revealing sensitive attributes. Despite growing technical interest, the operationalization of MPC-based fairness monitoring in real-world hiring contexts under concrete legal, industrial, and usability constraints remains unknown. This work addresses this gap through a co-design approach integrating technical, legal, and industrial expertise. We identify practical design requirements for MPC-based fairness monitoring, develop an end-to-end, legally compliant protocol spanning the full data lifecycle, and empirically validate it in a large-scale industrial setting. Our findings provide actionable design insights as well as legal and industrial implications for deploying MPC-based post-market fairness monitoring in algorithmic hiring systems.

Changyang He, Yue Deng, Alessandro Fabris et al.

The susceptibility to biases and discrimination is a pressing issue in today's labor markets. While digital recruitment systems play an increasingly significant role in human resource management, a systematic understanding of human-centered design principles for fair online hiring remains lacking, particularly considering the gap between idealized conceptualizations of fairness in research and actual fairness concerns expressed by job seekers. To address this gap, this work explores the potential of developing a fair recruitment framework based on job seekers' fairness concerns shared in r/jobs, one of the largest online job communities. Through a grounded theory approach, we uncover four overarching themes of job seekers' fairness concerns: personal attribute discrimination beyond legally protected attributes, interaction biases, improper interpretations of qualifications, and power imbalance. Drawing on value sensitive design, we derive design implications for fair algorithms and interfaces in recruitment systems, integrating them into a conceptual framework that spans different hiring stages.

Yue Deng, Changyang He

Robotaxis are emerging as a promising form of urban mobility, but removing human drivers fundamentally reshapes passenger-vehicle interaction and raises new design challenges. To inform robotaxi design based on real-world experience, we conducted 18 semi-structured interviews and autoethnographic ride experiences to examine users' perceptions, experiences, and expectations for robotaxi design. We found that users valued benefits such as increased agency and consistent driving. However, they also encountered challenges such as limited flexibility, insufficient transparency, and emergency handling concerns. Notably, users perceived robotaxis not merely as a mode of transportation, but as autonomous, semi-private transitional spaces, which made users feel less socially intrusive to engage in personal activities. Safety perceptions were polarized: some felt anxiety about reduced control, while others viewed robotaxis as safer than humans due to their cautious, law-abiding nature. Based on the findings, we propose a user-driven design framework spanning hailing, pick-up, traveling, and drop-off phases to support trustworthy, transparent, and accountable robotaxi design.

Zheng Chen, Yushi Feng, Jisheng Dang et al. · pku

Large Language Models (LLMs) have attained human-level fluency in text generation, which complicates the distinguishing between human-written and LLM-generated texts. This increases the risk of misuse and highlights the need for reliable detectors. Yet, existing detectors exhibit poor robustness on out-of-distribution (OOD) data and attacked data, which is critical for real-world scenarios. Also, they struggle to provide interpretable evidence to support their decisions, thus undermining the reliability. In light of these challenges, we propose IPAD (Inverse Prompt for AI Detection), a novel framework consisting of a Prompt Inverter that identifies predicted prompts that could have generated the input text, and two Distinguishers that examine the probability that the input texts align with the predicted prompts. Empirical evaluations demonstrate that IPAD outperforms the strongest baselines by 9.05% (Average Recall) on in-distribution data, 12.93% (AUROC) on out-of-distribution data, and 5.48% (AUROC) on attacked data. IPAD also performs robustly on structured datasets. Furthermore, an interpretability assessment is conducted to illustrate that IPAD enhances the AI detection trustworthiness by allowing users to directly examine the decision-making evidence, which provides interpretable support for its state-of-the-art detection results.