Auditing recommendation systems has attracted growing attention due to increasing concerns over filter bubbles, unfairness, and data misuse. A common approach is sock-puppet auditing, where autonomous agents interact with platforms to reveal risks. However, existing approaches rely on hard-coded agents, lacking adaptability to dynamic GUI layouts and generating behaviors far from those of real users, limiting the comprehensiveness and representativeness of assessment. To address these issues, we introduce AuditAgent, an LLM-powered GUI-agent framework for risk auditing. AuditAgent simulates realistic user preferences and performs adaptive, human-like interactions on recommendation platforms. This design enables more thorough and faithful auditing, providing comprehensive assessments across multiple risk dimensions, including filter bubbles, unfairness, and data misuse.

Artificial intelligence (AI) systems are playing an increasingly crucial role in people's lives, and their frequent unfair behaviors raise concerns about fairness. To unveil the unfairness in AI systems, researchers conduct fairness auditing on these systems. However, existing fairness auditing works often focus on group fairness while ignoring discriminatory phenomena among individuals. To unearth discriminatory phenomena against individuals within AI systems, this paper proposes an individual fairness auditing framework, termed ''substantiating'', which can identify discrimination instances within AI systems by constructing individual samples. To construct these samples for substantiating, auditors often have to rely on subjective prior knowledge, lacking guidelines on how to construct unfair samples. To address this issue, this paper introduces two categories of automated sample generation methods that can rapidly find unfair samples within a limited number of queries to the system and demonstrate their effectiveness through experiments. This paper evaluates the proposed auditing framework among three categories of stakeholders in AI fairness: auditors, AI model developers, and non-technical personnel. The research findings point out their demand for individual fairness audits of AI systems and highlight how the framework supports a reliable and convenient individual fairness audit.

Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations (exposure) of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.