When Ads Become Profiles: Large-Scale Audit of Algorithmic Biases and LLM Profiling Risks
This work exposes privacy risks and algorithmic biases in social media ad targeting, with implications for users, regulators, and AI governance.
The researchers conducted a large-scale audit of 435,000 Facebook ad impressions in Australia, revealing algorithmic biases that disproportionately target vulnerable groups with gambling and political ads, and demonstrated that LLMs can accurately reconstruct users' demographic profiles from ad streams, matching or exceeding human performance.
Automated ad targeting on social media is opaque, creating risks of exploitation and invisibility to external scrutiny. Users may be steered toward harmful content while independent auditing of these processes remains blocked. Large Language Models (LLMs) raise a new concern: the potential to reverse-engineer sensitive user attributes from exposure alone. We introduce a multi-stage auditing framework to investigate these risks. First, a large-scale audit of over 435,000 ad impressions delivered to 891 Australian Facebook users reveals algorithmic biases, including disproportionate Gambling and Politics ads shown to socioeconomically vulnerable and politically aligned groups. Second, a multimodal LLM can reconstruct users' demographic profiles from ad streams, outperforming census-based baselines and matching or exceeding human performance. Our results provide the first empirical evidence that ad streams constitute rich digital footprints for public AI inference, highlighting urgent privacy risks and the need for content-level auditing and governance.