Much attention has focused on algorithmic audits and impact assessments to hold
developers and users of algorithmic systems accountable. But existing algorithmic …

This is the interim publication of the first International Scientific Report on the Safety of
Advanced AI. The report synthesises the scientific understanding of general-purpose AI--AI …

Human-centric computer vision (HCCV) data curation practices often neglect privacy and
bias concerns, leading to dataset retractions and unfair models. HCCV datasets constructed …

An emerging concern in algorithmic fairness is the tension with privacy interests. Data
minimization can restrict access to protected attributes, such as race and ethnicity, for bias …

Computer Vision (CV) is playing a significant role in transforming society by utilizing
machine learning (ML) tools for a wide range of tasks. However, the need for large-scale …

In the burgeoning field of Large Language Models (LLMs), developing a robust safety
mechanism, colloquially known as" safeguards" or" guardrails", has become imperative to …

How realistic is the idea of an art ficial intelligence-assisted, decentralized and privacy-
enhancingfuture generation ofthe World Wide Web? Could data governance and other legal …

The imperative for smart surveillance systems to robustly detect anomalies poses a unique
challenge given the sensitivity of visual data and privacy concerns. We propose a novel …

Harms resulting from the development and deployment of facial processing technologies
(FPT) have been met with increasing controversy. Several states and cities in the US have …

Debates in AI ethics often hinge on comparisons between AI and humans: which is more
beneficial, which is more harmful, which is more biased, the human or the machine? These …