Intersectionality is a critical framework that, through inquiry and praxis, allows us to examine
how social inequalities persist through domains of structure and discipline. Given AI fairness' …

Generative AI systems across modalities, ranging from text (including code), image, audio,
and video, have broad social impacts, but there is no official standard for means of …

This contribution analyzes the self‐perception and political biases of OpenAI's Large
Language Model ChatGPT. Considering the first small‐scale reports and studies that have …

Demographic biases and social stereotypes are common in pretrained language models
(PLMs), and a burgeoning body of literature focuses on removing the unwanted …

Analysts, managers, and policymakers are interested in predictive analytics capable of
offering better foresight. It is generally accepted that in forecasting scenarios involving …

The recent release of very large language models such as PaLM and GPT-4 has made an
unprecedented impact in the popular media and public consciousness, giving rise to a …

With language models becoming increasingly ubiquitous, it has become essential to
address their inequitable treatment of diverse demographic groups and factors. Most …

The widespread adoption of Machine Learning systems, especially in more decision-critical
applications such as criminal sentencing and bank loans, has led to increased concerns …

Fairness measurement is crucial for assessing algorithmic bias in various types of machine
learning (ML) models, including ones used for search relevance, recommendation …

Entity bias widely affects pretrained (large) language models, causing them to rely on
(biased) parametric knowledge to make unfaithful predictions. Although causality-inspired …