While algorithm audits are growing rapidly in commonality and public importance, relatively
little scholarly work has gone toward synthesizing prior work and strategizing future research …

Many AI ethics guidelines have recently been published that center the fairness,
accountability, sustainability, and transparency of algorithmic decision-making. Relatively …

Large language models (LLMs) represent a major advance in artificial intelligence (AI)
research. However, the widespread use of LLMs is also coupled with significant ethical and …

Algorithms are ubiquitous and critical sources of information online, increasingly acting as
gatekeepers for users accessing or sharing information about virtually any topic, including …

Design biases in NLP systems, such as performance differences for different populations,
often stem from their creator's positionality, ie, views and lived experiences shaped by …

Important decisions that impact humans lives, livelihoods, and the natural environment are
increasingly being automated. Delegating tasks to so-called automated decision-making …

We propose differential fairness, a multi-attribute definition of fairness in machine learning
which is informed by intersectionality, a critical lens arising from the humanities literature …

How do software engineers identify and act on their ethical concerns? Past work examines
how software practitioners navigate specific ethical principles such as “fairness”, but this …

Open source software communities are a significant site of AI development, but “Ethical AI”
discourses largely focus on the problems that arise in software produced by private …

A growing proportion of human interactions are digitized on social media platforms and
subjected to algorithmic decision-making, and it has become increasingly important to …