As Artificial Intelligence (AI) technologies proliferate, concern has centered around the long-
term dangers of job loss or threats of machines causing harm to humans. All of this concern …

The significant advancements in applying artificial intelligence (AI) to healthcare decision-
making, medical diagnosis, and other domains have simultaneously raised concerns about …

Mitigating bias in machine learning systems requires refining our understanding of bias
propagation pathways: from societal structures to large-scale data to trained models to …

Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions
that have far‐reaching impact on individuals and society. Their decisions might affect …

The phenomenon of $\textit {bias amplification} $ occurs when models amplify training set
biases at test time. Existing metrics measure bias amplification with respect to single …

Artificial Intelligence (AI), and discussions surrounding its potential uses, have escalated into
polarised debates about the future, capturing a wide range of utopian and dystopian …

Artificial Intelligence has the potential to exacerbate societal bias and set back decades of
advances in equal rights and civil liberty. Data used to train machine learning algorithms …

As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …

It is not news that, for all its promised benefits, artificial intelligence has a bias problem.
Concerns regarding racial or gender bias in AI have arisen in applications as varied as …

During each stage of a dataset creation and development process, harmful biases can be
accidentally introduced, leading to models that perpetuates marginalization and …