Many guidelines for responsible AI have been suggested to help AI practitioners in the
development of ethical and responsible AI systems. However, these guidelines are often …

We present OxonFair, a new open source toolkit for enforcing fairness in binary
classification. Compared to existing toolkits:(i) We support NLP and Computer Vision …

The literature addressing bias and fairness in AI models (fair-AI) is growing at a fast pace,
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …

The responsible AI (RAI) community has introduced numerous processes and artifacts---
such as Model Cards, Transparency Notes, and Data Cards---to facilitate transparency and …

Value Sensitive Design (VSD) is a framework for integrating human values throughout the
technology design process. In parallel, Responsible AI (RAI) advocates for the development …

Machine Learning (ML) systems are capable of reproducing and often amplifying undesired
biases. This puts emphasis on the importance of operating under practices that enable the …

Natural language processing (NLP) tools have the potential to boost civic participation and
enhance democratic processes because they can significantly increase governments' …

Prior work has developed responsible AI (RAI) toolkits and studied how AI practitioners use
such resources when practicing RAI. However, AI practitioners may not have the relevant …

For almost a decade now, scholarship in and beyond the ACM FAccT community has been
focusing on novel and innovative ways and methodologies to audit the functioning of …

Current algorithmic fairness tools focus on auditing completed models, neglecting the
potential downstream impacts of iterative decisions about cleaning data and training …