RAI guidelines: Method for generating responsible AI guidelines grounded in regulations and usable by (non-) technical roles
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 …
development of ethical and responsible AI systems. However, these guidelines are often …
Policy advice and best practices on bias and fairness in AI
JM Alvarez, AB Colmenarejo, A Elobaid… - Ethics and Information …, 2024 - Springer
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 …
making it difficult for novel researchers and practitioners to have a bird's-eye view picture of …
Do Responsible AI Artifacts Advance Stakeholder Goals? Four Key Barriers Perceived by Legal and Civil Stakeholders
A Kawakami, D Wilkinson… - Proceedings of the AAAI …, 2024 - ojs.aaai.org
The responsible AI (RAI) community has introduced numerous processes and artifacts---
such as Model Cards, Transparency Notes, and Data Cards---to facilitate transparency and …
such as Model Cards, Transparency Notes, and Data Cards---to facilitate transparency and …
Guidelines for Integrating Value Sensitive Design in Responsible AI Toolkits
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 …
technology design process. In parallel, Responsible AI (RAI) advocates for the development …
Leveraging ontologies to document bias in data
M Russo, ME Vidal - arXiv preprint arXiv:2407.00509, 2024 - arxiv.org
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 …
biases. This puts emphasis on the importance of operating under practices that enable the …
OxonFair: A Flexible Toolkit for Algorithmic Fairness
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 …
classification. Compared to existing toolkits:(i) We support NLP and Computer Vision …
Thoughtful Adoption of NLP for Civic Participation: Understanding Differences Among Policymakers
Natural language processing (NLP) tools have the potential to boost civic participation and
enhance democratic processes because they can significantly increase governments' …
enhance democratic processes because they can significantly increase governments' …
Learning about Responsible AI On-The-Job: Learning Pathways, Orientations, and Aspirations
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 …
such resources when practicing RAI. However, AI practitioners may not have the relevant …
Law and the Emerging Political Economy of Algorithmic Audits
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 …
focusing on novel and innovative ways and methodologies to audit the functioning of …
JupyterLab in Retrograde: Contextual Notifications That Highlight Fairness and Bias Issues for Data Scientists
Current algorithmic fairness tools focus on auditing completed models, neglecting the
potential downstream impacts of iterative decisions about cleaning data and training …
potential downstream impacts of iterative decisions about cleaning data and training …