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Found 10 papers, 2 papers with code
Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor
In AI research and practice, rigor remains largely understood in terms of methodological rigor -- such as whether mathematical, statistical, or computational methods are correctly applied.
From Stem to Stern: Contestability Along AI Value Chains
As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap.
An Empirical Exploration of Trust Dynamics in LLM Supply Chains
In particular, a limited number of relevant trustors (e. g., end-users) and trustees (i. e., AI systems) have been considered, and empirical explorations have remained in laboratory settings, potentially overlooking factors that impact human-AI relationships in the real world.
Hear Me Out: A Study on the Use of the Voice Modality for Crowdsourced Relevance Assessments
The creation of relevance assessments by human assessors (often nowadays crowdworkers) is a vital step when building IR test collections.
Explainability in AI Policies: A Critical Review of Communications, Reports, Regulations, and Standards in the EU, US, and UK
This might be due to the willingness to conciliate explanations foremost as a risk management tool for AI oversight, but also due to the lack of a consensus on what constitutes a valid algorithmic explanation, and how feasible the implementation and deployment of such explanations are across stakeholders of an organization.
A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities
Despite the impressive performance of Artificial Intelligence (AI) systems, their robustness remains elusive and constitutes a key issue that impedes large-scale adoption.
Towards a multi-stakeholder value-based assessment framework for algorithmic systems
We, therefore, contribute to current ML auditing practices with an assessment framework that visualizes closeness and tensions between values and we give guidelines on how to operationalize them, while opening up the evaluation and deliberation process to a wide range of stakeholders.
Exploring Data Pipelines through the Process Lens: a Reference Model forComputer Vision
Researchers have identified datasets used for training computer vision (CV) models as an important source of hazardous outcomes, and continue to examine popular CV datasets to expose their harms.
Designing Evaluations of Machine Learning Models for Subjective Inference: The Case of Sentence Toxicity
Machine Learning (ML) is increasingly applied in real-life scenarios, raising concerns about bias in automatic decision making.
Unfairness towards subjective opinions in Machine Learning
Despite the high interest for Machine Learning (ML) in academia and industry, many issues related to the application of ML to real-life problems are yet to be addressed.
