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Found 11 papers, 1 papers with code
Discipline and Label: A WEIRD Genealogy and Social Theory of Data Annotation
Data annotation remains the sine qua non of machine learning and AI.
Assessing LLMs for Moral Value Pluralism
We apply RVR to the text generated by LLMs to characterize implicit moral values, allowing us to quantify the moral/cultural distance between LLMs and various demographics that have been surveyed using the WVS.
Walking the Walk of AI Ethics: Organizational Challenges and the Individualization of Risk among Ethics Entrepreneurs
Based on a qualitative analysis of technology workers tasked with integrating AI ethics into product development, we find that workers experience an environment where policies, practices, and outcomes are decoupled.
The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development
This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technology.
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Inappropriate design and deployment of machine learning (ML) systems leads to negative downstream social and ethical impact -- described here as social and ethical risks -- for users, society and the environment.
Healthsheet: Development of a Transparency Artifact for Health Datasets
Our findings from the interviewee study and case studies show 1) that datasheets should be contextualized for healthcare, 2) that despite incentives to adopt accountability practices such as datasheets, there is a lack of consistency in the broader use of these practices 3) how the ML for health community views datasheets and particularly \textit{Healthsheets} as diagnostic tool to surface the limitations and strength of datasets and 4) the relative importance of different fields in the datasheet to healthcare concerns.
Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker Incentives
The same group of workers then votes on a bonus payment structure, to elicit preferences.
Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure
In this paper, we introduce a rigorous framework for dataset development transparency which supports decision-making and accountability.
Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context
Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs.
Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics
Recent research on algorithmic fairness has highlighted that the problem formulation phase of ML system development can be a key source of bias that has significant downstream impacts on ML system fairness outcomes.
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
Rising concern for the societal implications of artificial intelligence systems has inspired a wave of academic and journalistic literature in which deployed systems are audited for harm by investigators from outside the organizations deploying the algorithms.
Computers and Society
