Search Results for author:
Found 9 papers, 3 papers with code
Mimetic Models: Ethical Implications of AI that Acts Like You
An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression.
Measuring Representational Harms in Image Captioning
We propose multiple measurement techniques for each type of harm.
REAL ML: Recognizing, Exploring, and Articulating Limitations of Machine Learning Research
Transparency around limitations can improve the scientific rigor of research, help ensure appropriate interpretation of research findings, and make research claims more credible.
An Uncommon Task: Participatory Design in Legal AI
Despite growing calls for participation in AI design, there are to date few empirical studies of what these processes look like and how they can be structured for meaningful engagement with domain experts.
Computer Vision and Conflicting Values: Describing People with Automated Alt Text
In this paper, we investigate the ethical dilemmas faced by companies that have adopted the use of computer vision for producing alt text: textual descriptions of images for blind and low vision people, We use Facebook's automatic alt text tool as our primary case study.
Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffs
Disaggregated evaluations of AI systems, in which system performance is assessed and reported separately for different groups of people, are conceptually simple.
Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP
We survey 146 papers analyzing {``}bias{''} in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing {``}bias{''} is an inherently normative process.
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process.
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
How are algorithmic assessments built, validated, and examined for bias?
