What is human-centered about human-centered AI? A map of the research landscape
The application of Artificial Intelligence (AI) across a wide range of domains comes with both
high expectations of its benefits and dire predictions of misuse. While AI systems have …
high expectations of its benefits and dire predictions of misuse. While AI systems have …
Vqgan-clip: Open domain image generation and editing with natural language guidance
Generating and editing images from open domain text prompts is a challenging task that
heretofore has required expensive and specially trained models. We demonstrate a novel …
heretofore has required expensive and specially trained models. We demonstrate a novel …
[BOOK][B] Towards a standard for identifying and managing bias in artificial intelligence
R Schwartz, R Schwartz, A Vassilev, K Greene… - 2022 - dwt.com
As individuals and communities interact in and with an environment that is increasingly
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …
virtual, they are often vulnerable to the commodification of their digital footprint. Concepts …
Automated fact-checking to support professional practices: systematic literature review and meta-analysis
Fact-checking is a time-consuming process that automation can potentially make more
efficient. This study provides a comprehensive, multidisciplinary state of the art that …
efficient. This study provides a comprehensive, multidisciplinary state of the art that …
Exploring how machine learning practitioners (try to) use fairness toolkits
Recent years have seen the development of many open-source ML fairness toolkits aimed
at hel** ML practitioners assess and address unfairness in their systems. However, there …
at hel** ML practitioners assess and address unfairness in their systems. However, there …
The data-production dispositif
Machine learning (ML) depends on data to train and verify models. Very often, organizations
outsource processes related to data work (ie, generating and annotating data and …
outsource processes related to data work (ie, generating and annotating data and …
Toward User-Driven Algorithm Auditing: Investigating users' strategies for uncovering harmful algorithmic behavior
Recent work in HCI suggests that users can be powerful in surfacing harmful algorithmic
behaviors that formal auditing approaches fail to detect. However, it is not well understood …
behaviors that formal auditing approaches fail to detect. However, it is not well understood …
A hunt for the snark: Annotator diversity in data practices
Diversity in datasets is a key component to building responsible AI/ML. Despite this
recognition, we know little about the diversity among the annotators involved in data …
recognition, we know little about the diversity among the annotators involved in data …
Are “intersectionally fair” ai algorithms really fair to women of color? a philosophical analysis
Y Kong - Proceedings of the 2022 ACM Conference on Fairness …, 2022 - dl.acm.org
A growing number of studies on fairness in artificial intelligence (AI) use the notion of
intersectionality to measure AI fairness. Most of these studies take intersectional fairness to …
intersectionality to measure AI fairness. Most of these studies take intersectional fairness to …
The dimensions of data labor: A road map for researchers, activists, and policymakers to empower data producers
Many recent technological advances (eg ChatGPT and search engines) are possible only
because of massive amounts of user-generated data produced through user interactions …
because of massive amounts of user-generated data produced through user interactions …