Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate

10 June 2024

Papers citing "Situated Ground Truths: Enhancing Bias-Aware AI by Situating Data Labels with SituAnnotate"

9 / 9 papers shown

Title
Stitching Gaps: Fusing Situated Perceptual Knowledge with Vision Transformers for High-Level Image Classification Delfina Sol Martinez Pandiani Nicolas Lazzari Valentina Presutti ViT 54 1 0 29 Feb 2024
DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering S. Borgo R. Ferrario Aldo Gangemi Nicola Guarino Claudio Masolo Daniele Porello Emilio M. Sanfilippo L. Vieu 18 91 0 03 Aug 2023
D-BIAS: A Causality-Based Human-in-the-Loop System for Tackling Algorithmic Bias Bhavya Ghai Klaus Mueller 66 40 0 10 Aug 2022
"Garbage In, Garbage Out" Revisited: What Do Machine Learning Application Papers Report About Human-Labeled Training Data? R. Geiger Dominique Cope Jamie Ip Marsha Lotosh Aayush Shah Jenny Weng Rebekah Tang 42 63 0 05 Jul 2021
Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligence Shakir Mohamed Marie-Therese Png William S. Isaac 90 406 0 08 Jul 2020
Measuring Social Biases in Grounded Vision and Language Embeddings Candace Ross Boris Katz Andrei Barbu 84 65 0 20 Feb 2020
Racial Bias in Hate Speech and Abusive Language Detection Datasets Thomas Davidson Debasmita Bhattacharya Ingmar Weber 102 459 0 29 May 2019
Datasheets for Datasets Timnit Gebru Jamie Morgenstern Briana Vecchione Jennifer Wortman Vaughan Hanna M. Wallach Hal Daumé Kate Crawford 266 2,194 0 23 Mar 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 109 645 0 13 Feb 2018