Software Doping Analysis for Human Oversight

Software Doping Analysis for Human Oversight

11 August 2023

Sebastian Biewer

Holger Hermanns

Anne Lauber-Rönsberg

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Papers citing "Software Doping Analysis for Human Oversight"

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Title
Building Symbiotic AI: Reviewing the AI Act for a Human-Centred, Principle-Based Framework Miriana Calvano Antonio Curci Giuseppe Desolda Andrea Esposito Rosa Lanzilotti Antonio Piccinno 108 1 0 14 Jan 2025
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 87 403 0 19 Oct 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 304 444 0 15 Oct 2020
Bias and Discrimination in AI: a cross-disciplinary perspective Xavier Ferrer Tom van Nuenen Jose Such Mark Coté Natalia Criado FaML 46 146 0 11 Aug 2020
Verifying Individual Fairness in Machine Learning Models Philips George John Deepak Vijaykeerthy Diptikalyan Saha FaML 49 59 0 21 Jun 2020
Two Simple Ways to Learn Individual Fairness Metrics from Data Debarghya Mukherjee Mikhail Yurochkin Moulinath Banerjee Yuekai Sun FaML 63 96 0 19 Jun 2020
A systematic review and taxonomy of explanations in decision support and recommender systems Ingrid Nunes Dietmar Jannach XAI 48 330 0 15 Jun 2020
On the Apparent Conflict Between Individual and Group Fairness Reuben Binns FaML 68 311 0 14 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 121 6,269 0 22 Oct 2019
Metric Learning for Individual Fairness Christina Ilvento FaML 80 97 0 01 Jun 2019
On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection Vivian Lai Chenhao Tan 78 377 0 19 Nov 2018
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 300 2,114 0 24 Oct 2016
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 86 838 0 16 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016