How can we effectively communicate the limitations, potential biases, and fairness considerations of AI systems to diverse stakeholders, including end-users and policymakers?

We argue that algorithmic bias evolves with human interaction in an iterative manner, which may have a long-term effect on algorithm performance and humans' ... Feb 20, 2024 ... In this review, we present a framework for understanding the balance of opportunities and challenges for minimizing bias in medical imaging. Jul 28, 2022 ... Long Story Short: Omitted Variable Bias in Causal Machine Learning ... We derive general, yet simple, sharp bounds on the size of the omitted ... Jul 24, 2024 ... Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital ... Jun 3, 2014 ... We conduct a meta-analysis of all relevant, high quality primary studies of defect prediction to determine what factors influence predictive performance. May 17, 2024 ... A set of ML models (random forest, XGBoost, multiplayer perceptron) are trained using citizen weather station observations and UKV variables ... May 22, 2019 ... The private and public sectors are increasingly turning to artificial intelligence (AI) systems and machine learning algorithms to automate ... Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. May 31, 2022 ... Additionally, ML algorithms tend to overfit to the data on which they are trained, which entails the learning of spurious relationships present ... Apr 27, 2023 ... Bias can be weeded out in a timely fashion if you collect reliable and comprehensive training data, select the most appropriate learning model, conduct ...