What are the limitations of relying solely on quantitative metrics to assess fairness?

Detecting bias in your ML model can be done proactively or reactively, and depending on your specific case, either one is viable. Reactive bias detection ... Oct 9, 2024 ... Selection bias occurs if a dataset's examples are chosen in a way that is not reflective of their real-world distribution. Selection bias can ... Jul 13, 2021 ... In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of ... Jun 2, 2022 ... This article will discuss the use of AI and ML in cancer care and provide a framework for mitigating bias to achieve cancer equity. 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 25, 2022 ... Machine learning bias is sometimes called algorithm bias or AI bias. Predictive models may be trained on data containing human decisions or ... Nov 10, 2017 ... Machine learning can perpetuate and even amplify behavioral biases. By design, a social-media site filtering news based on user preferences ... Feb 21, 2024 ... USC computer science researchers propose a novel approach to mitigate bias in machine learning model training, specifically in image generation. Jul 24, 2024 ... Introduction: Machine learning (ML) algorithms have been heralded as promising solutions to the realization of assistive systems in digital ... Jan 31, 2018 ... This post assumes some familiarity with machine learning, and reinforcement learning in particular. If you are new to RL, I'd recommend ...