What are the potential trade-offs between applying data augmentation techniques to improve model performance and the potential for exacerbating existing biases?

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. 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 ... Jul 15, 2024 ... As institutions of higher education turn to AI machine learning and data-driven algorithms to make their work more efficient, a new study ... Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... What is machine learning bias (AI bias)? ... Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm ... Feb 4, 2021 ... Data bias in machine learning is a type of error in which certain elements of a dataset are more heavily weighted and/or represented than others ... According to some futurists, financial markets' automation will substitute increasingly sophisticated, objective, analytical, model-based assessments of, for ... Jul 12, 2023 ... Healthcare algorithms and AI bias can contribute to existing health disparities for certain populations based on race, ethnicity, gender, age, or other ... Jun 8, 2023 ... Bias mitigation in machine learning models · 1. Pre-processing algorithms · 2. In-processing algorithms · 3. Post-processing algorithms. Jul 27, 2023 ... Bias is an additional parameter in a neural network that is used to adjust the output along with the weighted sum of the inputs to the neuron.