What are some strategies for developing algorithms that are less prone to bias?

Mar 22, 2024 ... Research suggests that digital ageism, that is, age-related bias, is present in the development and deployment of machine learning (ML) models. May 17, 2024 ... A set of ML models (random forest, XGBoost, multiplayer perceptron) are trained using citizen weather station observations and UKV variables ... 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 ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. In linear regression analysis, bias refers to the error that is introduced by approximating a real-life problem, which may be complicated, by a much simpler ... Dec 11, 2024 ... In the first of a series of research notes on bias in artificial intelligence (AI), this literature review examines available literature on ... Both of these existing methods address the problem of bias by adjusting the machine learning model. However, oftentimes it is the training data itself – i.e., ... Mar 19, 2010 ... A bias value allows you to shift the activation function to the left or right, which may be critical for successful learning. Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric ...