What are the ethical considerations related to data collection and labeling for fairness?

Mar 26, 2018 ... In the case of self-learning systems, the type of “garbage” is biased data. Left unchecked, feeding biased data to self-learning systems can ... This paper postulates that the root causes of bias are the prior decisions that affect- (a) what data was selected and (b) the labels assigned to those ... Mar 3, 2021 ... Abstract. Western societies are marked by diverse and extensive biases and inequality that are unavoidably embedded in the data used to train ... Sep 3, 2024 ... AI bias occurs when an AI system produces systematically prejudiced results due to erroneous assumptions in the machine learning process. These ... 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 ... Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, ... Feb 7, 2024 ... The landscape is overrun with “neutral” analysts using some kind of sentiment analysis to lazily label something as right or left. This MIT ... This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. 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.