What are the future directions for research on fairness and accountability in AI?

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 ... Feb 21, 2022 ... A model's ability to generalize is influenced by both the diversity of the data and the way the model is trained, researchers report. Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions. This manuscript is a literature study that provides a detailed survey regarding the different categories of bias and the corresponding approaches. Sep 15, 2023 ... In this article, we will embark on a journey to understand bias and fairness in machine learning algorithms, exploring their implications, real-world examples, ... 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. Aug 8, 2023 ... Encord Active offers features to help reduce bias in datasets, allowing you to identify and address any potential biases in your data workflow. 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., ... Aug 18, 2023 ... Here, we apply machine learning (ML) to produce a blended TROPOMI+GOSAT product that uses GOSAT to correct biases in the TROPOMI data and ... The purpose of this study is to illustrate the harmful effects of learned human-like biases in MLAGs, to highlight how improper algorithm training leads to ...