How can we make black-box AI models more transparent and accountable in terms of bias?

Oct 18, 2022 ... We develop and evaluate a machine learning system for identifying five major categories of biased and discriminatory language in job advertisements. According to some futurists, financial markets' automation will substitute increasingly sophisticated, objective, analytical, model-based assessments of, for ... The bias–variance trade-off implies that a model should balance underfitting and overfitting: Rich enough to express underlying structure in data and simple ... 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 ... 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 ... May 23, 2016 ... Machine Bias. There's ... The advantage of using a generic statistical learning algorithm is that you KNOW what information goes in. Bias in deep learning: A case study · Missing Data and Patients Not Identified by Algorithms: They occur due to known or unknown gaps in datasets used to train ... 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. Sep 5, 2022 ... This article aims to examine some of the ways in which racial bias gets included in AI and touch upon what can be done to mitigate these biases. 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 ...