What are the responsibilities of AI practitioners in mitigating bias?

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 ... Feb 4, 2019 ... Bias can creep in at many stages of the deep-learning process, and the standard practices in computer science aren't designed to detect it. May 17, 2024 ... A set of ML models (random forest, XGBoost, multiplayer perceptron) are trained using citizen weather station observations and UKV variables ... Mar 20, 2023 ... Debiasing ML refers to the process of mitigating or removing the effects of bias from the model or the data used to train the model. Mar 9, 2023 ... We know that synthetic data remains one of the most reliable and cost-effective solutions out there that improves the performance of the overall solution. 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. Oct 9, 2024 ... Selection bias occurs if a dataset's examples are chosen in a way that is not reflective of their real-world distribution. Selection bias can ... Oct 8, 2021 ... Supervised machine learning depends on labelled datasets, often processed and prepared by a data scientist or specialist. Bias in this labelling ... This review will discuss the relevant ethical and bias considerations in AI-ML specifically within the pathology and medical domain. Feb 15, 2023 ... Conclusions. Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML ...