How can organizations and research teams foster a more diverse and inclusive environment to improve the understanding and mitigation of bias in AI development?

Aug 13, 2018 ... This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Model bias, in Machine Learning, refers to the presence of systematic errors in a model that can cause it to consistently make incorrect predictions. Jul 13, 2021 ... In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of ... Jun 1, 2023 ... In this review article, we discuss the implicit bias in training neural networks using gradient-based methods. Apr 27, 2023 ... Bias can be weeded out in a timely fashion if you collect reliable and comprehensive training data, select the most appropriate learning model, conduct ... Aug 18, 2021 ... We demonstrate that state-of-the-art scheduling systems cause the black patients in our data set to wait about 30% longer than nonblack patients. Jul 25, 2022 ... Machine learning bias is sometimes called algorithm bias or AI bias. Predictive models may be trained on data containing human decisions or ... In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, ... Jul 12, 2023 ... Healthcare algorithms and AI bias can contribute to existing health disparities for certain populations based on race, ethnicity, gender, age, or other ... As humanities scholars start to make use of the possibilities provided by AI, there is a risk that humanities scholarship can become polluted by hidden gender ...