Bias in AI occurs when results cannot be generalized widely. Although most people associate algorithm bias resulting from preferences or exclusions in training data, bias can also be introduced by how data is obtained, how...

One of the key issues with AI is that algorithms developed in one institution or one set of data may not perform as well when used at different institutions with different data. Researchers at Mount...

Besides issues in getting a hold of large and diverse datasets, annotation or labeling, and sexy new methods to train models (synthetic data and federated learning!,) transparency also relates to model interpretability—in other words, humans...

We have been examining the issues of obtaining data (enough of it! and high quality) and preparing that data to be used in training and validating models. One emerging way to deal with the issue...

Transparency of data and AI algorithms is also a major concern. Transparency is relevant at multiple levels. First, in the case of supervised learning, , the accuracy of predictions relies heavily on the accuracy of...