  AI is increasingly playing a pivotal role in businesses and organizations,
impacting the outcomes and interests of human users. Automated Machine Learning
(AutoML) streamlines the machine learning model development process by
automating repetitive tasks and making data-driven decisions, enabling even
non-experts to construct high-quality models efficiently. This democratization
allows more users (including non-experts) to access and utilize
state-of-the-art machine-learning expertise. However, AutoML tools may also
propagate bias in the way these tools handle the data, model choices, and
optimization approaches adopted. We conducted an experimental study of
User-interface-based open source AutoML tools (DataRobot, H2O Studio, Dataiku,
and Rapidminer Studio) to examine if they had features to assist users in
developing fairness-aware machine learning models. The experiments covered the
following considerations for the evaluation of features: understanding use case
context, data representation, feature relevance and sensitivity, data bias and
preprocessing techniques, data handling capabilities, training-testing split,
hyperparameter handling, and constraints, fairness-oriented model development,
explainability and ability to download and edit models by the user. The results
revealed inadequacies in features that could support in fairness-aware model
development. Further, the results also highlight the need to establish certain
essential features for promoting fairness in AutoML tools.
