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.

This technical article will teach you how to pre-process data, create your own neural networks, and train and evaluate models using the US-CERT's simulated insider threat dataset. The methods and solutions are designed for non-domain experts; particularly cyber security professionals. We will start our journey with the raw data provided by the dataset and provide examples of different pre-processing methods to get it "ready" for the AI solution to ingest. We will ultimately create models that can be re-used for additional predictions based on security events. Throughout the article, I will also point out the applicability and return on investment depending on your existing Information Security program in the enterprise. Note: To use and replicate the pre-processed data and steps we use, prepare to spend 1–2 hours on this page. Stay with me and try not to fall asleep during the data pre-processing portion. What many tutorials don't state is that if you're starting from scratch; data pre-processing takes up to 90% of your time when doing projects like these. The author provides these methods, insights, and recommendations *as is* and makes no claim of warranty.

RapidMiner, the company that delivers real data science, fast and simple, today announced the immediate availability of RapidMiner 8.1 and RapidMiner Auto Model, a new addition to RapidMiner Studio that accelerates everything data scientists do when building machine learning models. "Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal."

"Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal." RapidMiner Auto Model accelerates the entire data science lifecycle using automated machine learning.

Recently Tom (@neuralmarket) and I had the chance to work together with Amanda Shiga (@AmandaShiga) from Nonlinear Digital to build web analytics process using RapidMiner. Amanda has an on-going pilot project to apply data mining techniques to clickstream and user behavior data collected from her client's website. The website has a number of value-weighted micro-conversions, such as newsletter signup, or downloading a whitepaper, or event registration. For online retailers, seeing the visitors convert to paying customers is the ultimate goal. The focus of web analytics nowadays has shifted from getting visitors to a website to turning the web visitors into high value customers.