Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture behavioral variations equitably and can then contribute to bias. We evaluate speech features extracted from two widely used speech analysis tools, OpenSMILE and Praat, to assess their reliability when considering adolescents with autism. We observed considerable variation in features across tools, which influenced model performance across context and demographic groups. We encourage domain-relevant verification to enhance the reliability of machine learning models in clinical applications.

Exploration, Evaluation and Explanation for ML Models: Machine learning technologies are at the core of a new generation of intelligent applications that differentiate disruptive businesses from established players. Today, business tasks like product recommendation, image tagging, sentiment analysis, churn prediction, fraud detection and lead scoring can only be achieved using machine learning (ML). To build these applications at scale, companies are fast adopting tools such as Dato's GraphLab Create and Predictive Services, enabling developers to accelerate the innovation cycle, and quickly take their ideas from inspiration to production. Industry practitioners understand that in order to secure adoption of intelligent applications, they must build trust in their models and predictions – that is, gain confidence that their models are achieving their desired outcomes and a good understanding of how predictions are made. In this talk, Carlos Guestrin, CEO of Dato, Inc. and Amazon Professor of Machine Learning at the University of Washington, describes: With these techniques, companies can start to have the means to gain trust and confidence in the models and predictions behind their core business applications.

Additionally, the fraud detection and prevention programs that keep your bank account safe are also utilizing predictive analytics. The most sophisticated of these programs analyze your spending habits and compare each purchase against them. The purchases themselves also have fraudulence probability scores (a 1,000 online purchase paid to a company based in Timbuktu is more likely to be fraudulent than a 200 purchase at your local grocery store). If anything seems fishy, the bank sends you an alert to ensure your funds aren't compromised if fraud is, in fact, taking place.