The field of machine learning continues to evolve, and there is no denying its impact on society and various aspects of our lives. It has never been more important for practitioners and innovators to consider a broader range of perspectives when building machine learning models and applications. This interesting topic came to us from The Machine Learning Times in their article, “Unleashing ML Innovation at Spotify with Ray.”
Machine learning is behind chatbots and predictive text, language translation apps, the shows Netflix suggests to you, and how your social media feeds are presented. It powers autonomous vehicles and machines that can diagnose medical conditions based on images. When companies today deploy artificial intelligence (AI) programs, they are most likely using machine learning.
Most organizations have little knowledge on how AI systems make decisions and, as a result, how the results are applied in the various target fields for AI and machine learning. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
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