A compilation of my articles on the topic of Machine learning Interpretability. Source: Human-Centered Machine Learning by Jess Holbrook Have you ever wondered why an important e-mail of yours was marked as spam, why Spotify keeps suggesting a particular song or why were you recommended a movie in horror genre on NetFlix? Well, occasionally we might … Continue reading Human-Centric & Interpretable Machine Learning
An overview of GoogleAI’s model Interpretability technique in terms of human-friendly concepts. How convolutional neural networks see the world It’s not enough to know if a model works, we need to know how it works: Sundar Pichai The emphasis today is slowly moving towards model interpretability rather than model predictions alone. The real essence of Interpretability, however, … Continue reading TCAV: Interpretability Beyond Feature Attribution
How to measure your model’s fairness and decide on the best fairness metrics. Photo by rawpixel on Unsplash Machine learning models are being increasingly used to make decisions that affect people’s lives. With this power comes a responsibility to ensure that the model predictions are fair and not discriminating. ProPublica, an independent, investigative journalism outfit, came out … Continue reading Is your Machine Learning Model Biased?