Abstract Machine learning has been increasingly deployed for myriad high-stakes decisions
that deeply impact people's lives. This is concerning, because not every model can be …

Interpretability of machine learning models is important in critical applications to attain trust
of users. Despite their strong performance, black-box machine learning models often meet …

Trustworthy machine learning and artificial intelligence Page 1 26 feature XRDS • SPRING
2019 • VOL.25 • NO.3 Math Destruction by Cathy O’Neil, catalogs numerous examples of …

Machine learning is becoming increasingly important in today's world. Beyond its powerful
performances, there has been an emerging concern about the trustworthiness of machine …

Developing machine learning models worthy of decision-maker trust is crucial to using
models in practice. Algorithmic transparency tools, such as explainability and uncertainty …

Learning methods such as boosting and deep learning have made ML models harder to
understand and interpret. This puts data scientists and ML developers in the position of often …

Interpretable and explainable machine learning has seen a recent surge of interest. We
focus on safety as a key motivation behind the surge and make the relationship between …

How and when can we depend on machine learning systems to make decisions for human-
being? This is probably the question everybody may (and should) ask before deploying …

Trusting machine learning algorithms requires having confidence in their outputs.
Confidence is typically interpreted in terms of model reliability, where a model is reliable if it …

Trustworthy machine learning requires a high level of interpretability of machine learning
models, yet many models are inherently black-boxes. Training interpretable models instead …