Decision List

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Official documents of Resolutions and Decisions of the Security Council, 2013-2014.

This manuscript is about the sense of justice that limits what individuals can do in pursuit of their ends and opens them to exploitation. It shows how flawed agents choosing under partial information advance those of their ends having nothing to do with justice by maintaining such a disposition.

A developer's knowledge of a computing system's requirements is necessarily imperfect because organizations change. Many requirements lie in the future and are unknowable at the time the system is designed and built. To avoid burdensome maintenance costs developers must therefore rely on a system's ability to change gracefully-its flexibility. Flex