As AI systems become more prevalent in society, we face bigger and tougher societal challenges. Given many of these challenges have not been faced before, practitioners will face scenarios that will require dealing with hard ethical and societal questions.
There has been a large amount of content published which attempts to address these issues through “Principles”, “Ethics Frameworks”, “Checklists” and beyond. However navigating the broad number of resources is not easy.
This repository aims to simplify this by mapping the ecosystem of guidelines, principles, codes of ethics, standards and regulation being put in place around artificial intelligence.
The repository consists of tools for multiple languages (R, Python, Matlab, Java) and resources in the form of:
Books & Academic Papers
Online Courses and Videos
Algorithms and Applications
Open-source and Commercial Libraries/Toolkits
Key Conferences & Journals
Outlier Detection (also known as Anomaly Detection) is an exciting yet challenging field, which aims to identify outlying objects that are deviant from the general data distribution. Outlier detection has been proven critical in many fields, such as credit card fraud analytics, network intrusion detection, and mechanical unit defect detection.
This overview is curated in the sense that all resources are rated by CourseDuck’s users. These ratings seem quite reliable, at least, I personally enjoyed their top-3 resources sometime the past years: