The Institute for Ethical AI & Machine Learning

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THE ML ENGINEER 🤖 Issue #133     This week in Issue #133:   Pitfalls of Causal Inference in XAI 7 Layers of MLOps Security Growing Large Language Models Automated Data Wrangling Clever vs Insightful Code Open Source ML Frameworks Awesome AI Guidelines to check out this week + more 🚀   Forward  email, or share the online version on 🐦 Twitter,  💼 Linkedin and  📕 Facebook!   If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone's support!       Pitfalls of Causal Inference in XAI SHAP Author Scott Lundberg shares an insightful perspective in an article that advocates for best practices in explainable AI and raises awareness of the pitfalls of trying to extract causal insights from modern predictive machine learning models.       7 Layers of MLOps Security A comprehensive article that covers 7 high level themes in security related to MLOps systems. This first article covers the first one which is protecting data, and provides an intuition across multiple phases of the ML lifecycle.       Growing Large Language Models The data exchange podcast dives into conversation with AI Researcher Connor Leathy, and discusses large language models, open datasets for language models, the state of natural language research, and more.       Automated Data Wrangling An interesting article that presents the motivations and tools available to introduce automation on data pre-processing, cleaning, curation, and labelling across open and closed datasets of all types and sizes.       Clever vs Insightful Code A proposed conceptual framework advocating for best practices on programming, differentiating clever vs insightful code, showing snippets that provide an intuition on what can be both a practical and philosophical concept.       OSS: GPU Accel. Frameworks   The topic for this week's featured production machine learning libraries is GPU Acceleration Frameworks. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. The four featured libraries this week are:   Vulkan Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced  data processing usecases. CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it. Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.   If you know of any libraries that are not in the "Awesome MLOps" list, please do give us a heads up or feel free to add a pull request!        OSS: Awesome AI Guidelines   As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following:   AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018 From What to How - An initial review of publicly available AI Ethics Tools, Methods and Research to translate principles into practices   If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!       © 2018 The Institute for Ethical AI & Machine Learning