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Issue #77
This week in Issue #77:
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If you would like to suggest articles, ideas, papers, libraries, jobs, events or provide feedback just hit reply or send us an email to! We have received a lot of great suggestions in the past, thank you very much for everyone's support!
"Made with ML (MWML)" is a fantastic free platform that focuses on enabling the ML community to learn, explore and built, through a set of curated resources, ML related lessons, a continuously updated set of ML projects, and more. Check it out and do make sure to add any projects / resources that are not listed already.
The Data Exchange podcast comes back with a fantastic conversation with Immuta Chief Legal Officer and BNH AI Managing Partner Andrew Burt. This podcast dives into core components of machine learning model governance, specifically from a legal professional perspective, diving into the intersection between these two fields, covering best practices and challenges of identifying and mitigating risks, as well as incident response and recovery in ML.
The Association for Computing Machinery has released their first ByteCast podcast, kicking off with a fantastic conversation with Computer Science Legend Donald Knuth, largely known for his book, "The Art of Computer Programming". In this podcast they discuss what led him to discover his love for computer science, as well as his outlook on how people learn technical skills, and how his mentorship has helped him write "human oriented" programs.
Following our post last week covering GPT-3, this week Microsoft comes with a very important topic, publishing a paper that covers the analysis of 146 NLP bias research papers. In this paper they dive into the issues and impact in some of this bias, as well as best practices required in the research field to ensure some of these undesired biases are identified and mitigated.
Machine Learning mastery has put together a great overview of an important sub-topic in feature selection. Namely this is feature selection with numerical or continuous input data. In this post they cover a hands on example using a diabetes prediction dataset, showcasing the challenges found in conitnuous inputs in the context of binary classification, and they teach how to evaluate the importance of numerical features using the ANOVA f-test and mutual information statistics.
[Updated List 07/06/2020] Due to the current global situation, a large number of conferences have had to face hard choices, several which decided going fully virtual. This hard choice has now open the doors to people from around the world to gain access to the great online content generated by expert speakers and contributors. We wanted to highlight some of these key conferences so they are not missed - these include:
Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at
The topic for this week's featured production machine learning libraries is Privacy Preserving ML. 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:
  • Substra - Substra is an open-source framework for privacy-preserving, traceable and collaborative Machine Learning.
  • Tensorflow Privacy - A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.
  • TF Encrypted - A Framework for Confidential Machine Learning on Encrypted Data in TensorFlow.
  • Uber SQL Differencial Privacy - Uber's open source framework that enforces differential privacy for general-purpose SQL queries.
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
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 thiese 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. We will be showcasingitg three resources from our list so we can check them out every week. This week's resources are:
  • 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