The MachineLearning EngineerIssue #77

This week in Issue #77:

• Made with Machine Learning Platform
• Identifying & Mitigating AI Risks
• ACM ByteCast with Donald Knuth
• Microsoft NLP Bias Research
• Feature Selection with Continuous Data
• [Updated] AI Conferences Gone Virtual in 2020
• Featured OSS Production ML Libraries
• Awesome AI Guidelines to check out this week
• + more 🚀

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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 a@ethical.institute! We have received a lot of great suggestions in the past, thank you very much for everyone’s support!

Made with ML Platform

“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.

Identifying & Mitigating AI Risks

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.

ACM ByteCast with Donald Knuth

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.

Microsoft NLP Bias Research

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.

Feature Selection with Cont. Data

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.

AI Conferences Gone Virtual 2020

[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:

• Pulsar Summit Virtual Conference - June 17th 2020
• Virtual Federated & Distributed ML Conf - June 18th
• Databricks Applied AI summit - June 22nd
• ICML Online - July 12th
• EuroPython 2020 - July 23rd

Did we miss any? Please let us know by replying to the newsletter email or by simply emailing us at a@ethical.institute

OSS: Privacy Preserving ML

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!

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 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:

• 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!