Tag: ethics

Become a Data Science Professional

Become a Data Science Professional

Amit Ness gathered an impressive list of learning resources for becoming a data scientist.

It’s great to see that he shares them publicly on his github so that others may follow along.

But beware, this learning guideline covers a multi-year process.

Amit’s personal motto seems to be “Becoming better at data science every day“.

Completing the hyperlinked list below will take you several hundreds days at the least!

Learning Philosophy:

Index

Implementations of Trustworthy and Ethical AI (Report)

Implementations of Trustworthy and Ethical AI (Report)

Want to consider artificial intelligence applications and implementations from an ethical standpoint? Here’s a high-level conceptual view you might like:

Kolja Verhage wrote a report The Implementation of Trustworthy/Ethical AI in the US and Canada in cooperation with the Netherlands Innovation Attaché Network. Based on numerous interviews with AI ethics experts, Kolja presents an overview of approaches and models on how to implement ethical AI.

For over 30 years there has been academic research on ethics and technology. Over the past five years, however, we’ve seen an acceleration in the impact of algorithms on society. This has led both companies
and governments across the world to think about how to govern these algorithms and control their impact on society. The first step of this has been for companies and governments to present abstract high-level principles of what they consider “Ethical AI”.

Kolja Verhage

You can access the report here.

nlintheusa.com/ethical-ai/
Guidelines for Ethical AI

Guidelines for Ethical AI

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.

github.com/EthicalML/awesome-artificial-intelligence-guidelines/
🔍 High Level Frameworks & Principles🔏 Processes & Checklists🔨 Interactive & Practical Tools
📜 Industry standards initiatives📚 Online Courses🤖 Research and Industry Newsletters
⚔ Regulation and Policy
Links to Awesome Artificial Intelligence Guidelines

This overview of ethical guidelines for Artificial Intelligence is by the same author of the repository of Machine Learning production resources shared earlier this year.

Google’s Responsible AI Practices

Google’s Responsible AI Practices

As a company that uses a lot of automation, optimization, and machine learning in their day-to-day business, Google is set on developing AI in a socially responsible way.

Fortunately for us, Google decided to share their principles and best practices for us to read.

Google’s Objectives for AI applications

The details behind the seven objectives below you can find here.

  1. Be socially beneficial.
  2. Avoid creating or reinforcing unfair bias.
  3. Be built and tested for safety.
  4. Be accountable to people.
  5. Incorporate privacy design principles.
  6. Uphold high standards of scientific excellence.
  7. Be made available for uses that accord with these principles.

Moreover, there are several AI technologies that Google will not build:

Google’s best practices for Responsible AI

For the details behind these six best practices, read more here.

  1. Use a Human-centered approach (see also here)
  2. Identify multiple metrics to assess training and monitoring
  3. When possible, directly examine your raw data
  4. Understand the limitations of your dataset and model
  5. Test, Test, Test,
  6. Continue to monitor and update the system after deployment
Best Tech & Programming Talks Ever

Best Tech & Programming Talks Ever

Every now and then, Twitter will offer these golden resources.

Ashley Willis recently asked people to name the best tech talk they’ve ever seen and the results are a resource I don’t want to lose.

Hundreds of people responded, sharing their contenders for the title.

Below, I selected some of the top-rated talks and clustered them accordingly. Click a category to jump to the section.


Big Idea & Programming Meta-Talks

The Future of Programming

Growing a Language

The Mess We’re In

Making Users Awesome

Ethical Dilemmas in Software Engineering


Testing code

Adding Eyes to Your Test Automation Framework

TATFT – Test All The F*cking Time


Language-Specific talks

Concurrency (Python)

How we program multicores (erlang)

Y Not- Adventures in Functional Programming (Ruby)

JavaScript: The Good Parts


Code Design

Core Design Principles for Software Developers

Design Patterns vs Anti pattern in APL


Containers & Kubernetes

The Container Operator’s Manual

Write a Container in Go From Scratch

Container Hacks and Fun Images

Kubernetes and the Path to Serverless

Let’s Build Kubernetes, With a Spreadsheet and Volunteers

Cover image via: https://toggl.com/blog/best-tech-websites

An ABC of Artificial Intelligence Concepts

An ABC of Artificial Intelligence Concepts

Yet another great resource by one of the teams at Google in collaboration with Oxford:

An ABC of Artificial Intelligence-related concepts!

The G is for GANs: Generative Adverserial Networks.

Want to know what GANs are all about?

Just read along with Google’s laymen explanation! Here’s an excerpt:

The P is for Predictions.

Currently the ABC is only available in English, but other language translations come available soon.

Check it out yourself!