What are the important questions to ask when developing or using AI? Long views on AI, Part 2
What does fairness in AI mean, and is it relevant in your use case? This question is posed by Beena Ammanath. Ammanath is the Global Head of the Deloitte AI Institute, Founder of Humans For AI and Board Member of AnitaB.org, as well as an author. I’ve had the pleasure of conversing with her a couple […]
Read More →What is the right balance between innovation and regulation in AI? Long views on AI, Part 1
What AI model maker would take the risk to make any powerful model if they could be responsible for anything someone might possibly do with it? This question is posed by Dan Jeffries – author, futurist, engineer, and systems architect. I’ve had the pleasure of conversing with him a couple of times. First about the […]
Read More →The EU AI Act could help get to Trustworthy AI, according to the Mozilla Foundation
The EU AI Act may turn out to be one of the most influential legal frameworks in the world. Here is how, and why, the Mozilla Foundation is engaging with it, and how you can get involved too
Read More →Trustworthy AI: How to ensure trust and ethics in AI
A pragmatic and direct approach to ethics and trust in artificial intelligence (AI) — who would not want that? This is how Beena Ammanath describes her new book, Trustworthy AI.
Read More →The state of AI ethics: The principles, the tools, the regulations
What do we talk about when we talk about AI ethics? Just like AI itself, definitions for AI ethics seem to abound. A definition that seems to have garnered some consensus is that AI ethics is a system of moral principles and techniques intended to inform the development and responsible use of artificial intelligence technologies.
Read More →AI ethics in the real world: FTC commissioner shows a path toward economic justice
FTC Commissioner Rebecca Kelly Slaughter lays out machine learning and AI's potential for harm and outlines some ways for the FTC to counter it.
Read More →The state of AI in 2020: Biology and healthcare’s AI moment, ethics, predictions, and graph neural networks
Research and industry breakthroughs, ethics, and predictions. This is what AI looks like today, and what it's likely to look like tomorrow.
Read More →Explainable AI: From the peak of inflated expectations to the pitfalls of interpreting machine learning models
We have reached peak hype for explainable AI. But what does this actually mean, and what will it take to get there?
Read More →Explainable AI: A guide for making black box machine learning models explainable
In the future, AI will explain itself, and interpretability could boost machine intelligence research. Getting started with the basics is a good way to get there, and Christoph Molnar's book is a good place to start.
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