A collection of useful Slides & Quotes on AI-Ethics and XAI

Some basic information on AI Ethics & Algorithmic Bias:

The ethics of artificial intelligence is part of the ethics of technology specific to robots and other artificially intelligent beings. It is typically divided into robo-ethics, a concern with the moral behavior of humans as they design, construct, use and treat artificially intelligent beings, and machine ethics, which is concerned with the moral behavior of artificial moral agents (AMAs). (more info)

Algorithmic bias

Algorithmic bias describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others. Bias can emerge due to many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected, or used to train the algorithm. Algorithmic bias is found across platforms, including but not limited to search engine results and social media platforms, and can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. (more info)

IEEE standard

Currently, a new IEEE standard is being drafted that aims to specify methodologies which help creators of algorithms eliminate issues of bias and articulate transparency (i.e. to authorities or end users) about the function and possible effects of their algorithms. The project was approved February 2017 and is sponsored by the Software & Systems Engineering Standards Committee, a committee chartered by the IEEE Computer Society. A draft of the standard is expected to be submitted for balloting in June 2019. (more info on IEEE)

According to the motto: “A picture says more than a thousand words” some useful slides & quotes below.

Five ethical challenges of Artificial Intelligence

Food for Thoughts

12 steps to put AI-Ethics into practice

Psychological Model of Explanation (IHMC)

Development of Nuclear Energy vs. Artificial Intelligence

A brief overview of Challenges of Explainability

A bit of AI Dystopia ;-)

A simplified Responsible Machine Learning Workflow

5 Themes to Consider for Ethical Data Science

There is still much to be done. No retreat, no surrender!

An overview of model agnostic, local, and post hoc Interpretability Methods

Interpretability vs. Accuracy

Main ethical and moral issues associated with the development and implementation of AI

The Ethical Workflow (by Dr. David Leslie: turing.ac.uk)

Requirements of Trustworthy AI (According to the European Ethics Guidelines for Trustworthy AI)

Types of Algorithmic Bias

Popular Machine Learning Algorithms and Taxonomy

9 ethical issues in Artificial Intelligence

A silent warning

Responsible Machine Learning Workflow

Further development of the workflow from the paper: https://www.mdpi.com/2078-2489/11/3/137/pdf (mdpi)

5 Variations of Artificial Intelligence (Article: 5 Variation of Artificial Intelligence)

Threat to human dignity

Artificial Intelligence: 4 Ethical Implications

I hope it contains some useful and above all inspiring content.

This might be also of interest (my latest book):

Available at Amazon: THE AI THOUGHT BOOK: Inspirational Thoughts & Quotes on Artificial Intelligence

Murat