AI

IEEE Bookmarks on Ethical AI Considerations

In this post, you will get to have bookmarks for ethical AI by IEEE (Institute of Electrical and Electronics Engineers) group. Those starting on the journey of ethical AI would find these bookmarks very useful. ML researchers and data scientists would also want to learn about ethical AI practices to apply them while building and testing the models. The following are some bookmarks on ethical AI considerations by IEEE group:

Ethical AI Guiding Principles

The primary goals (guiding principles) laid down by IEEE in relation to ethical AI are the following:

  • Fairness (Human Rights): Idea is to ensure that AI does not end up infringing on internationally recognized human rights.
  • Safety (Well-being): Design considerations should be made keeping in mind the well-being of humans.
  • Accountability: AI designers and developers are responsible for considering AI design, development, decision processes, and outcomes.
  • Transparency: AI-powered solutions operate in a transparent manner.
  • Security (Awareness of misuse): Solutions are secured enough not to be misused.

Who should get involved with Ethical AI

If you are one of the following, you would surely want yourself get up to speed on ethical AI principles:

  • Academic Educators
  • Corporate Board members and C-level executives
  • Government policy makers
Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.

Recent Posts

Coefficient of Variation in Regression Modelling: Example

When building a regression model or performing regression analysis to predict a target variable, understanding…

1 week ago

Chunking Strategies for RAG with Examples

If you've built a "Naive" RAG pipeline, you've probably hit a wall. You've indexed your…

2 weeks ago

RAG Pipeline: 6 Steps for Creating Naive RAG App

If you're starting with large language models, you must have heard of RAG (Retrieval-Augmented Generation).…

2 weeks ago

Python: List Comprehension Explained with Examples

If you've spent any time with Python, you've likely heard the term "Pythonic." It refers…

3 weeks ago

Large Language Models (LLMs): Four Critical Modeling Stages

Large language models (LLMs) have fundamentally transformed our digital landscape, powering everything from chatbots and…

3 months ago

Agentic Workflow Design Patterns Explained with Examples

As Large Language Models (LLMs) evolve into autonomous agents, understanding agentic workflow design patterns has…

4 months ago