Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

T. DukeBuilding Responsible AI Algorithmshttps://doi.org/10.1007/978-1-4842-9306-5_3

3. Data

Toju Duke¹  

(1)

London, UK

 

After setting up and understanding the AI principles, the next foundational element and part of responsible AI development is looking at the data and its guiding principles.

Data is the underlying ingredient of all ML systems. As ML works with loads of data, it’s critical to understand the foundations of data and data ethics, with the primary focus on training and fine-tuning your model appropriately.

The “80 Million Tiny Images” dataset was built by MIT in 2006 and it contains different nouns from Wordnet, a large database for words using ...