ΑΙhub.org
 

GRACE Podcast: Dr Harriett Jernigan interviews Dr Brandeis Marshall


by
04 July 2022



share this:
grace podcast

GRACE: Global Review of AI Community Ethics is a new student-run, peer-reviewed, open-access, international journal. To accompany the journal, there is a podcast hosted by Dr Harriett Jernigan.

In this first episode, Harriett interviews Dr Brandeis Marshall about her research, ranking algorithms, misinformation, combining the analytical and the creative, the lack of Black women in leadership roles in the data industry, the disproportional effect of data on Black women, tech solutionism, her forthcoming book, and more.

Listen to the audio version below:

You can watch the video version here.

Dr Brandeis Marshall is founder and CEO of DataedX Group, a social impact business that provides learning and development activities on recognizing algorithmic harms and humanizing data practices for data educators, scholars and practitioners. She is also Full Professor of Computer Science at Spelman College. She holds a Ph.D. and Master of Science in Computer Science from Rensselaer Polytechnic Institute and a Bachelor of Science in Computer Science from the University of Rochester. Find out more about her forthcoming book here.

Dr Harriett Jernigan is a lecturer at Stanford University. She earned her BA in German and Creative Writing at the University of Alabama and her PhD in German Studies at Stanford University. She specializes in writing across the disciplines; second-language acquisition; project-based instruction; social geography; and German languages, literatures and cultures.




GRACE




            AIhub is supported by:



Related posts :



Designing value-aligned autonomous vehicles: from moral dilemmas to conflict-sensitive design

  13 Nov 2025
Autonomous systems increasingly face value-laden choices. This blog post introduces the idea of designing “conflict-sensitive” autonomous traffic agents that explicitly recognise, reason about, and act upon competing ethical, legal, and social values.

Learning from failure to tackle extremely hard problems

  12 Nov 2025
This blog post is based on the work "BaNEL: Exploration posteriors for generative modeling using only negative rewards".

How AI can improve storm surge forecasts to help save lives

  10 Nov 2025
Looking at how AI models can help provide more detailed forecasts more quickly.

Rewarding explainability in drug repurposing with knowledge graphs

and   07 Nov 2025
A RL approach that not only predicts which drug-disease pairs might hold promise but also explains why.

AI Song Contest – vote for your favourite

  06 Nov 2025
Voting is open until 9 November.



 

AIhub is supported by:






 












©2025.05 - Association for the Understanding of Artificial Intelligence