Considering The Ethical Responsibilities Of ML And AI Engineers The Machine Learning Podcast

Summary

Machine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are necessary to ensure that this next generation of technical and economic systems are equitable and beneficial for the people that they impact.


Announcements


Hello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.
Your host is Tobias Macey and today I'm interviewing Nicholas Cifuentes-Goodbody about the different elements of the machine learning workflow where ethics need to be considered


Interview


Introduction
How did you get involved in machine learning?
To start with, who is responsible for addressing the ethical concerns around AI?
What are the different ways that AI can have positive or negative outcomes from an ethical perspective?


What is the role of practitioners/individual contributors in the identification and evaluation of ethical impacts of their work?

What are some utilities that are helpful in identifying and addressing bias in training data?
How can practitioners address challenges of equity and accessibility in the delivery of AI products?
What are some of the options for reducing the energy consumption for training and serving AI?
What are the most interesting, innovative, or unexpected ways that you have seen ML teams incorporate ethics into their work?
What are the most interesting, unexpected, or challenging lessons that you have learned while working on ethical implications of ML?
What are some of the resources that you recommend for people who want to invest in their knowledge and application of ethics in the realm of ML?


Contact Info


WorldQuant University's Applied Data Science Lab
LinkedIn


Parting Question


From your perspective, what is the biggest barrier to adoption of machine learning today?


Closing Announcements


Thank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.
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Links


UNESCO Recommendation on the Ethics of Artificial Intelligence
European Union AI Act
How machine learning helps advance access to human rights information
Disinformation, Team Jorge
China, AI, and Human Rights
How China Is Using A.I. to Profile a Minority
Weapons of Math Destruction
Fairlearn
AI Fairness 360
Allen Institute for AI NYT
Allen Institute for AI
Transformers
AI4ALL
WorldQuant University
How to Make Generative AI Greener
Machine Learning Emissions Calculator
Practicing Trustworthy Machine Learning
Energy and Policy Considerations for Deep Learning
Natural Language Processing
Trolley Problem
Protected Classes
fairlearn (scikit-learn)
BERT Model


The intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0
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