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Agile AI Ethics: Balancing Short Term Value with Long Term Ethical Outcomes // Pamela Jasper // MLOps Meetup #51

Agile AI Ethics: Balancing Short Term Value with Long Term Ethical Outcomes // Pamela Jasper // MLOps Meetup #51

FromMLOps.community


Agile AI Ethics: Balancing Short Term Value with Long Term Ethical Outcomes // Pamela Jasper // MLOps Meetup #51

FromMLOps.community

ratings:
Length:
67 minutes
Released:
Feb 12, 2021
Format:
Podcast episode

Description

MLOps community meetup #51! Last Wednesday we talked to Pamela Jasper, AI Ethicist, Founder, Jasper Consulting Inc.

// Abstract:
One of the challenges to the widespread adoption of AI Ethics is not only its integration with MLOps, but the added processes to embed ethical principles will slow and impede Innovation. I will discuss ways in which DS and ML teams can adopt Agile practices for Responsible AI.

// Bio:
Pamela M. Jasper, PMP is a global financial services technology leader with over 30 years of experience developing front-office capital markets trading and quantitative risk management systems for investment banks and exchanges in NY, Tokyo, London, and Frankfurt. Pamela developed a proprietary Credit Derivative trading system for Deutsche Bank and a quantitative market risk VaR system for Nomura. Pamela is the CEO of Jasper Consulting Inc, a consulting firm through which she provides advisory and audit services for AI Ethics governance. Based on her experience as a software developer, auditor and model risk program manager, Pamela created an AI Ethics governance framework called FAIR – Framework for AI Risk which was presented at the NeurIPS 2020 AI conference. Pamela is available as an Advisor, Auditor and Keynote Speaker on AI Ethics Governance. She is a member of BlackInAI, The Professional Risk Managers Industry Association, Global Association of Risk Managers and ForHumanity.

//Takeaways
Agile methods of adopting AI Ethical processes.

----------- Connect With Us ✌️-------------   
Join our Slack community:  https://go.mlops.community/slack
Follow us on Twitter:  @mlopscommunity
Sign up for the next meetup:  https://go.mlops.community/register

Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Pamela on LinkedIn: https://www.linkedin.com/in/pamela-michelle-j-a5a3a914/

Timestamps:
[00:00] Introduction to Pamela Jasper
[00:17] Pamela's Background
[05:45] Agile IA/Agile Machine Learning, If they are the right fit for each other?
[07:50] What is agile? Not necessarily in and of itself a hard-coded framework.
[08:05] Agile itself based on May 2001 Manifesto is simply a set of values and principles and teams that make decisions around these values and principles.
[10:17] Proposal of Pamela: Let's do Agile with the underlying Ethics that are involved in the ways that you're creating this machine learning. Is that correct?
[10:28] "What I'm suggesting is that Ethics become baked into almost to the mindset of a machine learning engineer, data scientists and in the machine learning operational process for MLOps."
[14:37] "Not all models are created equal"
[15:59] How would be in an Agile way put into practice in your mind?
[36:38] What are the things that would help bridge the gap between AI Ethics and the Agile?
[41:01] It's not that you're trying to bring on the Agile framework to the different pieces of Ethics. It's that you're bringing that into the Agile framework?
[41:21] "We're weaving Ethics into the bedrock of existing Machine Learning practices."
[45:13] How can you really get a diverse team if you're not hiring someone who's there as a diverse person?
[48:59] What would Epics look like if you're baking Ethics?
[52:52] How do you apply Ethics to an ethically questionable domain like gambling?
[54:42] "I think that we can create an AI app for gambling is legal that becomes legal in that construct."
[56:23] Do you think it's possible/desirable to automate any of the ethical considerations in this way?
Released:
Feb 12, 2021
Format:
Podcast episode

Titles in the series (100)

Weekly talks and fireside chats about everything that has to do with the new space emerging around DevOps for Machine Learning aka MLOps aka Machine Learning Operations.