The Business Case for AI Ethics

All Tech is Human Series #7: with Alayna Kennedy and William Griffin

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Details from the original livestream event put on by All Tech is Human

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Action Items & Takeaways

For designers and developers:

1. How to incorporate AI Ethics in the business workflow: ask the following questions and speculate about the impact of your case study on the entirety of humanity. You can utilize the “Hyper Giant Decision Making Framework”:

   1. Is there good will?

   2. Categorical imperative: If every company in every industry in the world used this tech, what would the outcome be? (Stakeholders become the entire world)

   3. Law of humanity: Are people being used as a means to an end (for efficiency, profits, etc..) or are people benefiting from this use case?

2. Use Stakeholder Specific Language for AI Ethics Language:

   1. If you are speaking with developers versus customers versus users, you will likely use different jargon.

   2. Prioritize accessibility about these topics with non-technical stakeholders

3. How to involve diverse communities in the design process:

   1. Even if the intended client does not include members of diverse communities, they still need to be considered in the design -- including point of view and use cases

   2. Remember that the burden of this work does not lie on the members of the selected community

For AI Ethicists:

1. How to get buy-in from within the organization:

   1. Find ways to utilize AI Ethics to grow your company’s bottom line

   2. Explain that incorporating ethics and ethically aligned design into the product life cycle has been proven to lead to increased user retention

   3. Explain that asking ethical questions and tackling ethical challenges allows for more questions to be asked and fosters creativity and innovation

   4. Explain that ethics translates to monetary gain and trust towards an organization

2. How to incorporate AI Ethics into a product that has already been deployed or is in late life-cycle stage:

   1. Utilize continuous integration, like “ethically aligned design” frameworks

      1. Continuous evaluation and assessment of risk throughout all iterations of the product, and throughout all stages of the life cycle

3. How to get the public informed about AI Ethics:

   1. Advocates need to come out and explain how technologies are being used, and how they work, to the general public (public scholarship and journalism)

   2. Once these topics are explained, steps need to be laid out to the general public telling them what they can do -- especially if they choose to not participate in the technology after being informed

For K-12 Teachers:

1. How to teach AI Ethics to K-12 Students:

   1. Take a look at the resources on K-12 education below!

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Annotated Resources

Resources From Alayna Kennedy (guest speaker from this episode):

1. Twitter: @Alayna__Kennedy

2. Personal Website

Resources From William Griffin (guest speaker from this episode):

1. Twitter: @WillGriffin1of1

2. Hypergiant

Relevant Media, Links, and Publications

1. The Business Case for AI Ethics: Moving From Theory to Action (mentioned by David)

2. More Than Meets AI Report (mentioned by Alayna)

3. Ethically Aligned Design (for integration into the product lifecycle)

4. Big tech companies back away from selling facial recognition to police. That’s progress. (mentioned by William)

5. Coded Bias (mentioned by David)

6. Behind the Paper That Led to a Google Researcher’s Firing

7. Hypergiant on Twitter

8. Working Group on Responsible AI Certification Mark, contributors:

   1. World Economic Forum

   2. AI Global

   3. Schwartz Reisman Institute

9. IBM AI Ethics Board

K-12 and Beyond AI Ethics Resources

1. EthicalCS (K-12)

2. An Ethics of Artificial Intelligence Curriculum for Middle School Students by MIT Media Lab (K-12)

3. What Do We Teach When We Teach Tech Ethics? A Syllabi Analysis by Casey Fiesler et. al

4. More Than "If Time Allows": The Role of Ethics in AI Education by Natalie Garrett et. al

5. Teaching Ethics with Coding resources and in-class assignments / activities:

   1. Ethically Aligned CS 1 Course by Evan Peck

   2. AI, Justice, Imagination: Technology and Speculative Fiction course syllabus by Janet Zong York and Jonathan Zong

   3. Data Intro Course on GitHub by Sorelle Friedler

   4. Process Oriented Guided Inquiry Learning in Computer Science by Chris Mayfield

   5. Fairness and Machine Learning by Solon Barocas, Moritz Hardt, and Arvind Narayanan

   6. Ethics and Social Sciences in Intro Python Courses (5 modules) by Jessie J. Smith

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Do you have any more action items or takeaways that you’d like to share that are related to this topic? Any resources you’d like to include in this list? We’d love to hear from you! Feel free to leave your thoughts in a comment below (you can post your comment by hovering over the bottom-right corner of the comment box).