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More Intelligent Tomorrow

Bringing History and Foresight to Ethical AI

MIT_MegMitchell_1024x1024 Michael Gilday
Margaret Mitchell, Michael Gilday
S02 E06 · March 2, 2022

Dr. Margaret Mitchell (Meg) is a researcher in ethics-informed AI with a focus on natural language generation, computer vision, and other augmentative and assistive technologies.

In this episode of More Intelligent Tomorrow, Meg talks to Michael Gilday about the opportunities in machine learning to create a more diverse, equitable, and inclusive future.

Foresight in AI

Foresight is an indispensable tool for shaping and evaluating AI project outcomes. Instead of focusing on creating technology to improve something that already exists, a longer-term focus –one that is two, five, or ten years into the future–can help us understand what we should be working on today. It’s a fairly straightforward way of thinking, yet foresight is often brushed aside as incalculable.

Foresight also can present a liability issue. If you’re working on a technology that will be less discriminatory, for example, that means your technology right now is discriminatory. Fear of impending regulation and of  misinterpretations that hamper development can cause a troubling lack of imagination within development teams.

Embracing Diverse Perspectives

Bringing in people who have a creative mindset or a different perspective can help technical teams see things in a more imaginative way. Science fiction writers, for example, are adept at bringing foresight into a project and help teams see how things might evolve over time. That, in turn, could help us be smarter about the kinds of development we do. But bringing people who are adept at foresight into a project, such as science fiction writers, creates an opportunity to think through how things might evolve over time. That, in turn, could help us be smarter about the kinds of development we do.

Similarly, historians can shed light on patterns of development over time. Instead of focusing on  how rapidly technology is changing, they can offer a reflection on corresponding power dynamics and sociological changes that can also inform how we develop a technology.

A collaboration of humanities-oriented thinkers and science-oriented thinkers can help us think through the storyline of what a technology should be. There’s a need to focus not only on how well the model or system works in isolation but also how well it works in context.

Understanding how people use a technology–and therefore understanding people– is not something computer scientists are always good at. It requires different skill sets, which makes collaboration with subject matter experts critical.”

To really understand what it means to have AI in our social contexts, we need social scientists, anthropologists, and historians. So, how do we bring a diversity of voices and experiences into these technological challenges and conversations?

People will generally align to work that is incentivized: awards and grants, bases for promotions, and job expectations. For example, if you want to incentivize inclusion, performance reviews and promotions should be based upon how an employee incorporates diverse perspectives into their work.

Now is a great time to focus our attention on the science of diversity and inclusion. We’re on a global scale we haven’t been able to see before. We have infinitely better access to different cultures and perspectives on differences and similarities like we’ve never had before.”

Listen to this episode of More Intelligent Tomorrow to learn about:

    • The culture of ethical behavior and the bottom-up-top-down approach with regulators and corporations
    • How no-code solutions are removing barriers in AI and machine learning work
    • Malicious actors vs. irresponsible ones and why ignorance is the biggest problem we face
    • Gender bias and progress bringing more women into in tech and STEM
    • How transparency can be prioritized over the obfuscation that is prevalent right now
Margaret Mitchell
AI Research Scientist
Michael Gilday
Michael Gilday
VP, Storytelling & Creative - DataRobot
AI Bias, AI Ethics
[Infographic] The State of AI Bias — and How Companies Are Addressing It
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Tags: AI bias AI ethics AI regulation ai trust Artificial Intelligence behavioral science bias conversational AI data science deep learning ethics innovation More Intelligent Tomorrow NLP Season 2

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