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AI: Passionate defenses and reasoned critique [Advancing Research Community Workshop Series]
Wednesday, September 18, 2024 • Advancing Research Community
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AI: Passionate defenses and reasoned critique [Advancing Research Community Workshop Series]
Speakers: Rachael Dietkus, LCSW , Llewyn Paine , Nishanshi Shukla and David Womack
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Summary

AI adoption is rapidly accelerating in the insights space, and researchers are rushing to explore the possibilities and pitfalls it presents. Without a doubt, it will change the nature of our work, but where do we stand now? Our panelists will examine passionate defenses for the value of AI, offer reasoned critiques, discuss practical applications, and discuss how we can collectively move forward in an ethical and human-centered manner. Attend all of our Advancing Research community workshops Each free virtual workshop is made up of panelists who will share short provocations on engaging ideas to discuss as a group, as well as a leader in our field to moderate. If you're looking for discussions that challenge the status quo and can truly advance research, look no further than our workshop series. (P.S. We’ll be drawing most of our Advancing Research 2025 conference speakers from those who present at upcoming workshops—so tune in for a sneak peek of what's to come from #AR2025!) July 24, 4-5pm EDT Watch Video Theme 1: Democratization Working with it, not against August 7, 11am-12pm EDT Watch Video Theme 2: Collaboration Learning from market research, data science, customer experience, and more August 21, 4-5pm EDT Watch Video Theme 3: Communication Innovative techniques for making your voice heard September 4, 11am-12pm EDT Watch Video Theme 4: Methods Expanding the UXR toolkit beyond interviews October 2, 11am-12pm EDT Watch Video Theme 6: Junctures for UXR Possible futures and the critical decisions to move us forward October 16, 4-5pm EDT Watch Video Theme 7: Open Call Propose ideas that don’t match our other workshops’ themes

Key Insights

  • AI is essential for modeling complex natural systems that exceed human cognitive capacity, such as soil and water ecosystems, enabling new scientific breakthroughs.

  • Large language models tend to average results, ignoring edge cases where true innovation and scientific progress often occur.

  • AI systems frequently reflect and reinforce dominant cultural and social biases, marginalizing subaltern groups and creating life-impacting injustices.

  • A critical question when designing AI is who is missing from the conversation and who will be impacted, ensuring inclusivity beyond tokenism.

  • Ethics and trauma-informed practices must be central to AI design to prevent unintentional harm and support responsible technology deployment.

  • Bias is pervasive in AI and data sets, but increasing interdisciplinary and diverse participation can reduce its impact.

  • AI’s environmental costs are significant and must be weighed against its potential benefits, particularly in climate-related problem solving.

  • Human-to-human interaction remains irreplaceable; AI should augment rather than replace social or empathetic roles.

  • Public discourse on AI is often dominated by fear and negativity; sharing positive and responsible use cases could balance the narrative.

  • AI systems evolve as organic ecosystems, requiring ongoing monitoring and adjustment to mitigate bias and power imbalances continuously.

Notable Quotes

"AI is both absolutely necessary and completely terrifying for science."

"Science advances not from the center, but along those edges and on the margins."

"AI creates a virtual worldview where counter-views do not exist, causing life-threatening problems for marginalized groups."

"Who was involved in the process, who benefited, and who was harmed—these are critical questions from design justice."

"Nothing is inherently better because it was produced via human or machine learning; we must interrogate the goal first."

"AI is making seeing multiplicities possible; it’s not just about a single thing anymore."

"The emotional cost and burden of creating and using AI tools are often overlooked in cost-benefit analyses."

"All design has costs; AI is not unique in this respect."

"Bias is constantly evolving; new ones are emerging that we might not even be aware of."

"AI systems are organic and evolving; we must tend to them constantly to keep bias and harms at bay."

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