This video is featured in the AI and UX playlist.
Summary
Artificial intelligence (AI) has graduated from science fiction to commoditized widget form, readily able to snap into many processes of daily life. Hence, enterprises of all maturity levels are increasingly eager to explore AI’s roles in their innovation, or outright survival strategies. Concurrently, ethical and responsible development and execution of AI-based solutions will increasingly become critical for purposes of safety and fairness. Ensuring that AI proliferates along the right path will require the infusion of multi-faceted research activities along the entire AI lifecycle. We will discuss the challenges and opportunities regarding this topic in this presentation.
Key Insights
-
•
70% of enterprise AI projects show little to no business impact, and nearly 90% of data science projects fail to reach production.
-
•
AI bias, particularly intersectional bias in facial recognition, remains a critical unresolved challenge, exemplified by the Gender Shades project.
-
•
Black-box AI decision-making hinders stakeholder trust and adoption, due to AI’s probabilistic nature and complexity.
-
•
AI development teams are overly engineer-centric, lacking inclusion of product researchers and ethicists to address societal and user-centered concerns.
-
•
ML ops, adapted from DevOps, offers governance and accountability frameworks but currently remains engineer-focused.
-
•
Expanding ML ops to include human-centered researchers can improve AI explainability, trustworthiness, and fairness.
-
•
Visualization research is essential to analyze and interpret high-dimensional AI data and uncover hidden biases across intersectional subgroups.
-
•
AI explainability requires moving beyond feature importance towards causal reasoning and natural language explanations accessible to non-technical stakeholders.
-
•
AI trust is evolving and hinges on AI’s ability to provide convincing, interpretable answers that humans can understand and scrutinize in dialogue form.
-
•
Humanizing AI is not simply building human-like interfaces but creating governance frameworks that democratize responsible AI development.
Notable Quotes
"AI development is often uninformed and hurried, resulting in deployments that don’t operate well in the real world."
"Humanizing AI means creating governance frameworks that involve a broad array of research competencies for democratizing safe and effective AI."
"Almost 90% of data science projects do not make it into production—they die on the vine."
"Black box decision making is a hallmark problem—information goes in, something comes out, but we have no clue why."
"Bias is fueled by over-engineering without enough participation from non-technical roles that could reduce it."
"The Gender Shades project exposed how facial recognition algorithms had up to a 33% error rate disparity between demographic groups."
"ML ops offers governance, accountability, and a clear stakeholder responsibility framework borrowed from DevOps."
"We want to increase trust and engagement among end users by helping non-technical stakeholders participate in model evaluation."
"Explainability metrics like trustworthiness and understandability are hard, open research problems needing AI-HCI collaboration."
"AI trust will grow when AI can provide back-and-forth justifications like a human would in conversation."
Or choose a question:
More Videos
"Design automation will reduce armies of workers to maybe just a single operator of the machine."
Erin MaloneUnderstanding the past to prepare for the future
July 19, 2024
"Experimentation gives us the permission to innovate and is in fact an invitation to innovate."
Changying (Z) ZhengNavigating Innovation with Integrity
September 25, 2024
"Good trouble is part of our craft—it's about making the right amount of disruption to improve healthcare."
Carol MassaDesigning Health: Integrating Service Design, Technology, and Strategy to Transform Patient and Clinician Experiences
December 3, 2024
"You can also reach us quickly by going to help-desk-customer-service for any tech or support issues."
Bria AlexanderOpening Remarks
October 3, 2023
"Position your discovery work as de-risking investments rather than just fuel for innovation or empathy building."
Mike OrenWhy Pharmaceutical's Research Model Should Replace Design Thinking
March 28, 2023
"The advantage of having one moderator is I work with all the teams pretty much and can connect the dots across products."
Feleesha SterlingBuilding a Rapid Research Program
May 18, 2023
"I never want to walk into a review with my stakeholders at the SVP level and see something for the first time."
Amy MarquezINVEST: Discussion
June 15, 2018
"You can’t put the toothpaste back in the tube once biased AI harms your brand or users."
Jay BustamanteNavigating the Ethical Frontier: DesignOps Strategies for Responsible AI Innovation
October 2, 2023
"With User Interviews, you can launch a project immediately and get your first qualified participant as fast as three hours."
Lily Aduana Savannah Hobbs Brittany Rutherford5 Reasons to Bring Your Recruiting in-House (and How To Do It)
March 12, 2021
Latest Books All books
Dig deeper with the Rosenbot
What are effective methods for prototyping intangible organizational capabilities without building full systems?
How do newer generations of law students view the incorporation of UX principles into legal education?
What are small steps teams can take to start making meaningful inclusive research without large-scale studies?