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
"We created a self-service UXR hub to empower teammates with research fundamentals amid downsized research teams."
Maggie DieringerCreating Consistency Through Constant Change
January 8, 2024
"Providing alternative content is often a kinder gesture than any lengthy alternative text could be."
Abby CovertStuck? Diagrams Help
October 27, 2022
"Impact curated by France Alana was outstanding."
Bria AlexanderDay 3 Welcome
September 25, 2024
"Women’s empowerment is often glorified but doesn’t necessarily meet their needs beneath the surface."
Mansi GuptaDrawing from Feminist Practice to Make Inclusive Design Operational
September 9, 2022
"It’s not like you can go down to the corner store, pick out the right brand of design ops, and sprinkle it everywhere."
Dan WillisFilling the Void
November 7, 2018
"Conflict actually strengthens relationships if we choose to engage with it."
Laura WeissTurn Down the Heat: 3 Ways to Handle Conflict in the Moment
November 20, 2024
"With Qubits, you can create a design roadmap in half a minute aligned to your preferred design process."
Aurobinda Pradhan Shashank DeshpandeIntroduction to Collaborative DesignOps using Cubyts
September 8, 2022
"As a civic designer, it’s really up to you to hold managers accountable for their promises on inclusion."
Yalenka Mariën Marie MervaillieDesigning for Digital Inclusion in the Belgian Government
December 8, 2021
"Working on a different word than empower: People already have power, we have to respect it."
Bria Alexander Ariel Kennan Charlotte Lee Sarah Brooks Emily Lessard Gordon Ross Joanne DongReflect and Chart Forward
December 10, 2021
