Humanizing AI: Filling the Gaps with Multi-faceted Research
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 wrote hypotheses as we believe this pain point matters, here’s how we test it, and how success looks."
Edgar Anzaldua MorenoUsing Research to Determine Unique Value Proposition
March 11, 2021
"Design has to incorporate intellectual humanism—remembering we’re designing for people, with people."
Uday GajendarThe Rise of Meta-Design: A Starter Playbook
May 19, 2022
"We mapped like this for three solid days playing the map forwards, eliminating steps, mitigating risks."
Gordon Ross12 Months of COVID-19 Design and Digital Response with the British Columbia Government
December 8, 2021
"Both tribes really believe in doing awesome for customers, that’s our common ground."
Steve Portigal Chris Chapo Kelly Goto Christian RohrerDiscussion
May 13, 2015
"Sharing war stories helps illustrate the necessity of flexibility and being present in the moment."
Steve Portigal Susan Simon-Daniels Tamara Hale Randolph Duke IIWar Stories LIVE! Q&A-Discussion
March 30, 2020
"My job was to insulate my team from the pressures and not let anyone else do UX work. That was wrong."
Rob Mitzel Sébastien MaloThe Tale of Two Companies: Building a Successful UX Practice in a Century-Old Enterprise
January 8, 2024
"Product managers often see design work as a black box and want transparency and accountability."
Aurobinda Pradhan Shashank DeshpandeIntroduction to Collaborative DesignOps using Cubyts
September 9, 2022
"The term full kit team was coined because you needed the full set of people to produce exceptional experiences."
Carla Casariego Sarah SpencerDesignOps in Wonderland
October 24, 2019
"Make sure you clearly define who your message applies to so your audience can quickly scan and recognize relevance."
Laura Gatewood Laine ProkayBeyond Buzzwords: Adding Heart to Effective Slack Communication
September 23, 2024
