Machine Learning and User Experience: A Few Resources

In the past couple years, we’ve seen a huge increase in folks working in the intersection of machine learning and user experience (or data science and design, ai and art, and a ton of different other names!)

It has been a little bit of a challenge to keep track of who is doing what, so I have been keeping a running list of great resources that I’d recommend folks checking out if they are interested in this field.

This medium article will serve as a snapshot in time to some of my favorite resources as of Summer 2019 (and updated on a rolling basis), and organized roughly by the group they came from. These are rough notes and I am trying my best to get these resources out into the community, so apologies for not sharing more detail and going into depth! (And super apologies if I left something out — happy to include it in future iterations!)

Please credit this article if you find any of these sources useful and plan to use them in newsletters, other resource aggregating websites, syllabi, etc. for the future! I’ve worked really hard to find these articles, work with different companies and teams to get them to share these, and do SEO to make these machine-discoverable. “Alone, we can do so little; together, we can do so much” HK — Please help normalize the practice of ML/AI and UX/design by sharing sources (like this list and @mluxmeetup) and helping us all build a community!

Let’s get started!

Machine Learning and UX (MLUX) Meetup Resources

• MLUX meetup (you can see all of our past talks here!)
• MLUX youtube (all of our past recordings!)
• MLUX twitter (@mluxeetup)
• MLUX linkedin company page, community group
• MLUX patreon (support us because all of our events are FREE and we’re trying to get others who might not have access opportunities to speak to our community!)

Companies publishing resources and case studies in the field of ML+UX

Google

• People + AI Research Team (People + AI Research)
• People + AI Guidebook
• Human-Centered ML (medium article by Jess Holbrook and Josh Lovejoy)
• Teachable Machine (Google Creative Labs)
• TripTech Method (CHI Paper 2019)
• Control and Simplicity in the Age of AI (by Gabe Clapper)
• Inclusive ML Guide — AutoML (Google Cloud)

Microsoft

• Human-AI eXperience (HAX) Toolkit (Launched July 2021)
• Guidelines for Human-AI Interaction (medium article)
• Guidelines for Human-AI Interaction (CHI publication by Saleema Amershi et al)
• Human centered AI cheatsheet (medium article by Josh Lovejoy)

Salesforce

• Salesforce Medium Article on their method to data driven personas
• Einstein Designer — AI-Powered, Personalized Design at Scale
• Salesforce talk on Data Driven Personas at MLUX
• Salesforce.org AI4Social Good talk at MLUX

Facebook

• What is the role of an AI Designer? (medium article by Amanda Linden, Director of Product Design at Facebook AI)
• Designing in a World where Machines are Learning (Interaction17 talk by Tony Chu, Product Designer at Facebook AI)

Spotify

• Simultaneous Triangulation: Mixing User Research & Data Science Methods
• Integrating Data Scientists and User Researchers at Spotify
• How Can Design Thinking Build Trust in the Age of Machine Learning?
• Three Principles for Designing ML-Powered Products

Apple

• Machine Learning Human Interface Guidelines
• Designing Great ML Experiences

IDEO

• Inviting Algorithms to the design team
• Data, Ethics, and AI
• What does a Data Scientist do at a Design Company?
• Using Data Science to Design Human Connections
• AI Ethics Tool

Stanford d.school

• Designing Machine Learning course (materials)
• I love Algorithms! cards (medium article)

IBM

• Design for AI Guide
• Trusting AI
• AI Fairness 360 Open Source Toolkit

UX Research for ML and AI

• How UX can (and should!) humanize ML (dscout People Nerds webinar)
• User Research for Machine Learning Systems: A Case Study Walkthrough (dscout People Nerds blog)
• UXR for ML and AI talk at Stitchfix Algo Hour

Interactive walk throughs of Machine Learning

• Tony Chu + Stephanie Yee’s R2D3: A Visual Introduction to Machine Learning (Part 1, Part 2)
• Stitchfix algorithms tour

Other Algorithms/AI + Design

• algorithms.design
• aimeets.design
• AIxDesign

For UX/Designers looking to learn AI/ML

• Elements of AI
• Fast.ai
• Andrew Ng’s Machine Learning Coursera
• Distill.pub

Data Science + Machine Learning Resources

Data Driven Personas: Personas informed by Unsupervised Learning (k-means or hierarchical clustering, PCA, etc)

• Data Driven Personas: Constructing Archetypal Users (Zhang et al 2016 CHI Paper)
• Choosing the right UX Metrics for your product: HEART Framework by Kerry Rodden

Machine Learning Prototyping + AI Art

• ml5.js (great demos here)
• Runway ML
• Wekinator (by Rebecca Fiebrink)
• Machine Learning 4 Artists (ml4a by Gene Kogan)
• AIArtists.org Creative Tools for Generating AI Art
• Simulating Intelligence (by People + AI Research team)

Data Visualization + Exploring your data/examining bias/etc

• Google’s “What if..?” tool
• Google’s Facets tool
• Interactive Confusion Matrix
• Understanding UMAP
• Machine Learning for Visualization by Ian Johnson

AI Ethics + Future of AI/Automation

There is so much out there! But here are a few of my favorites :)

• A People’s Guide to Artificial Intelligence by Mimi Onuoha and Diana Nucera a.k.a. Mother Cyborg
• Kathy Baxter’s (Architect, Ethical AI Practice at Salesforce) numerous posts on medium (“How to Build Ethics into AI” Part 1, Part 2; Women in AI Ethics Summit)
• Building Ethics into AI: Lessons Learned from Pioneers in the Trenches by Kathy Baxter
• The Foundation of Responsible Artificial Intelligence by Susan Etlinger, Prophet/Altimeter Group
• Anna Bethke’s (Head of AI for Social Good at Intel) AI Ethics Toolkits blog post (for more technical nuance between the different toolkits out there)
• Mia Dand’s 100 Brilliant Women in AI Ethics
• Institutions/Research groups: AI Now, Partnership on AI, feminist.ai, Trust, Transparency, and Control Labs
• Conferences/groups at conferences: ACM FAT*, Queer in AI, eyeO
• Nature paper (2019) on Global Landscape of AI Ethics guidelines

UC Berkeley-affiliated groups in AI Ethics, Policy, Data Science accessibility for all space

• Center for Technology, Society & Policy* (twitter)
• Algorithmic Fairness and Opacity working group* (twitter)
• Center for Human-Compatible AI (twitter)
• CITRIS
• Berkeley Center for New Media
• Berkeley Institute for Data Science
• Berkeley d-lab

*= Fellowship sponsor of the Machine Learning and User Experience Meetup