Donald Martin discusses working with communities to understand complex societal issues and proactively mitigate machine learning bias.

A look at some of the ways machine learning has the potential to enrich humanities research.

Thryn Shapira discusses balancing user control and automation in AI-powered reminiscing features.

The second edition of PAIR’s AI design guidance adds action-oriented materials to help product teams put theory into practice.

Designed to help researchers and product teams better understand datasets, improve data quality and mitigate bias issues.

A look at PAIR’s papers from CHI 2021. They cover everything from AI’s role in creative endeavors to the human side of data collection.

If “just different enough” were simply a quantitative measure of difference, recommendations would be easy. Alas, it is not.

“What PAIR is reading” is a reading list series compiled by PAIR team members on a rotating basis.

Through new co-creation and speculative design methodologies, the LocAI team explores the role of local context in AI product design.

Will we learn from ML to attach confidence levels to much of what we say? The rise of hashtags gives clues.

Trust researcher Frens Kroeger talks about the need to move from explainability to trust in AI ecosystems.

On Wednesday, November 18, PAIR hosted a virtual symposium focused on boundary objects for participatory machine learning.

Catherine D’Ignazio and Lauren Klein spoke with Fernanda Viégas and Martin Wattenberg about their new book, Data Feminism.

For machine learning, expressing a lack of confidence can be a sign of strength.

Google Research’s Courtney Heldreth and Diana Akrong discuss their work on how AI can help improve the lives of farmers in the Global South

By David Weinberger

PAIR’s latest AI Explorable dives into privacy.

Mat Budelman is a Senior Product Designer at Spotify, where he works on Spotify’s Personalization team.

Here’s what’s piquing the interest of PAIR folks these days.

Sarah Gold is a designer, founder and CEO of Projects by IF. She discusses IF’s manifesto for Society-Centered Design.

How machine learning thinks about its errors turns out to be a handy life skill.