Towards a Systems-Level Approach to Fair ML with Sarah M. Brown - #456

episode

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) not yet transcribed

▲ 0

Transcript

This episode hasn't been transcribed yet Upvotes decide what gets transcribed next — 0 so far. Sign in to upvote

Related

recent transcribed audio

Transrapid, Flugtaxis, Hyperloop – Warum es neuartige Verkehrstechnik so schwer hat 28 min · 22 Sep 2026 · Das Wissen | SWR

NPR News: 09-21-2026 8PM EDT 4 min · 22 Sep 2026 · NPR News Now

Alistair Nicholson in the studio! - AFL Coaches Association Senior Coach of the Year award | AFLCA CEO (22.09.26) 11 min · 22 Sep 2026 · Whateley

The Leadership Portfolio with Luke Hodge | Whateley (22.09.26) 21 min · 22 Sep 2026 · Whateley

Who is the 2026 AFL Coach of the Year? | The Leadership Portfolio with Luke Hodge (22.09.26) 1 min · 22 Sep 2026 · Whateley

Brisbane's Grand Final schematic | The Leadership Portfolio with Luke Hodge (22.09.26) 1 min · 22 Sep 2026 · Whateley

Report any issue

Source

Recorded15 Feb 2021

Publisherfeeds.megaphone.fm ↗

Handlerec:12496081

Show notes

Today we’re joined by Sarah Brown, an Assistant Professor of Computer Science at the University of Rhode Island. In our conversation with Sarah, whose research focuses on Fairness in AI, we discuss why a “systems-level” approach is necessary when thinking about ethical and fairness issues in models and algorithms. We also explore Wiggum: a fairness forensics tool, which explores bias and allows for regular auditing of data, as well as her ongoing collaboration with a social psychologist to explore how people perceive ethics and fairness. Finally, we talk through the role of tools in assessing fairness and bias, and the importance of understanding the decisions the tools are making. The complete show notes can be found at twimlai.com/go/456.

Ask this episode

“What did the council decide on item 42?” — answered with the speaker and the moment.

Sign in