Learn About AI Bias and Taking Action
An AI tool doesn’t announce its bias. It just changes who gets the loan or the interview call. You already rely on these tools daily, and closing the distance between what they claim and what they change is where accountability starts.
Bring examples from your work to a discussion that weighs what you’ve seen against what the AI Bias cohort has documented across healthcare and hiring.
You’ll come away with the Non-Profit and Individual Toolkits and the videos the cohort released, ready for your team to use.
Grab the AI Bias Toolkits Here
Non-Profit ToolkitGet the Nonprofit Toolkit
Individual ToolkitGet the Individual ToolkitMeet the Thought Leaders Behind the AI Bias Toolkits
Audrey Schaub
Read Audrey’s bio
Audrey Schaub is a fractional COO with 20+ years leading enterprise transformation across multiple industries. For She Leads AI’s AI Bias governance cohort, she drove development of multiple toolkits and supporting materials designed to make bias literacy accessible to every audience, regardless of technical background.
Carmela Pereira
“And I’m like, no, people do evil things with it. That doesn’t mean it’s evil.”
Read Carmela’s bio
Carmela Pereira is a Strategic Integrator and AI Governance practitioner who specializes in bridging human intuition and artificial intelligence. Drawing on a diverse background spanning design systems, branding, and the hospitality industry, she brings a centered, intentional approach to AI adoption — one that keeps human values and ethical governance at the foundation of technological progress. For the AI Bias cohort, Carmela brings strategic clarity to how the work is framed and shared. Her MVP/Agile approach has shaped the cohort’s momentum — keeping the team moving forward with intention and the work directional, not a destination.
Jasmine Schwarz
“We must keep the information accessible, or we will be the bias.”
Read Jasmine’s bio
Jasmine Schwarz is an AI governance consultant and founder of Schwarz Strategic Services. She helps mission-driven organizations and small businesses adopt AI with a clear view of the risk, the people affected, and how their teams really operate day to day. She came to this work through healthcare, finance, and nonprofit leadership, and she brings structure to the places where oversight usually falls behind: who holds decision authority, where the exposure points are, what documentation a team can realistically keep up with, and whether implementation matches what leadership intended. She has seen what happens when governance fails. The harm rarely shows up all at once. It builds over time. Her guiding principle for the AI Bias cohort is simple. If the information isn’t accessible, the system itself becomes the bias.
Tomar Beh
“We are envisioning toolkits for different communities.”
Read Tomar’s bio
Tomar Beh contributed to the development of the AI Bias cohort’s not-for-profit toolkit. She also helps organisations understand and adopt AI responsibly, with a focus on practical implementation, AI literacy and governance. She is the author of Start AI Right for Nonprofits, a practical guide supporting nonprofit and creative leaders to move from AI confusion to clarity. Her work brings a mission-driven perspective to responsible AI, including bias, governance, accessibility and how organisations introduce AI in ways that work for their teams and communities.
Kim Olpin
“Bias in AI tools is not a problem that gets solved once. The tools change, the bias shifts, and new patterns emerge.”
Read Kim’s bio
Kim Olpin runs Jericho Road Pasadena, an organization that supports other nonprofits by matching skilled volunteers, training leaders, and building peer support networks. She knows nonprofit operations from the inside, and she trains local nonprofit leaders on AI, meeting them where they are, whether they’re terrified of it or pretending they already get it. For the AI Bias cohort, Kim created the nonprofit toolkit.
A Case Study From the Nonprofit Toolkit
From AI Bias, A Practical Guide for Nonprofits, developed by Kim Olpin with Tomar Beh
Benefits Intake
Research at the 2025 ACM Conference on Fairness, Accountability, and Transparency documented AI-assisted intake and eligibility systems across US safety-net organizations that denied eligible community members access to services before a human ever reviewed their case.
The Harm
For people applying for food assistance, housing support, or other critical resources, a wrongful denial is not a paperwork problem. In documented cases, the AI replicated the gatekeeping behavior of the bureaucracies it was meant to replace, and did so faster and with less possibility for the human judgment that sometimes makes exceptions.
What Could Have Helped
- Build a clear path to human review into the intake process before any AI-assisted tool goes live, not after a problem surfaces
- Tell people clearly when AI is involved in their intake process and give them a way to request human review of their case
- Pilot new intake tools before full deployment, testing denial rate patterns by income, race, and primary language
A process that moves faster may be turning people away faster. If AI is involved in any part of how community members enter your programs, you need a human in the loop.
A Case Study From the Everyday Users Toolkit
From AI Bias, A Practical Guide for Everyday Users
Your Résumé
In 2024, University of Washington researchers ran over three million comparisons of 550+ real résumés against 500+ job listings across three commercial language models. The only variable they changed was the applicant’s name at the top. The models preferred white names over Black names 85% vs. 9%, and male names over female names 52% vs. 11%. They never once favored a Black male candidate over an identical white male candidate.
The Harm
A qualified person is filtered out before any human sees the application. There is no rejection letter to explain why, no way to know it happened, and nothing to appeal. The applicant never hears back and concludes they were not good enough.
What Could Have Helped
- Employers testing screening tools across names and publishing what they find
- Disclosure at the point of application that automated screening is in use
- A stated route to request human review of an automated rejection
- Human review of the rejection pile, not only the shortlist
Assume software screens your résumé first. Use exact keywords from the posting and keep your formatting simple. Reach out directly to a recruiter, connection, or hiring manager whenever you can. Bypassing the automated filter is your single best advantage.
Join Us on Saturday, October 24, for Recognizing AI Bias and Taking Action
10am–noon Pacific • On Zoom
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