AI Bias & Algorithmic Justice: A Working Session

A facilitated working session for practitioners engaged in algorithmic-fairness, algorithmic-audit, and algorithmic-justice work. The session combines a one-hour structured-audit case-study analysis with a thirty-minute structured share-back across attendee work.

The session is led by Dr. Joy Buolamwini and her Algorithmic Justice League team. The structured-audit case-study analysis walks through one specific recent audit — selected from facial-analysis, hiring algorithms, credit, or healthcare contexts depending on the cohort interest — with the methodological decisions exposed in detail. The case-study covers the research-question selection, the data-collection approach, the analysis methodology, the findings-presentation work, and the post-publication advocacy and policy engagement.

The structured share-back invites attendees to briefly present their own current work — an audit in progress, a fairness research project, a policy-engagement initiative, an advocacy campaign — in two-to-three-minute blocks with brief facilitated feedback from the AJL team and from peer attendees. The share-back is rapid by design; the goal is to expose the cohort to a range of current work and to surface specific patterns and challenges across the field.

The session structure is 90 minutes total. The first 60 minutes is the case-study analysis. The final 30 minutes is the structured share-back. A pre-event briefing document distributes one week prior to give attendees the case-study framing and prepare them for the share-back format.

Who this is for: verified practitioners in algorithmic-fairness research, algorithmic-audit work, AI ethics, AI policy, and adjacent fields. Graduate students and post-doctoral fellows in computer science, human-computer interaction, and adjacent fields working on algorithmic-fairness research. Professionals at the intersection of technology and policy. Founders and senior staff at algorithmic-justice and responsible-AI organizations. The session is limited to verified practitioners to preserve the working-session quality of the discussion.

What attendees will leave with: a detailed case-study analysis of the audit selected for the session, with full methodological specifics. Exposure to a range of current work across the field through the share-back. Specific contacts across the cohort for follow-up collaboration conversations. Continuing access to the AI-and-algorithmic-justice subgroup threads within the African Women in Tech Leadership network and the Academia and Research network.

Logistics: virtual via the platform's event infrastructure. 90-minute duration. Capacity 100, limited to verified practitioners; registered count tracked at the platform-event-detail level. Recording available to registered attendees only and embargoed from broader sharing in keeping with the working-session orientation.

Pre-event prep: a structured pre-event briefing document distributes one week prior. The briefing covers the case-study framing and the share-back format expectations. Attendees who wish to participate in the share-back submit a one-paragraph description of their current work at registration; the AJL team curates the share-back order across the submissions.

Post-event continuation: a follow-up thread in the African Women in Tech Leadership network and the Academia and Research network for attendees in active algorithmic-justice work. Individual mentor sessions with Joy are available through her mentor profile for attendees with extended follow-up questions.

Cost: tiered ticket pricing reflecting the session's specialized practitioner orientation and the structural infrastructure behind it. Scholarships are available for attendees for whom the ticket cost is a barrier; the request is at the bottom of the registration form.

This session has run multiple times across the platform's events program with consistent registration demand. The session structure is replicable and the broader platform infrastructure supports running additional sessions on different case-study topics as the algorithmic-justice field continues to develop.

The platform's broader algorithmic-justice and AI-ethics infrastructure supports this session series. The AI Bias and Algorithmic Justice subgroup runs ongoing discussion threads between session events, and senior researcher members rotate hosting subsequent sessions on different case-study topics. The session structure has produced specific cross-organization collaborations among attendees that have continued across years.

The algorithmic-justice working-session conversation has historically been carried in academic-conference and trade-conference contexts that do not center the specific operational practitioner work the field actually requires. The platform's working-session format addresses the gap and provides the structured engagement that the cohort of practitioners has surfaced as specifically useful.

Registration opens four weeks before the event date and typically reaches capacity within two weeks given the limited verified-practitioner audience. Cancellation policy follows the platform's standard event-cancellation framework with specific attention to the share-back coordination that depends on confirmed attendance.

Accessibility provisions include live captioning of the case-study and share-back segments, recorded captions on the post-event recording, scheduled-pause-points in the discussion, and accommodations for attendees with varying communication styles in the rapid-share-back format. Specific accessibility requirements are accommodated through the registration form.

The session format has evolved across multiple iterations. The early iterations were single-format case-study presentations; attendee feedback consistently surfaced the value of the share-back segment as the highest-value component for practitioner-community building, leading to the current two-segment structure with both the case-study analysis and the share-back.

The session is one component of the platform's broader algorithmic-justice and AI-ethics infrastructure. Adjacent programming includes the AI Bias and Algorithmic Justice subgroup threads within the African Women in Tech Leadership network and the Academia and Research network, the annual practitioner retreat for verified members of the algorithmic-justice cohort, and individual mentor sessions with Joy Buolamwini and other senior researcher members.

The session series has produced sustained practitioner-community connections across the iterations. Attendees from prior sessions have collaborated on subsequent research projects, co-authored audit reports, and built specific policy-advocacy initiatives drawing on the connections formed in the session breakout segments.

The platform's event registration confirmation for the working session includes a calendar invitation, the platform's video-conferencing access details, the structured pre-event briefing document, and the case-study materials. The platform's broader confidentiality framework applies to the case-study analysis and the share-back. Specific organizational details, unpublished research findings, and pre-publication audit work shared in the session do not leave the room without the explicit consent of the originating attendee.

The session series participates in a broader practitioner community across academic, industry, and policy contexts. The Algorithmic Justice League's broader programming includes additional research seminars, policy-engagement workshops, and adjacent practitioner-community events that platform attendees can engage with beyond the specific platform session series.

Members of the platform's AI Bias and Algorithmic Justice subgroup are encouraged to engage with the broader scholarly and practitioner literature including the specific audit reports and policy briefs that organizations including the AJL, the Distributed AI Research Institute, the Center on Race and Digital Justice, and adjacent research organizations have produced across recent years.