2026-09-05 –, Workshop Room A
AI doesn’t just reflect society—it amplifies its contradictions. From viral “bikini” image manipulation trends(!) in generative tools to hiring systems that systematically disadvantage women* in tech, we see the same pattern: erasure in one place, hypervisibility in another. In this hands-on workshop, we build AI agents with LangChain and LangGraph to detect and surface gender bias in real text systems—turning critique into code, and visibility into a technical feature.
We start by examining real-world examples of bias in tech and AI outputs, then move directly into building AI agents using LangChain and LangGraph. Participants will design and implement a pipeline that detects gender bias in text, classifies its severity, and generates more inclusive rewrites.
The focus is practical: turning bias from an abstract concept into something observable, measurable, and actionable through working AI agents.
AI Agents, Women* in Tech, Gender Bias, LangChain/LangGraph, LLMs
Hilal Işık is a Senior Data Analytics Coach, researcher, and community-driven practitioner working at the intersection of data, education, and social impact. She has hands-on experience in Python, SQL, machine learning, and data visualization, and focuses on translating complex data into clear, actionable insights for diverse audiences. Alongside her work in data analytics, she is an independent researcher and published author with a background in sociology, human rights, and European Studies. Hilal is also actively involved in the tech community, including initiatives such as PyLadies, where she supports knowledge sharing and collaborative learning around AI and data projects.
