BABL AI: Algorithmic Bias and Risk Assessments – Lessons from Practice

Written by Jeremy Werner

Jeremy is an experienced journalists, skilled communicator, and constant learner with a passion for storytelling and a track record of crafting compelling narratives. He has a diverse background in broadcast journalism, AI, public relations, data science, and social media management.
Posted on 01/11/2024
In Research

BABL AI has been a leading auditing firm of AI systems for years and one of the ways we’ve highlighted our expertise is in academic papers. In “Algorithmic Bias and Risk Assessments,” CEO Shea Brown, Chief Ethics Officer Jovana Davidovic, and Senior Advisors Benjamin Lange and Mitt Regan, outlined their approach to assessing algorithmic systems for ethical risk.


In the paper, they emphasized the distinction between ethical risk and technical algorithmic bias assessments. They discuss the interdependence of these assessments and stress the importance of situating algorithms within their socio-technical contexts. Drawing on their experience in advising and conducting ethical risk assessments for clients across various industries, the authors identify key factors that may be ethically relevant in algorithm use. They aim to contribute insights to the emerging field of algorithm assessment and audits, with the overarching goal of minimizing the risk of harm associated with algorithmic systems.

You can read the full article HERE.

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