Tune in to learn about the applicability of algorithmic auditing.

Guest, Shea Brown, founder and CEO of BABL AI, joins us to share his perspective. An Associate Professor of Instruction at the University of Iowa, Brown has his PhD in Astrophysics and specializes in AI Ethics and Machine Learning, with a focus on Algorithmic Auditing and AI Governance.

In this episode, we discuss:

– Brown’s use of AI in astrophysics and its applicability, given the extent of data from the sky

– Brown’s transition to founding BABL AI after realizing a big problem: countless examples of bias in AI

– BABL AI and the consulting work they do and what the process entails

– algorithmic auditing which, like any audit, is a check and balance aimed at ensuring people and organizations meet the required standards and follow appropriate procedures (i.e., we don’t want to harm people, we don’t want to infringe on people’s rights, people should be involved in algorithmic decision-making, etc.)

– algorithmic auditing, with a focus on the socio-technical aspect of how tech is being used more broadly

– what to expect for the future of AI regulation and governance

– the importance of accountability and transparency, and the trade-offs that come with regulation

– algorithmic transparency, and whether your audience knows an algorithm is being used and the data being processed; accountability and transparency are important for building trust within a society which should demand more from the companies using their data

– how historical data will always hold a level of bias that needs to be considered

If you like what you hear, follow us on LinkedIn (@Gakovii) or Instagram (@gakovii__).

Technically Biased is available on Spotify, Apple, and Amazon, among others.

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