[Part 2 of 3] Foundations of Trustworthy AI: A Guide to Mitigating Systemic Bias and Reducing the Pilot-to-Production Gap

Author(s): Suresh Dhamapurkar

Originally published on Towards AI.

Part 2 of 3: Biases in Methodology and Measurement; Human and Systemic Sources of Bias; Real-World Examples of Systemic Biases in Consumer LLMs

This next category of biases arises from the processes of data selection, handling, and measurement. They represent fundamental errors in the application of the scientific method to data analysis.

The article elaborates on various systemic biases that occur in data selection, methodology, and human cognition in the context of AI and machine learning. It discusses biases in methodology and measurement, explaining how misapplication of the scientific method leads to significant errors. Various forms of bias including data-snooping, selection, measurement, and omitted variable bias are defined with examples illustrating their impact in different fields such as finance, human resources, and operations. The discussion extends to address algorithmic bias and its ethical implications, emphasizing how historic societal prejudices are encoded in AI systems, leading to discrimination at scale. Real-world implications of these biases, particularly in consumer large language models, highlight the need for awareness and mitigation strategies to ensure fairness in AI systems.

Read the full blog for free on Medium.

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Published via Towards AI