eventSeminar
Robotics in a Human-Centered World: Innovations and Implications
ConferenceApr 01, 20258:30 AM - 6:30 PM
April
01
2025
2025 Spring Conference
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2025 Spring Conference
In an era when information is treated as a form of power and self-knowledge an unqualified good, the value of what remains unknown is often overlooked.
In an era when information is treated as a form of power and self-knowledge an unqualified good, the value of what remains unknown is often overlooked.
IBM Synthetic Data Sets (SDS) have been created for use cases in the financial industry.
IBM Synthetic Data Sets (SDS) have been created for use cases in the financial industry.
Using the same machine learning model for high-stakes decisions in many settings amplifies the strengths, weaknesses, biases, and idiosyncrasies of the original model. When the same person re-encounters the same model again and again, or models trained on the same dataset, she might be wrongly rejected again and again. Thus algorithmic monoculture could lead to consistent ill-treatment of individual people by homogenizing the decision outcomes they experience. This talk will formalize the measure of outcome homogenization, describe experiments on US census data that demonstrate that the sharing of training data consistently homogenizes outcomes, then present an ethical argument for why and in what circumstances outcome homogenization is wrong.
Kathleen CreelHAI Network Affiliate; Assistant Professor of Philosophy and Computer Science, Northeastern University
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