107 research outputs found
Machine Learning from Health Insurance Administrative Data: Opioids, Obamacare, and Other Applications
Decolonial AI Alignment: Vi\'{s}esadharma, Argument, and Artistic Expression
Prior work has explicated the coloniality of artificial intelligence (AI)
development and deployment. One process that that work has not engaged with
much is alignment: the tuning of large language model (LLM) behavior to be in
line with desired values based on fine-grained human feedback. In addition to
other practices, colonialism has a history of altering the beliefs and values
of colonized peoples; this history is recapitulated in current LLM alignment
practices. We suggest that AI alignment be decolonialized using three
proposals: (a) changing the base moral philosophy from Western philosophy to
dharma, (b) permitting traditions of argument and pluralism in alignment
technologies, and (c) expanding the epistemology of values beyond instructions
or commandments given in natural language
Quantization of Prior Probabilities for Hypothesis Testing
Bayesian hypothesis testing is investigated when the prior probabilities of
the hypotheses, taken as a random vector, are quantized. Nearest neighbor and
centroid conditions are derived using mean Bayes risk error as a distortion
measure for quantization. A high-resolution approximation to the
distortion-rate function is also obtained. Human decision making in segregated
populations is studied assuming Bayesian hypothesis testing with quantized
priors
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