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Found 11 papers, 1 papers with code
Exploring Public Opinion on Responsible AI Through The Lens of Cultural Consensus Theory
As the societal implications of Artificial Intelligence (AI) continue to grow, the pursuit of responsible AI necessitates public engagement in its development and governance processes.
Evaluating Large Language Models on the GMAT: Implications for the Future of Business Education
The rapid evolution of artificial intelligence (AI), especially in the domain of Large Language Models (LLMs) and generative AI, has opened new avenues for application across various fields, yet its role in business education remains underexplored.
FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design
Addressing this, we introduce \textsc{FinMem}, a novel LLM-based agent framework devised for financial decision-making.
Harnessing Collective Intelligence Under a Lack of Cultural Consensus
We find that iDLC-CCT better predicts the degree of consensus, generalizes well to out-of-sample entities, and is effective even with sparse data.
Actively learning a Bayesian matrix fusion model with deep side information
High-dimensional deep neural network representations of images and concepts can be aligned to predict human annotations of diverse stimuli.
A Game-theoretic Model of the Consumer Behavior Under Pay-What-You-Want Pricing Strategy
In this paper, we employed game theory and behavioral economics to model consumers' behavior in response to a PWYW pricing strategy where there is an information asymmetry between the consumer and supplier.
Evaluation of Human and Machine Face Detection using a Novel Distinctive Human Appearance Dataset
Face detection is a long-standing challenge in the field of computer vision, with the ultimate goal being to accurately localize human faces in an unconstrained environment.
Adaptive Sampling for Convex Regression
In this paper, we introduce the first principled adaptive-sampling procedure for learning a convex function in the $L_\infty$ norm, a problem that arises often in the behavioral and social sciences.
Capturing human category representations by sampling in deep feature spaces
Here, we introduce a method to estimate the structure of human categories that combines ideas from cognitive science and machine learning, blending human-based algorithms with state-of-the-art deep image generators.
Learning a face space for experiments on human identity
Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images.
