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Found 5 papers, 0 papers with code
AI Hallucinations: A Misnomer Worth Clarifying
As large language models continue to advance in Artificial Intelligence (AI), text generation systems have been shown to suffer from a problematic phenomenon termed often as "hallucination."
Systemic Fairness
This paper develops formalisms for firm versus systemic fairness, and calls for a greater focus in the algorithmic fairness literature on ecosystem-wide fairness - or more simply systemic fairness - in real-world contexts.
Privately Fine-Tuning Large Language Models with Differential Privacy
Differential privacy (DP) provides a rigorous framework that allows adding noise in the process of training or fine-tuning LLMs such that extracting the training data becomes infeasible (i. e., with a cryptographically small success probability).
Whom to Test? Active Sampling Strategies for Managing COVID-19
This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers.
Deep Learning for Information Systems Research
Related to this broader goal, this paper makes five timely contributions.
