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An Exploration Of Causal Cognition In Large Language Models, Vicky Chang
An Exploration Of Causal Cognition In Large Language Models, Vicky Chang
Electronic Thesis and Dissertation Repository
Causal cognition, how beings perceive and reason about cause and effect, is crucial not only for survival and adaptation in biological entities but also for the development of causal artificial intelligence. Large language models (LLMs) have recently taken center stage due to their remarkable capabilities, demonstrating human-like reasoning in their generative responses. This thesis explores how LLMs perform on causal reasoning questions and how modifying information in the prompt affect their reasoning. Using 1392 causal inference questions from the CLADDER dataset, LLM responses were assessed for accuracy. With simple prompting, LLMs performed more accurately on intervention queries compared to association …
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Towards Long-Term Fairness In Sequential Decision Making, Yaowei Hu
Graduate Theses and Dissertations
With the development of artificial intelligence, automated decision-making systems are increasingly integrated into various applications, such as hiring, loans, education, recommendation systems, and more. These machine learning algorithms are expected to facilitate faster, more accurate, and impartial decision-making compared to human judgments. Nevertheless, these expectations are not always met in practice due to biased training data, leading to discriminatory outcomes. In contemporary society, countering discrimination has become a consensus among people, leading the EU and the US to enact laws and regulations that prohibit discrimination based on factors such as gender, age, race, and religion. Consequently, addressing algorithmic discrimination has …
Achieving Causal Fairness In Recommendation, Wen Huang
Achieving Causal Fairness In Recommendation, Wen Huang
Graduate Theses and Dissertations
Recommender systems provide personalized services for users seeking information and play an increasingly important role in online applications. While most research papers focus on inventing machine learning algorithms to fit user behavior data and maximizing predictive performance in recommendation, it is also very important to develop fairness-aware machine learning algorithms such that the decisions made by them are not only accurate but also meet desired fairness requirements. In personalized recommendation, although there are many works focusing on fairness and discrimination, how to achieve user-side fairness in bandit recommendation from a causal perspective still remains a challenging task. Besides, the deployed …
Achieving Causal Fairness In Machine Learning, Yongkai Wu
Achieving Causal Fairness In Machine Learning, Yongkai Wu
Graduate Theses and Dissertations
Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative …