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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah
Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah
Computer Information Systems Faculty Publications
In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use.
Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng
Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng
Research Collection School Of Computing and Information Systems
Unprecedented demand for data science professionals in the industry has led to many educational institutions launching new data science courses. It is however imperative that students of data science programmes learn through execution of real-world, authentic projects on top of acquiring foundational knowledge on the basics of data science. In the process of working on authentic, real-world projects, students not only create new knowledge but also learn to solve open, sophisticated, and ill-structured problems in an inter-disciplinary fashion. In this paper, we detailed our approach to design a data science curriculum premised on learners solving authentic data science problems sourced …
Cinema Trends And Viewer Preferences: An Analysis Of Movie Trends, Factors Leading To Box-Office Success, And Viewer Ratings, Brandon Crossland
Cinema Trends And Viewer Preferences: An Analysis Of Movie Trends, Factors Leading To Box-Office Success, And Viewer Ratings, Brandon Crossland
Analytics Capstones
This research paper investigates the critical factors that impact the success and profitability of feature films in the entertainment industry. The study is divided into two primary parts. The first part aims to identify trends in cinema and predict box office earnings using advanced data analytics techniques. The second part examines user reviews to determine the key factors that influence film viewership. The objective is to provide valuable insights to cinema enthusiasts, film executives, and streaming platforms, helping them make informed decisions on film production and recommendations. The methods utilized include descriptive data visualizations in Excel and Python and predictive …
Small But Mighty: Examing The Utility Of Microstatistics In Modeling Ice Hockey, Matt Palmer
Small But Mighty: Examing The Utility Of Microstatistics In Modeling Ice Hockey, Matt Palmer
Senior Honors Theses
As research into hockey analytics continues, an increasing number of metrics are being introduced into the knowledge base of the field, creating a need to determine whether various stats are useful or simply add noise to the discussion. This paper examines microstatistics – manually tracked metrics which go beyond the NHL’s publicly released stats – both through the lens of meta-analytics (which attempt to objectively assess how useful a metric is) and modeling game probabilities. Results show that while there is certainly room for improvement in understanding and use of microstats in modeling, the metrics overall represent an area of …
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
Regulating Machine Learning: The Challenge Of Heterogeneity, Cary Coglianese
All Faculty Scholarship
Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, …