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Artificial Intelligence & Research

AI & Research Guide

This guide contains links to resources about AI and machine learning as they pertain to the work of research.

Elon Showcase - Recent Scholarship in or around AI

Braun, D., Han, Y., & Wang, H. E. (2023). The application of feed forward neural networks to merger arbitrage: A return-based analysis. Finance Research Letters, 58, 104391.

Chen, C., & Sundar, S. S. (2023). Is this AI trained on Credible Data? The Effects of Labeling Quality and Performance Bias on User Trust. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 1–11.

Ferrell, O.C., Harrison, D.E., Ferrell, L.K., Ajjan, H., & Hochstein, B.W. (2024). A theoretical framework to guide AI ethical decision making. AMS Review.

Petrescu, M., Ajjan, H., & Harrison, D. (2023a). The Role of AI Agents in Spreading and Detecting Fake Online Reviews: A Systematic Review: An Abstract. In B. Jochims & J. Allen (Eds.), Optimistic Marketing in Challenging Times: Serving Ever-Shifting Customer Needs (pp. 333–334). Springer Nature Switzerland.

Petrescu, M., Ajjan, H., & Harrison, D. L. (2023b). Man vs machine – Detecting deception in online reviews. Journal of Business Research, 154, 113346.

Ryu, H., Miller, J., Teymuroglu, Z., Wang, X., Booth, V., & Campbell, S. A. (2021). Spatially localized cluster solutions in inhibitory neural networks. Mathematical Biosciences, 336, 108591.

Safarnejad, L., Xu, Q., Ge, Y., & Chen, S. (2021). A Multiple Feature Category Data Mining and Machine Learning Approach to Characterize and Detect Health Misinformation on Social Media. IEEE Internet Computing, 25(5), 43–51.

Xie, T., Ge, Y., Xu, Q., & Chen, S. (2023). Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance. AI, 4(1), 333–347.