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I am a third-year PhD student in Economics at the University of Utah. My research interests are behavioral economics, experimental economics (Lab/Field), and applied microeconomics. I am advised by Prof. Haimanti Bhattacharya and Prof. Subhasish Dugar. I also actively collaborate with Allen Institute of Artificial Intelligence. I focus on understanding the learning effects and ability of deception detection with experience using neoclassical methods. Furthermore, I plan to explore how humans perceive and benefit from AI and technology in education, marketplaces, and political scenarios. Previously, I worked as a statistician at DeepFlux and research assistant at ISI Kolkata when I worked with Prof. Priyodarshi Banerjee and Prof. Saibal Kar. In 2021, I was also a Guest Lecturer at Women's Christian College. I founded Alankar, a women-run online jewelry brand fostering positive social impact on employbility. |
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Deceptive communication or behavior can inflict loss, making it important to be able to distinguish these from trustworthy ones. This article pursues the hypothesis that repeated exposure or experience can cause learning and hence better detection of deception. We investigate using data culled from events in a TV game show. Decision-makers in the show repeatedly faced situations where they had to correctly identify an individual from within a group all claiming to be that individual. Increased experience reduced average detection error in the sample. Analysis of the data suggested this relationship was significant and driven by learning. |
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We conduct a laboratory experiment utilizing data from a TV game show, where natural conversations surrounding an underlying objective truth between individuals with conflicting objectives lead to intentional deception. We pursue several lines of inquiry: Do individuals seek the assistance of an artificial intelligence (AI) tool to aid their discernment of truth from text-based misinformation? Are individuals willing to pay for the service provided by the AI? We also investigate factors that may influence individuals' reluctance in or excessive dependence on seeking AI assistance, such as "AI aversion" or its absence, as well as overconfidence in one's ability to identify the truth. Furthermore, we examine, while controlling for the predictive accuracies of both the majority of humans and the AI tool, whether individuals, in comparison to the AI tool, are more or less inclined to submit the same guess that a majority of other individuals had submitted for that episode as their own. |
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We analyze a novel TV game show data where conversations in a high-stake environment between individuals with conflicting objectives result in lies. We investigate the manifestation of potentially verifiable language cues of deception in the presence of objective truth. We show that there exists a class of detectors (based on a Large Language Model) that have similar truth detection performance compared to human subjects, even when the former accesses only the language cues while the latter engages in conversations with complete access to all potential sources of cues (language and audio-visual). Our model detects novel but accurate language cues in many cases where humans failed to detect deception, opening up the possibility of humans collaborating with algorithms and ameliorating their ability to detect the truth. |
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Women make up a substantial majority of India's poor and they are the cruelest victims of the society. Organizing women through Self Help Groups and equipping them to undertake income-generating activities through the formation of microenterprises have created an economic revolution in the country. The paper focuses on the scope and rationale of microfinance in India and how the Self Help Group-Bank Linkage Programme by NABARD has played its part in empowering rural women financially. We find positive increase in loan disbursements, but sheer increase in loan outstanding over a period of ten years. |
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© Sanchaita Hazra |