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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 for Artificial Intelligence. My research interests are behavioural economics, applied microeconomics, and artificial intelligence (AI). I use experimental methods in neoclassical economics to explore how humans perceive and benefit from AI and technology in deception, education, marketplaces, and political scenarios. Previously, I worked as a statistician at DeepFlux (accquired by Pivot Roots) and research assistant at ISI Kolkata. My mentors include Prof. Priyodarshi Banerjee and Prof. Saibal Kar. In 2021, I was also a Guest Lecturer of Economics at Women's Christian College, University of Kolkata. I founded Alankar, a women-run online jewelry brand fostering positive social impact on employbility. |
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| Research · Awards · Talks · Teaching |
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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, a distinguishing feature absent in previous text-based deception datasets. We show that there exists a class of detectors 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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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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Lies in textual form are pervasive on social media, yet evidence regarding individuals' ability to discern truth from deceptive textual content is scarce. To address this knwoeldge gap, we conduct an experiment on Prolific to investigate (1) people's ability in detecting lies in text, and additionally, (2) how their willingness to seek assistance from an artificial intelligence system (AI) in identifying lies varies with and without information about the AI's efficacy. |
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One of the key impacts of AI is on the marketplace. AI can be a persuasion tool. Just like advertisers and copywriters, AI can generate messages with the aim of persuading people about any good, topic, or idea. If AI-generated messages are as persuasive as human-generated ones, or even more, AI could have large impacts on society.We examine how generative AI performs as a persuasion tool, both when it competes and when it collaborates with humans. |
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A practical first step toward an end-to-end automation for scientific discovery. We posit that Large Generative Models (LGMs) present an incredible potential for automating hypothesis discovery, however, LGMs alone are not enough. |
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A less studied use case of generative AI, which we plan to focus on in this work, is scientific writing. Scientific writing is an inherently iterative process, often involving multiple revisions to refine clarity, accuracy, coherence, and readability while presenting complex scientific ideas. Not all writers have the same domain expertise. We envision a three-phase randomized controlled trial of increasing complexity to conduct this evaluation simulating a peer-reviewed process in a journal or conference publication. |
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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 |