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I am a fourth-year PhD student in Economics at the University of Utah. 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 behavioral economics, experimental economics (Lab/Field), applied microeconomics, and artificial intelligence (AI). My work focuses on applying the methodologies of experimental economics in neoclassical economics to explore and gain deeper insights into the potential of AI in influencing human decision-making. Previously, I worked as a statistician at DeepFlux (now accquired by Pivot Roots) and a research assistant at ISI Kolkata. My mentors include Prof. Priyodarshi Banerjee and Prof. Saibal Kar. In 2021, I was also a 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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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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We analyze a novel TV game show data where conversations in a high-stake environment between individuals with conflicting objectives result in lies in the presence of an objective truth, a distinguishing feature absent in previous text-based deception datasets. We show that there exists a class of detectors with similar truth detection performance as humans, even when the former accesses only the language cues while the latter detects lies using both language and audio-visual cues. 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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We investigate how low-quality AI advisors, lacking quality disclosures, can help spread text-based lies while seeming to help people detect lies. Participants in our experiment discern truth from lies by evaluating transcripts from a game show that mimicked deceptive social media exchanges on topics with objective truths. We find that when relying on low-quality advisors without disclosures, participants' truth-detection rates fall below their own abilities, which recovered once the AI's true effectiveness was revealed. Conversely, high-quality advisor enhances truth detection, regardless of disclosure. We discover that participants' expectations about AI capabilities contribute to their undue reliance on opaque, low-quality advisors. |
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We examine whether individuals can identify factual incorrectness in an LLM (GPT) when asked questions about different US and international policy-relevant topics. Detecting hallucinations is challenging, and current LLMs struggle with this task, making it crucial to distinguish between hallucinated text and factual statements, especially given GPT's dominance in internet search platforms. |
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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 |