Browsing by Author "Mohanty, Vikram"

Now showing 1 - 6 of 6
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    Designing Human-AI Collaborative Systems for Historical Photo Identification
    Mohanty, Vikram (Virginia Tech, 2023-08-30)
    Identifying individuals in historical photographs is important for preserving material culture, correcting historical records, and adding economic value. Historians, antiques dealers, and collectors often rely on manual, time-consuming approaches. While Artificial Intelligence (AI) offers potential solutions, it's not widely adopted due to a lack of specialized tools and inherent inaccuracies and biases. In my dissertation, I address this gap by combining the complementary strengths of human intelligence and AI. I introduce Photo Sleuth, a novel person identification pipeline that combines crowdsourced expertise with facial recognition, supporting users in identifying unknown portraits from the American Civil War era (1861--65). Despite successfully identifying numerous unknown photos, users often face the `last-mile problem' --- selecting the correct match(es) from a shortlist of high-confidence facial recognition candidates while avoiding false positives. To assist experts, I developed Second Opinion, an online tool that employs a novel crowdsourcing workflow, inspired by cognitive psychology, effectively filtering out up to 75% of facial recognition's false positives. Yet, as AI models continually evolve, changes in the underlying model can potentially impact user experience in such crowd--expert--AI workflows. I conducted an online study to understand user perceptions of changes in facial recognition models, especially in the context of historical person identification. Our findings showed that while human-AI collaborations were effective in identifying photos, they also introduced false positives. To reduce these misidentifications, I built Photo Steward, an information stewardship architecture that employs a deliberative workflow for validating historical photo identifications. Building on this foundation, I introduced DoubleCheck, a quality assessment framework that combines community stewardship and comprehensive provenance information, for helping users accurately assess photo identification quality. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that foster accurate decision-making in the context of historical photo identification.
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    DoubleCheck: Designing Community-based Assessability for Historical Person Identification
    Mohanty, Vikram; Luther, Kurt (ACM, 2023)
    Historical photos are valuable for their cultural and economic significance, but can be difficult to identify accurately due to various challenges such as low-quality images, lack of corroborating evidence, and limited research resources. Misidentified photos can have significant negative consequences, including lost economic value, incorrect historical records, and the spread of misinformation that can lead to perpetuating conspiracy theories. To accurately assess the credibility of a photo identification (ID), it may be necessary to conduct investigative research, use domain knowledge, and consult experts. In this paper, we introduce DoubleCheck, a quality assessment framework for verifying historical photo IDs on Civil War Photo Sleuth (CWPS), a popular online platform for identifying American Civil War-era photos using facial recognition and crowdsourcing. DoubleCheck focuses on improving CWPS's user experience and system architecture to display information useful for assessing the quality of historical photo IDs on CWPS. In a mixed-methods evaluation of DoubleCheck, we found that users contributed a wide diversity of sources for photo IDs, which helped facilitate the community's assessment of these IDs through DoubleCheck's provenance visualizations. Further, DoubleCheck's quality assessment badges and visualizations supported users in making accurate assessments of photo IDs, even in cases involving ID conflicts.
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    A Mycorrhizal Model for Transactive Solar Energy Markets with Battery Storage
    Gould, Zachary Michael Isaac; Mohanty, Vikram; Reichard, Georg; Saad, Walid; Shealy, Tripp; Day, Susan (MDPI, 2023-05-13)
    Distributed market structures for local, transactive energy trading can be modeled with ecological systems, such as mycorrhizal networks, which have evolved to facilitate interplant carbon exchange in forest ecosystems. However, the complexity of these ecological systems can make it challenging to understand the effect that adopting these models could have on distributed energy systems and the magnitude of associated performance parameters. We therefore simplified and implemented a previously developed blueprint for mycorrhizal energy market models to isolate the effect of the mycorrhizal intervention in allowing buildings to redistribute portions of energy assets on competing local, decentralized marketplaces. Results indicate that the applied mycorrhizal intervention only minimally affects market and building performance indicators—increasing market self-consumption, decreasing market self-sufficiency, and decreasing building weekly savings across all seasonal (winter, fall, summer) and typological (residential, mixed-use) cases when compared to a fixed, retail feed-in-tariff market structure. The work concludes with a discussion of opportunities for further expansion of the proposed mycorrhizal market framework through reinforcement learning as well as limitations and policy recommendations considering emerging aggregated distributed energy resource (DER) access to wholesale energy markets.
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    Photo Steward: A Deliberative Collective Intelligence Workflow for Validating Historical Archives
    Mohanty, Vikram; Luther, Kurt (ACM, 2023-11-06)
    Historical photographs of people generate significant cultural and economic value, but correctly identifying the subjects of photos can be a difficult task, requiring careful attention to detail while synthesizing large amounts of data from diverse sources. When photos are misidentified, the negative consequences can include financial losses and inaccuracies in the historical record, and even the spread of mis- and disinformation. To address this challenge, we introduce Photo Steward, an information stewardship architecture that leverages a deliberative workflow for validating historical photo IDs. We explored Photo Steward in the context of Civil War Photo Sleuth (CWPS), a popular online community dedicated to identifying photos from the American Civil War era (1861–65) using facial recognition and crowdsourcing. While the platform has been successful in identifying hundreds of unknown photographs, there have been concerns about unverified identifications and misidentifications. Our exploratory evaluation of Photo Steward on CWPS showed that its validation workflow encouraged users to deliberate while making photo ID decisions. Further, its stewardship visualizations helped users to assess photo ID information accurately, while fostering diverse forms of stigmergic collaboration.
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    Promoting Sustainable Charging Through User Interface Interventions
    Filipowicz, Alexandre; Bravo, Nayeli; Iliev, Rumen; Mohanty, Vikram; Wu, Charlene; Shamma, David A. (ACM, 2023-09-18)
    With the rising popularity of electrified vehicles, emphasis has been placed on encouraging charging with renewable energy and maximizing battery longevity to improve vehicle sustainability. Many mobile applications offer tools to suggest charging times with more sustainable renewable energy and charging strategies that preserve battery health. However, these options often result in longer, less convenient charging times for drivers. Here we conducted three charging scenario studies to identify factors that influence willingness to wait for sustainable charging. Participants selected between faster but less sustainable charging options and slower charging options that either reduce charging emissions or improve battery longevity. We find people’s willingness to wait for green energy is influenced by situational factors; further we find that information and battery longevity interventions can increase willingness to wait for sustainable charging. Finally, we provide design recommendations to promote sustainably in charging behaviors.
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    Save A Tree or 6 kg of CO2? Understanding Effective Carbon Footprint Interventions for Eco-Friendly Vehicular Choices
    Mohanty, Vikram; Filipowicz, Alexandre; Bravo, Nayeli; Carter, Scott; Shamma, David (ACM, 2023-04-19)
    From ride-hailing to car rentals, consumers are often presented with eco-friendly options. Beyond highlighting a “green” vehicle and CO2 emissions, CO2 equivalencies have been designed to provide understandable amounts; we ask which equivalencies will lead to eco-friendly decisions. We conducted five ride-hailing scenario surveys where participants picked between regular and eco-friendly options, testing equivalencies, social features, and valence-based interventions. Further, we tested a car-rental embodiment to gauge how an individual (needing a car for several days) might behave versus the immediate ride-hailing context. We find that participants are more likely to choose green rides when presented with additional information about emissions; CO2 by weight was found to be the most effective. Further, we found that information framing—be it individual or collective footprint, positive or negative valence— had an impact on participants’ choices. Finally, we discuss how our findings inform the design of effective interventions for reducing car-based carbon-emissions.