Data-Based Insights for the Masses: Scaling Natural Language Querying to Middleware Data

Kausik, Lakkaraju,; Palaiya, Vinamra; Paladi, Sai Teja; Appajigowda, Chinmayi; Srivastava, Biplav; Johri, Lokesh

doi:10.1007/978-3-031-00129-1_49

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Our architecture has been built, generalizing our experience building chatbots across different domains where the usability of the system is essential (for example, education (Lakkaraju et al 2022a), networking (Lakkaraju et al 2022b), etc.) with little or no modifications.…”

Section: Safe and Usable Chatbot Architecturementioning

confidence: 99%

On safe and usable chatbots for promoting voter participation

Muppasani,

Pallagani,

Lakkaraju

et al. 2023

AI Magazine

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Chatbots, or bots for short, are multimodal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis‐information and hacking. Instead, in this work, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first‐time voters. In particular, we have built a system that amplifies official information while personalizing it to users' unique needs transparently (e.g., language, cognitive abilities, linguistic abilities). The uniqueness of this work are (a) a safe design where only responses that are grounded and traceable to an allowed source (e.g., official question/answer) will be answered via system's self‐awareness (metacognition), (b) a do‐not‐respond strategy that can handle customizable responses/deflection, and (c) a low‐programming design‐pattern based on the open‐source Rasa platform to generate chatbots quickly for any region. Our current prototypes use frequently asked questions (FAQ) election information for two US states that are low on an ease‐of‐voting scale, and have performed initial evaluations using focus groups with senior citizens. Our approach can be a win‐win for voters, election agencies trying to fulfill their mandate and democracy at large.

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Section: Safe and Usable Chatbot Architecturementioning

confidence: 99%

On safe and usable chatbots for promoting voter participation

Muppasani,

Pallagani,

Lakkaraju

et al. 2023

AI Magazine

Self Cite

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show abstract

“…Fourth, the results should be understandable to the user for which innovations are needed in visualization, interaction and explanation generation. We have begun initial steps towards them (Muppasani et al 2022;Lakkaraju et al 2022).…”

Section: Assigning States To Power Data Of a Locationmentioning

confidence: 99%

A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors

Muppasani

Jaya²,

Appajigowda³

et al. 2023

AAAI

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The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.

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Data-Based Insights for the Masses: Scaling Natural Language Querying to Middleware Data

Cited by 2 publications

References 3 publications

On safe and usable chatbots for promoting voter participation

On safe and usable chatbots for promoting voter participation

A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors

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