Authors: Wei Sun, Scott McFaddin, Linh Ha Tran, Shivaram Subramanian, Kristjan Greenewald, Yeshi Tenzin, Zack Xue, Youssef Drissi, Markus Ettl

Published on: February 03, 2024

Impact Score: 8.52

Arxiv code: Arxiv:2402.02006

Summary

• What is new: Integrating scalable causal inference methods, interpretable decision-making approaches, and the use of large language models for bridging communication gaps between data scientists and business users.
• Why this is important: Challenges in enterprise adoption of prescriptive AI due to limitations in observational data, issues with interpretability of AI-driven recommendations, and the communication divide between data scientists and business users.
• What the research proposes: A suite of prescriptive AI solutions that includes scalable causal inference, interpretable algorithms, and a conversational agent powered by large language models for improved communication.
• Results: The proof-of-concept, PresAIse, demonstrates the ability of non-ML experts to interact with prescriptive AI models through a natural language interface, making advanced analytics accessible for strategic decision-making.

Technical Details

Technological frameworks used: PresAIse

Models used: Large Language Models (LLMs)

Data used: Observational data

Potential Impact

This research could impact various sectors seeking to adopt prescriptive AI for decision-making, including healthcare, finance, and logistics, by offering a more interpretable and accessible approach. Companies in the AI development and enterprise analytics platforms could particularly benefit or face disruption.

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