Authors:
Alexander Smirnov
;
Andrew Ponomarev
;
Nikolay Shilov
and
Tatiana Levashova
Affiliation:
St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russian Federation
Keyword(s):
Decision Support, Augmented Intelligence, Large Language Models, Generative AI, Conversational AI, Dialogue-Based DSS, Evaluative AI.
Abstract:
The growing complexity of technical, social, and business systems created and managed by humans determine the need for effective decision support. Recent advancements in AI push the boundary of what can be accomplished using AI tools and what are possible modes of human-AI interaction, bringing a concept of augmented intelligence, extending intellectual capabilities of human by variety of AI-based tools, while leaving final decision-making (as well as some other operations, e.g., goal-setting, coordination, control) to a human. This paper explores possibilities of using augmented intelligence for decision support. Starting with a general structure of decision-making process, it highlights and reviews current trends in several branches of AI, that are most important for decision support. Then, it proposes an integrated approach combining conversational, generative, and evaluative AI. Distinguishing features of the proposed approach are integration and mutual enrichment of data- and mo
del-based techniques, as well as using modern LLMs as a basis for human-AI interaction during decision-making.
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