Reciprocal Human Machine Learning (RHML): Human-AI Collaboration Based on Theories of Dyadic Learning

David G. Schwartz; Dov Teéni; Inbal Yahav

doi:10.1609/aaaiss.v1i1.27483

Reciprocal Human Machine Learning (RHML): Human-AI Collaboration Based on Theories of Dyadic Learning

David G. Schwartz, Dov Teéni, Inbal Yahav

Coller School of Management

Bar-Ilan University

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

In this position paper we advocate a Reciprocal Human Machine Learning paradigm based on two theories of humanhuman learning behavior. Drawing from Jörg’s theory of reciprocal learning in dyads and the Jewish tradition of Havruta - pair-based study, we suggest that human-machine collaboration based on these established human-human collaborative forms can achieve a rich and robust human-in-the-learningloop (HITLL) framework in which both parties experience learning over time.

Original language	English
Pages	94-97
Number of pages	4
DOIs	https://doi.org/10.1609/aaaiss.v1i1.27483
State	Published - 3 Oct 2023
Event	2023 AAAI Summer Symposium Series, SuSS 2023 - Singapore, Singapore Duration: 17 Jul 2023 → 19 Jul 2023

Conference

Conference	2023 AAAI Summer Symposium Series, SuSS 2023
Country/Territory	Singapore
City	Singapore
Period	17/07/23 → 19/07/23

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10.1609/aaaiss.v1i1.27483

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abstract = "In this position paper we advocate a Reciprocal Human Machine Learning paradigm based on two theories of humanhuman learning behavior. Drawing from J{\"o}rg{\textquoteright}s theory of reciprocal learning in dyads and the Jewish tradition of Havruta - pair-based study, we suggest that human-machine collaboration based on these established human-human collaborative forms can achieve a rich and robust human-in-the-learningloop (HITLL) framework in which both parties experience learning over time.",

author = "Schwartz, {David G.} and Dov Te{\'e}ni and Inbal Yahav",

note = "Publisher Copyright: {\textcopyright} 2023, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 2023 AAAI Summer Symposium Series, SuSS 2023 ; Conference date: 17-07-2023 Through 19-07-2023",

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Reciprocal Human Machine Learning (RHML): Human-AI Collaboration Based on Theories of Dyadic Learning

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