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Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach
There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compo...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561722/ https://www.ncbi.nlm.nih.gov/pubmed/34738081 http://dx.doi.org/10.3389/frai.2021.670009 |
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author | Kozierok, Robyn Aberdeen, John Clark, Cheryl Garay, Christopher Goodman, Bradley Korves, Tonia Hirschman, Lynette McDermott, Patricia L. Peterson, Matthew W. |
author_facet | Kozierok, Robyn Aberdeen, John Clark, Cheryl Garay, Christopher Goodman, Bradley Korves, Tonia Hirschman, Lynette McDermott, Patricia L. Peterson, Matthew W. |
author_sort | Kozierok, Robyn |
collection | PubMed |
description | There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed. Here, we present a set of criteria for evaluating these systems, called Hallmarks of Human-Machine Collaboration. The Hallmarks build on the success of heuristic evaluation used by the user interface community and past evaluation techniques used in the spoken language and chatbot communities. They consist of observable characteristics indicative of successful collaborative communication, grouped into eight high-level properties: robustness; habitability; mutual contribution of meaningful content; context-awareness; consistent human engagement; provision of rationale; use of elementary concepts to teach and learn new concepts; and successful collaboration. We present examples of how we used these Hallmarks in the DARPA Communicating with Computers (CwC) program to evaluate diverse activities, including story and music generation, interactive building with blocks, and exploration of molecular mechanisms in cancer. We used the Hallmarks as guides for developers and as diagnostics, assessing systems with the Hallmarks to identify strengths and opportunities for improvement using logs from user studies, surveying the human partner, third-party review of creative products, and direct tests. Informal feedback from CwC technology developers indicates that the use of the Hallmarks for program evaluation helped guide development. The Hallmarks also made it possible to identify areas of progress and major gaps in developing systems where the machine is an equal, creative partner. |
format | Online Article Text |
id | pubmed-8561722 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85617222021-11-03 Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach Kozierok, Robyn Aberdeen, John Clark, Cheryl Garay, Christopher Goodman, Bradley Korves, Tonia Hirschman, Lynette McDermott, Patricia L. Peterson, Matthew W. Front Artif Intell Artificial Intelligence There is a growing desire to create computer systems that can collaborate with humans on complex, open-ended activities. These activities typically have no set completion criteria and frequently involve multimodal communication, extensive world knowledge, creativity, and building structures or compositions through multiple steps. Because these systems differ from question and answer (Q&A) systems, chatbots, and simple task-oriented assistants, new methods for evaluating such collaborative computer systems are needed. Here, we present a set of criteria for evaluating these systems, called Hallmarks of Human-Machine Collaboration. The Hallmarks build on the success of heuristic evaluation used by the user interface community and past evaluation techniques used in the spoken language and chatbot communities. They consist of observable characteristics indicative of successful collaborative communication, grouped into eight high-level properties: robustness; habitability; mutual contribution of meaningful content; context-awareness; consistent human engagement; provision of rationale; use of elementary concepts to teach and learn new concepts; and successful collaboration. We present examples of how we used these Hallmarks in the DARPA Communicating with Computers (CwC) program to evaluate diverse activities, including story and music generation, interactive building with blocks, and exploration of molecular mechanisms in cancer. We used the Hallmarks as guides for developers and as diagnostics, assessing systems with the Hallmarks to identify strengths and opportunities for improvement using logs from user studies, surveying the human partner, third-party review of creative products, and direct tests. Informal feedback from CwC technology developers indicates that the use of the Hallmarks for program evaluation helped guide development. The Hallmarks also made it possible to identify areas of progress and major gaps in developing systems where the machine is an equal, creative partner. Frontiers Media S.A. 2021-10-18 /pmc/articles/PMC8561722/ /pubmed/34738081 http://dx.doi.org/10.3389/frai.2021.670009 Text en Copyright © 2021 The MITRE Corporation. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence Kozierok, Robyn Aberdeen, John Clark, Cheryl Garay, Christopher Goodman, Bradley Korves, Tonia Hirschman, Lynette McDermott, Patricia L. Peterson, Matthew W. Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title | Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title_full | Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title_fullStr | Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title_full_unstemmed | Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title_short | Assessing Open-Ended Human-Computer Collaboration Systems: Applying a Hallmarks Approach |
title_sort | assessing open-ended human-computer collaboration systems: applying a hallmarks approach |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8561722/ https://www.ncbi.nlm.nih.gov/pubmed/34738081 http://dx.doi.org/10.3389/frai.2021.670009 |
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