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Human-AI collaboration by design

Published online by Cambridge University Press:  16 May 2024

Binyang Song*
Affiliation:
Virginia Tech, United States of America
Qihao Zhu
Affiliation:
Singapore University of Technology and Design, Singapore
Jianxi Luo
Affiliation:
Singapore University of Technology and Design, Singapore

Abstract

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Human-AI collaboration (HAIC) is a promising strategy to transform engineering design and innovation, yet how to design artificial intelligence (AI) to boost HAIC remains unclear. Accordingly, this paper provides a new, unified, and comprehensive scheme for classifying AI roles. On this basis, we develop an AI design framework that outlines expected AI capabilities, interactive attributes, and trust enablers across various HAIC scenarios, offering guidance for integrating AI into human teams effectively. We also discuss current advancements, challenges, and prospects for future research.

Type
Artificial Intelligence and Data-Driven Design
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

References

Bittner, E., Oeste-Reiß, S. and Leimeister, J.M., 2019. Where is the bot in our team? Toward a taxonomy of design option combinations for conversational agents in collaborative work.CrossRefGoogle Scholar
Bruemmer, D.J., Marble, J.L. and Dudenhoeffer, D.D., 2002, September. Mutual initiative in human-machine teams. In Proceedings of the IEEE 7th conference on human factors and power plants (pp. 7-7). IEEE.Google Scholar
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., … & Zhang, Y. (2023). Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712.Google Scholar
Caldwell, S., Sweetser, P., O'Donnell, N., Knight, M.J., Aitchison, M., Gedeon, T., Johnson, D., Brereton, M., Gallagher, M. and Conroy, D., 2022. An agile new research framework for hybrid human-AI teaming: Trust, transparency, and transferability. ACM Transactions on Interactive Intelligent Systems (TiiS), 12(3), pp.136.CrossRefGoogle Scholar
Callaway, E.It will change everything”: DeepMind's AI makes gigantic leap in solving protein structures. Nature. 2020 Dec;588(7837):203-204. https://dx.doi.org/10.1038/d41586-020-03348-4. PMID: 33257889.CrossRefGoogle Scholar
Chong, L., Raina, A., Goucher-Lambert, K., Kotovsky, K. and Cagan, J., 2023. The evolution and impact of human confidence in artificial intelligence and in themselves on ai-assisted decision-making in design. Journal of Mechanical Design, 145(3), p.031401.CrossRefGoogle Scholar
Chong, L., Zhang, G., Goucher-Lambert, K., Kotovsky, K. and Cagan, J., 2022. Human confidence in artificial intelligence and in themselves: The evolution and impact of confidence on adoption of AI advice. Computers in Human Behavior, 127, p.107018.CrossRefGoogle Scholar
Dellermann, D., Calma, A., Lipusch, N., Weber, T., Weigel, S. and Ebel, P., 2019. The Future of Human-AI Collaboration: A Taxonomy of Design Knowledge for Hybrid Intelligence Systems. Hawaii International Conference on System Sciences (HICSS).CrossRefGoogle Scholar
Deloitte. (2020). Insights 2020. The social enterprise in a world disrupted: Leading the shift from survive to thrive, 2021 DELOITTE GLOBAL HUMAN CAPITAL TRENDS, 64 pages, 9th of December.Google Scholar
Dubey, A., Abhinav, K., Jain, S., Arora, V. and Puttaveerana, A., 2020, February. HACO: a framework for developing human-AI teaming. 13th Innovations in Software Engineering Conference (pp. 1-9).CrossRefGoogle Scholar
Endsley, M.R., 2023. Supporting Human-AI Teams: Transparency, explainability, and situation awareness. Computers in Human Behavior, 140, p.107574.CrossRefGoogle Scholar
Filippi, S., 2023. Measuring the impact of ChatGPT on fostering concept generation in innovative product design. Electronics, 12(16), p.3535.CrossRefGoogle Scholar
Gyory, J.T., Song, B., Cagan, J. and McComb, C., 2021. Communication in AI-assisted teams during an interdisciplinary drone design problem. Proceedings of the Design Society, 1, pp.651660.CrossRefGoogle Scholar
Inel, O., Draws, T. and Aroyo, L., 2023, November. Collect, measure, repeat: Reliability factors for responsible AI data collection. AAAI Conf. on Human Computation and Crowdsourcing (Vol. 11, No. 1, pp. 5164).Google Scholar
Jiang, J., Karran, A.J., Coursaris, C.K., Léger, P.M. and Beringer, J., 2023. A situation awareness perspective on human-AI interaction: Tensions and opportunities. International Journal of Human–Computer Interaction, 39(9), pp.17891806.CrossRefGoogle Scholar
Liu, M., Hu, Y. (2023). Application Potential of Stable Diffusion in Different Stages of Industrial Design. Artificial Intelligence in HCI. Lecture Notes in Computer Science, vol 14050. Springer, Cham.CrossRefGoogle Scholar
Luo, J., 2022. Data-driven innovation: What is it?. IEEE Transactions on Engineering Management, 70(2), pp.784790.CrossRefGoogle Scholar
Luo, J., 2023. Designing the future of the fourth industrial revolution. Journal of Engineering Design, 34(10), 779785.CrossRefGoogle Scholar
Luo, J., Sarica, S., & Wood, K. L. (2021). Guiding data-driven design ideation by knowledge distance. Knowledge-Based Systems, 218, 106873, 2021.CrossRefGoogle Scholar
McDermotta, P.L., Walkera, K.E., Dominguez, C.O., Nelsonb, A. and Kasdaglis, N., 2017. Quenching the thirst for human-machine teaming guidance: Helping military systems acquisition leverage cognitive engineering research. In 13th International Conference on Naturalistic Decision Making (pp. 236–240).Google Scholar
Memmert, L. and Bittner, E., 2022. Complex Problem Solving through Human-AI Collaboration: Literature Review on Research Contexts.CrossRefGoogle Scholar
Nichol, A., Jun, H., Dhariwal, P., Mishkin, P. and Chen, M., 2022. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751.Google Scholar
OpenAI., 2023. ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat.Google Scholar
Papachristos, E., Skov Johansen, P., Møberg Jacobsen, R., Bjørn Leer Bysted, L. and Skov, M.B., 2021. How do People Perceive the Role of AI in Human-AI Collaboration to Solve Everyday Tasks?. 1st International Conference of the ACM Greek SIGCHI Chapter (pp. 1-6).CrossRefGoogle Scholar
Peeters, M.M., van Diggelen, J., Van Den Bosch, K., Bronkhorst, A., Neerincx, M.A., Schraagen, J.M. and Raaijmakers, S., 2021. Hybrid collective intelligence in a human–AI society. AI & society, 36, pp.217238.CrossRefGoogle Scholar
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M. and Sutskever, I., 2021, July. Zero-shot text-to-image generation. In International Conference on Machine Learning (pp. 8821–8831). PMLR.Google Scholar
Regenwetter, L., Nobari, A. H., & Ahmed, F. (2022). Deep generative models in engineering design: A review. Journal of Mechanical Design, 144(7), 071704.CrossRefGoogle Scholar
Rombach, R., Blattmann, A., Lorenz, D., Esser, P. and Ommer, B., 2022. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 10684–10695).CrossRefGoogle Scholar
Ross, A., Chen, N., Hang, E.Z., Glassman, E.L. and Doshi-Velez, F., 2021, May. Evaluating the interpretability of generative models by interactive reconstruction. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).CrossRefGoogle Scholar
Schelble, B.G., Lopez, J., Textor, C., Zhang, R., McNeese, N.J., Pak, R. and Freeman, G., 2022. Towards ethical AI: Empirically investigating dimensions of AI ethics, trust repair, and performance in human-AI teaming. Human Factors, p.00187208221116952.CrossRefGoogle Scholar
Seeber, I., Bittner, E., Briggs, R.O., De Vreede, T., De Vreede, G.J., Elkins, A., Maier, R., Merz, A.B., Oeste-Reiß, S., Randrup, N. and Schwabe, G., 2020. Machines as teammates: A research agenda on AI in team collaboration. Information & management, 57(2), p.103174.CrossRefGoogle Scholar
Shao, R., 2023, April. An Empathetic AI for Mental Health Intervention: Conceptualizing and Examining Artificial Empathy. In Proceedings of the 2nd Empathy-Centric Design Workshop (pp. 1-6).CrossRefGoogle Scholar
Siddharth, L., Blessing, L., & Luo, J. (2022). Natural language processing in-and-for design research. Design Science, 8, e21.CrossRefGoogle Scholar
Siemon, D., 2022. Elaborating team roles for artificial intelligence-based teammates in human-AI collaboration. Group Decision and Negotiation, 31(5), pp.871912.CrossRefGoogle Scholar
Song, B., Yuan, C., Permenter, F., Arechiga, N. and Ahmed, F., 2023. Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings. arXiv preprint arXiv:2306.06110.CrossRefGoogle Scholar
Song, B., Gyory, J.T., Zhang, G., Zurita, N.F.S., Stump, G., Martin, J., Miller, S., Balon, C., Yukish, M., McComb, C. and Cagan, J., 2022. Decoding the agility of artificial intelligence-assisted human design teams. Design Studies, 79, p.101094.CrossRefGoogle Scholar
Song, B., Soria Zurita, N.F., Nolte, H., Singh, H., Cagan, J. and McComb, C., 2022. When faced with increasing complexity: the effectiveness of artificial intelligence assistance for drone design. Journal of Mechanical Design, 144(2), p.021701.CrossRefGoogle Scholar
Song, B., Soria Zurita, N.F., Nolte, H., Singh, H., Cagan, J. and McComb, C., 2021. Addressing challenges to problem complexity: Effectiveness of AI assistance during the design process. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.CrossRefGoogle Scholar
Srinivasan, R. and González, B.S.M., 2022. The role of empathy for artificial intelligence accountability. Journal of Responsible Technology, 9, p.100021.CrossRefGoogle Scholar
Tan, S.C., Chen, W. and Chua, B.L., 2023. Leveraging generative artificial intelligence based on large language models for collaborative learning. Learning: Research and Practice, pp.110.CrossRefGoogle Scholar
Turing, A.M., 2012. Computing machinery and intelligence (1950). The Essential Turing: the Ideas That Gave Birth to the Computer Age, pp.433464.Google Scholar
Viros-i-Martin, A. and Selva, D., 2019. Daphne: A virtual assistant for designing earth observation distributed spacecraft missions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, pp.30-48.CrossRefGoogle Scholar
Vorobeva, D., Costa Pinto, D., António, N. and Mattila, A.S., 2023. The augmentation effect of artificial intelligence: can AI framing shape customer acceptance of AI-based services? Tourism, pp.121.CrossRefGoogle Scholar
Wang, L., Zhang, X., Li, Q., Zhang, M., Su, H., Zhu, J. and Zhong, Y., 2023. Incorporating neuro-inspired adaptability for continual learning in artificial intelligence. Nature Machine Intelligence, pp.113.CrossRefGoogle Scholar
Zhang, G., Raina, A., Cagan, J. and McComb, C., 2021. A cautionary tale about the impact of AI on human design teams. Design Studies, 72, p.100990.CrossRefGoogle Scholar
Zhu, Q., & Luo, J. (2023a). Generative transformers for design concept generation. Journal of Computing and Information Science in Engineering, 23(4), 041003.Google Scholar
Zhu, Q., & Luo, J. (2023b). Toward Artificial Empathy for Human-Centered Design: A Framework. Journal of Mechanical Design, 146(6), 061401.CrossRefGoogle Scholar
Zhu, Q., Zhang, X., & Luo, J. (2023). Biologically Inspired Design Concept Generation Using Generative Pre-Trained Transformers. Journal of Mechanical Design, 145(4), 041409.CrossRefGoogle Scholar