Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential ...
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Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential impact of generative AI technologies such as ChatGPT in the domains of education, business, and society. Its objective is to offer initial guidance on the opportunities, challenges, and implications associated with these technologies. It is worth noting that, given its nature as an editorial opinion piece, this submission has not undergone a formal double-blind review process but has been reviewed informally by appropriate experts.Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer 1 Equal contributions Y.K. Dwivedi et al.International Journal of Information Management 71 (2023) 102642 3 significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.
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Semantic filters:
misinformationchatGPT
Topics:
artificial intelligence generative AI Twitter privacy innovation management
Methods:
chatGPT chatbot generative pre-trained transformer literature study language model
What is the Minimum to Trust AI?—A Requirement Analysis for (Generative) AI-based Texts
2023 | International Conference on Business Informatics | Citations: 0
Authors: Tomitza, Christoph; Schaschek, Myriam; Straub, Lisa; Winkelmann, Axel
Abstract: The generative Artificial Intelligence (genAI) innovation enables new potential ...
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Abstract: The generative Artificial Intelligence (genAI) innovation enables new potentials for end-users, affecting youth and the inexperienced. Nevertheless, as an innovative technology, genAI risks generating misinformation that is not recognizable as such. The extraordinary AI outputs can result in increased trustworthiness. An end-user assessment system is necessary to expose the unfounded reliance on erroneous responses. This paper identifies requirements for an assessment system to prevent end-users from overestimating trust in generated texts. Thus we conducted requirements engineering based on a literature review and two international surveys. The results confirmed the requirements which enable human protection, human support, and content veracity in dealing with genAI. Overestimated trust is rooted in miscalibration; clarity about genAI and its provider is essential to solving this phenomenon, and there is a demand for human verifications. Consequently, our findings provide evidence for the significance of future IS research on human-centered genAI trust solutions.
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Semantic filters:
misinformationchatGPT
Topics:
artificial intelligence database system misinformation generative AI source code
Methods:
survey chatGPT synthesis literature study cross sectional research