Augmented Intelligence: Surveys of Literature and Expert Opinion to Understand Relations Between Human Intelligence and Artificial Intelligence

Yau, Kok‐Lim Alvin; Lee, HeeJeong Jasmine; Chong, Yung-Wey; Ling, Mee Hong; Syed, Aqeel Raza; Wu, Celimuge; Goh, Hock Guan

doi:10.1109/access.2021.3115494

Cited by 23 publications

(9 citation statements)

References 38 publications

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“…Our study contributes to research on applying machine learning to detect fake content in online platforms and, to some extent, the literature on human-AI interaction (A. Rai et al, 2019;Yau et al, 2021). The literature has discussed a wide range of scenarios of how we can keep humans and AI in a loop to achieve maximum performance (Fügener et al, 2021;Ge et al, 2021;Lou & Wu, 2021;Raisch & Krakowski, 2021).…”

Section: Theoretical Implicationsmentioning

confidence: 91%

“…Rai et al, 2019), to advance fake content detection. Specifically, we augment fake content detection by identifying discriminating and domain invariant linguistic features based on a large collection of human judgments and perceptions of truth and deception, representing an approach that incorporates collective human intelligence to enhance AI (Yau et al, 2021).…”

Section: Production and Operations Managementmentioning

confidence: 99%

“…To address this issue, this study adopts an augmented artificial intelligence (AI) perspective, defined as a type of human–AI hybrid where humans and AI augment one and another (A. Rai et al., 2019), to advance fake content detection. Specifically, we augment fake content detection by identifying discriminating and domain invariant linguistic features based on a large collection of human judgments and perceptions of truth and deception, representing an approach that incorporates collective human intelligence to enhance AI (Yau et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

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Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

et al. 2023

Production and Operations Management

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Online platforms are experimenting with interventions such as content screening to moderate the effects of fake, biased, and incensing content. Yet, online platforms face an operational challenge in implementing machine learning algorithms for managing online content due to the labeling problem, where labeled data used for model training are limited and costly to obtain. To address this issue, we propose a domain adaptive transfer learning via adversarial training approach to augment fake content detection with collective human intelligence. We first start with a source domain dataset containing deceptive and trustworthy general news constructed from a large collection of labeled news sources based on human judgments and opinions. We then extract discriminating linguistic features commonly found in source domain news using advanced deep learning models. We transfer these features associated with the source domain to augment fake content detection in three target domains: political news, financial news, and online reviews. We show that domain invariant linguistic features learned from a source domain with abundant labeled examples can effectively improve fake content detection in a target domain with very few or highly unbalanced labeled data. We further show that these linguistic features offer the most value when the level of transferability between source and target domains is relatively high. Our study sheds light on the platform operation in managing online content and resources when applying machine learning for fake content detection. We also outline a modular architecture that can be adopted in developing content screening tools in a wide spectrum of fields.

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Section: Theoretical Implicationsmentioning

confidence: 91%

Section: Production and Operations Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

et al. 2023

Production and Operations Management

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“…Before recent advances in artificial intelligence (AI), automating our search process has been extremely difficult due to differences in the regulations. However, augmented intelligence (AuI), which combines Boolean logic and AI ( 11 ), proposes to reduce the massive amount of work required by human involvement to perform systematic reviews ( 12 – 14 ). The use of machine learning (ML) algorithms, a subset of AI ( 15 ), has previously been demonstrated to assist with categorized document selection in systematic literature reviews, and its use, along with text mining, can reduce manual effort and reliance on humans and increase the efficiency of systematic literature reviews ( 10 , 16 ).…”

Section: Introductionmentioning

confidence: 99%

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

et al. 2023

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BackgroundManually keeping up-to-date with regulations such as directives, guidance, laws, and ordinances related to cell and gene therapy is a labor-intensive process. We used machine learning (ML) algorithms to create an augmented intelligent system to optimize systematic screening of global regulations to improve efficiency and reduce overall labor and missed regulations.MethodsCombining Boolean logic and artificial intelligence (i.e., augmented intelligence) for the search process, ML algorithms were used to identify and suggest relevant cell and gene therapy regulations. Suggested regulations were delivered to a landing page for further subject matter expert (SME) tagging of words/phrases to provide system relevance on functional words. Ongoing learning from the repository regulations continued to increase system reliability and performance. The automated ability to train and retrain the system allows for continued refinement and improvement of system accuracy. Automated daily searches for applicable regulations in global databases provide ongoing opportunities to update the repository.ResultsCompared to manual searching, which required 3–4 SMEs to review ~115 regulations, the current system performance, with continuous system learning, requires 1 full-time equivalent to process approximately 9,000 regulations/day. Currently, system performance has 86% overall accuracy, a recommend recall of 87%, and a reject recall of 84%. A conservative search strategy is intentionally used to permit SMEs to assess low-recommended regulations in order to prevent missing any applicable regulations.ConclusionCompared to manual searches, our custom automated search system greatly improves the management of cell and gene therapy regulations and is efficient, cost effective, and accurate.

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“…With the continuous advancement of intelligent manufacturing, technological reform and innovation continues to evolve and develop in the manufacturing industry, prompting the traditional inspection operation mode and operation method to face huge challenges [1]. At present, in order to ensure the safe and stable operation of the power system, the requirements for the reliability of the equipment are getting higher and higher, and the requirements for the inspection personnel are also getting higher and higher [2].…”

Section: Introductionmentioning

confidence: 99%

Application Research of Inspection System Based on Intelligent Wearable Device in Communication Power Inspection

Cheng

Yang

et al. 2022

J. Phys.: Conf. Ser.

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In order to effectively reduce the difficulty in communication power inspection and further improve the inspection efficiency of inspectors, this paper designs an inspection system based on smart wearable devices. Firstly, based on the analysis of the inspection task requirements of the communication power inspection, the overall structure of the wearable device intelligent inspection system is constructed, and the application of the wearable device intelligent inspection system in power engineering is studied; secondly, according to the inspection situation of the inspection personnel in the traditional inspection method, the key technical composition of the multi-functional inspection intelligent observation wearable lens is designed, and the key texture feature extraction technology of the communication power supply is used to locate the specific image target in the inspection process, and detect the local area. The abnormal sound is discharged, abnormal sound training and identification are completed, and intelligent inspection of the communication power supply is realized. Finally, it is verified by test experiments that the inspection system based on smart wearable devices designed in this paper can realize information transmission, task configuration, and data access in communication power inspection, with high work efficiency.

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Augmented Intelligence: Surveys of Literature and Expert Opinion to Understand Relations Between Human Intelligence and Artificial Intelligence

Cited by 23 publications

References 38 publications

Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

Augmenting fake content detection in online platforms: A domain adaptive transfer learning via adversarial training approach

Search, identification, and curation of cell and gene therapy product regulations using augmented intelligent systems

Application Research of Inspection System Based on Intelligent Wearable Device in Communication Power Inspection

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