Integrating intuition and artificial intelligence in organizational decision-making

Vincent, Vinod

doi:10.1016/j.bushor.2021.02.008

Cited by 36 publications

(22 citation statements)

References 39 publications

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“…In contrast (Jarrahi, 2018), Mulgan (2018) and also Lehner et al (2021) already foresee a shift towards partly autonomous digital actors as decision-makers in the future. Research on ethical issues associated with AI-based decision-making (Dignum, 2018;Leyer and Schneider, 2021;Vincent, 2021), its related algorithms (Martin, 2019b) and Big Data (Lombardi et al, 2015) in the (broader) fields of accounting and auditing however remains scarce apart from a few recent highlights that provide substance and a future research agenda (Gunz and Thorne, 2020;Kellogg et al, 2020;Marrone and Hazelton, 2019). Munoko's et al (2020) insights on ethics in an AI-based auditing shall be emphasised here as example.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking

Lehner

Ittonen²,

Silvola³

et al. 2022

AAAJ

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PurposeThis paper aims to identify ethical challenges of using artificial intelligence (AI)-based accounting systems for decision-making and discusses its findings based on Rest's four-component model of antecedents for ethical decision-making. This study derives implications for accounting and auditing scholars and practitioners.Design/methodology/approachThis research is rooted in the hermeneutics tradition of interpretative accounting research, in which the reader and the texts engage in a form of dialogue. To substantiate this dialogue, the authors conduct a theoretically informed, narrative (semi-systematic) literature review spanning the years 2015–2020. This review's narrative is driven by the depicted contexts and the accounting/auditing practices found in selected articles are used as sample instead of the research or methods.FindingsIn the thematic coding of the selected papers the authors identify five major ethical challenges of AI-based decision-making in accounting: objectivity, privacy, transparency, accountability and trustworthiness. Using Rest's component model of antecedents for ethical decision-making as a stable framework for our structure, the authors critically discuss the challenges and their relevance for a future human–machine collaboration within varying agency between humans and AI.Originality/valueThis paper contributes to the literature on accounting as a subjectivising as well as mediating practice in a socio-material context. It does so by providing a solid base of arguments that AI alone, despite its enabling and mediating role in accounting, cannot make ethical accounting decisions because it lacks the necessary preconditions in terms of Rest's model of antecedents. What is more, as AI is bound to pre-set goals and subjected to human made conditions despite its autonomous learning and adaptive practices, it lacks true agency. As a consequence, accountability needs to be shared between humans and AI. The authors suggest that related governance as well as internal and external auditing processes need to be adapted in terms of skills and awareness to ensure an ethical AI-based decision-making.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking

Lehner

Ittonen²,

Silvola³

et al. 2022

AAAJ

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“…AI is viewed as a game-changer and as being able to facilitate both the “ abilities to self learn and a race to improve decision quality ” (Vincent, 2021 , p. 425). Kaplan and Haenlein ( 2019 ) defined AI “ as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation ” (p.17).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Kaplan and Haenlein ( 2019 ) defined AI “ as a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation ” (p.17). In supply chain management and the manufacturing industry, there has been an upsurge in AI (Kumar et al, 2019 ) that has significantly impacted operations and human roles in firms (Vincent, 2021 ; Awan et al, 2021 ) suggested that AI initiatives in firm supply chain operations can improve knowledge of the processes used to generate business performance. AI is a complex and multifaceted construct with profound implications for firm operations management (Zeba et al, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Still, it rarely addresses how AI is applied in the OM field, such as in manufacturing, production, warehousing and logistics, and robot dynamics (Toorajipour et al, 2021 ). Even though the supply chain literature has acknowledged that many AI applications include production forecasting, supplier selection, material consumption forecasting, and customer segmentation (Toorajipour et al, 2021 ), the AI literature typically revolves around understanding effective ways to combine human intuition and decision-making (Vincent, 2021 ). The use of AI technologies gives marketers a competitive edge that reflects marketing tactics and customer behaviors (Jabbar et al, 2020 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

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“…However, since the feature nodes are initialized in a completely random way for width learning, there may be a large number of ine cient node generation and a certain amount of feature information missing [29,30]. To solve this problem and to learn the vector representation better, the denoising self-encoder adds noise to the original data in order to solve this problem: noisy(x, y)…”

Section: Arti Cial Intelligence Decision-making Platformmentioning

confidence: 99%

Big Data Analysis Technology for Artificial Intelligence Decision-Making Platform Construction and Application

Liu

2022

Mobile Information Systems

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With the development of the information age, it is the opportunity and challenge for enterprises to apply big data analysis technology to make decisions and better solve the major problems of global and sustainable development. Decision-making is crucial for enterprises, and a correct decision can improve the development potential and competitiveness of enterprises. However, traditional decision models have certain limitations, and it is difficult to handle the massive, polymorphic, and changing decision data. To address these problems, we propose a combination of temporal data anomaly detection and width learning big data analysis technology for building an intelligent decision platform to assist enterprises to better solve major decision problems. First, the goal of time series data anomaly detection is to correctly determine whether the data points in each moment of the time series are abnormal. The variation of time series data is affected by various factors, and the fluctuation of data caused by some nonanomalous factors can increase the difficulty of anomaly detection. To address the above problems, we propose an anomaly detection algorithm for time series data based on time series decomposition method. In this algorithm, the time series are decomposed by STL method and HP filtering method according to whether the time series is periodic or not, and then the components of the time series that are relevant to anomaly detection are retained and anomaly detection is performed on the processed time series using a cyclic model. Then, based on the call text and signaling data in enterprise decision-making, an improved width learning model called coding width learning is proposed. The coding width learning model is used to identify decision problems and make comprehensive decisions to improve the model training time and the accuracy of identification. At the same time, an integrated learning method with parallelized training is proposed for width learning in order to further improve the efficiency of coding width learning and prevent the potential memory explosion problem. Finally, the experimental results show that the proposed anomaly detection method effectively improves the anomaly detection performance of the model, and its performance is better than the existing time series anomaly detection algorithm based on variation self-encoder; combined with the improved width learning model, it can make fast decisions and analysis without using scenarios and targets, and help and guide the work of related personnel.

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Integrating intuition and artificial intelligence in organizational decision-making

Cited by 36 publications

References 39 publications

Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking

Artificial intelligence based decision-making in accounting and auditing: ethical challenges and normative thinking

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Big Data Analysis Technology for Artificial Intelligence Decision-Making Platform Construction and Application

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