Knowledge-based improvement: simulation and artificial intelligence for identifying and improving human decision-making in an operations system

Purpose While it is evident that the introduction of machine learning and the availability of big data have revolutionized various organizational operations and processes, existing academic and practitioner research within decision process literature has mostly ignored the nuances of these influences on human decision-making. Building on existing research in this area, this paper aims to define these concepts from a decision-making perspective and elaborates on the influences of these emerging technologies on human analytical and intuitive decision-making processes. Design/methodology/approach The authors first provide a holistic understanding of important drivers of digital transformation. The authors then conceptualize the impact that analytics tools built on artificial intelligence (AI) and big data have on intuitive and analytical human decision processes in organizations. Findings The authors discuss similarities and differences between machine learning and two human decision processes, namely, analysis and intuition. While it is difficult to jump to any conclusions about the future of machine learning, human decision-makers seem to continue to monopolize the majority of intuitive decision tasks, which will help them keep the upper hand (vis-à-vis machines), at least in the near future. Research limitations/implications The work contributes to research on rational (analytical) and intuitive processes of decision-making at the individual, group and organization levels by theorizing about the way these processes are influenced by advanced AI algorithms such as machine learning. Practical implications Decisions are building blocks of organizational success. Therefore, a better understanding of the way human decision processes can be impacted by advanced technologies will prepare managers to better use these technologies and make better decisions. By clarifying the boundaries/overlaps among concepts such as AI, machine learning and big data, the authors contribute to their successful adoption by business practitioners. Social implications The work suggests that human decision-makers will not be replaced by machines if they continue to invest in what they do best: critical thinking, intuitive analysis and creative problem-solving. Originality/value The work elaborates on important drivers of digital transformation from a decision-making perspective and discusses their practical implications for managers.

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How should we regulate artificial intelligence?

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2017.0360 ◽

2018 ◽

Vol 376 (2128) ◽

pp. 20170360 ◽

Cited By ~ 5

Author(s):

Chris Reed

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Decision Making ◽

Current System ◽

General System ◽

Fundamental Rights ◽

Ex Post ◽

Ex Ante ◽

Human Decision

Using artificial intelligence (AI) technology to replace human decision-making will inevitably create new risks whose consequences are unforeseeable. This naturally leads to calls for regulation, but I argue that it is too early to attempt a general system of AI regulation. Instead, we should work incrementally within the existing legal and regulatory schemes which allocate responsibility, and therefore liability, to persons. Where AI clearly creates risks which current law and regulation cannot deal with adequately, then new regulation will be needed. But in most cases, the current system can work effectively if the producers of AI technology can provide sufficient transparency in explaining how AI decisions are made. Transparency ex post can often be achieved through retrospective analysis of the technology's operations, and will be sufficient if the main goal is to compensate victims of incorrect decisions. Ex ante transparency is more challenging, and can limit the use of some AI technologies such as neural networks. It should only be demanded by regulation where the AI presents risks to fundamental rights, or where society needs reassuring that the technology can safely be used. Masterly inactivity in regulation is likely to achieve a better long-term solution than a rush to regulate in ignorance. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.

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Artificial Intelligence Inroads Into HR

10.4018/978-1-7998-8497-2.ch016 ◽

2022 ◽

pp. 231-246

Author(s):

Swati Bansal ◽

Monica Agarwal ◽

Deepak Bansal ◽

Santhi Narayanan

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Human Resources ◽

Generation Y ◽

Industry 4.0 ◽

Career Aspirations ◽

Work Life ◽

Human Decision ◽

The Right ◽

Talent Acquisition

Artificial intelligence is already here in all facets of work life. Its integration into human resources is a necessary process which has far-reaching benefits. It may have its challenges, but to survive in the current Industry 4.0 environment and prepare for the future Industry 5.0, organisations must penetrate AI into their HR systems. AI can benefit all the functions of HR, starting right from talent acquisition to onboarding and till off-boarding. The importance further increases, keeping in mind the needs and career aspirations of Generation Y and Z entering the workforce. Though employees have apprehensions of privacy and loss of jobs if implemented effectively, AI is the present and future. AI will not make people lose jobs; instead, it would require the HR people to upgrade their skills and spend their time in more strategic roles. In the end, it is the HR who will make the final decisions from the information that they get from the AI tools. A proper mix of human decision-making skills and AI would give organisations the right direction to move forward.

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Concepts and tools of artificial intelligence for human decision making

Acta Psychologica ◽

10.1016/0001-6918(88)90057-1 ◽

1988 ◽

Vol 68 (1-3) ◽

pp. 217-236 ◽

Cited By ~ 1

Author(s):

Anna Vari ◽

Janos Vecsenyi

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Human Decision

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A Review: Artificial Intelligent Approach for Enhancing Adaptability in an Adaptive E-Learning Environment

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/10.35940/ijeat.d2297.0410421 ◽

2021 ◽

Vol 10 (4) ◽

pp. 37-42

Author(s):

M.P.L. Perera

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Decision Making ◽

Fuzzy Logic ◽

Artificial Intelligent ◽

Human Decision ◽

Decision Making Processes ◽

E Learning ◽

Intelligent Approach ◽

Learning Frameworks

Adaptive e-learning the aim is to fill the gap between the pupil and the educator by discussing the needs and skills of individual learners. Artificial intelligence strategies that have the potential to simulate human decision-making processes are important around adaptive e-Learning. This paper explores the Artificial techniques; Fuzzy Logic, Neural Networks, Bayesian Networks and Genetic Algorithms, highlighting their contributions to the notion of the adaptability in the sense of Adaptive E-learning. The implementation of Artificial Neural Networks to resolve problems in the current Adaptive e-learning frameworks have been established.

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Artificial intelligence for production engineering: a historical approach

Robotica ◽

10.1017/s026357470001506x ◽

1987 ◽

Vol 5 (2) ◽

pp. 99-110 ◽

Cited By ~ 1

Author(s):

Igor Aleksander

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Visual Information ◽

Design Stage ◽

Production Engineering ◽

Television Camera ◽

Knowledge Based ◽

Work Cycle ◽

Programming Techniques ◽

Automated Decision Making

SUMMARYThis paper describes the principles of the advanced programming techniques often dubbed Artificial Intelligence involved in decision making as may be of some value in matters related to production engineering. Automated decision making in the context of production can adopt many aspects. At the most obvious level, a robot may have to plan a sequence of actions on the basis of signals obtained from changing conditions in its environment. These signals may, indeed, be quite complex, for example the input of visual information from a television camera.At another level, automated planning may be required to schedule the entire work cycle of a plant that includes many robots as well as other types of automated machinery. The often-quoted dark factory is an example of this, where not only some of the operations (such as welding) are done by robots, but also the transport of part-completed assemblies is automatically scheduled as a set of actions for autonomic transporters and cranes. It is common practice for this activity to be preprogrammed to the greatest detail. Automated decision making is aimed at adding flexibility to the process so that it can absolve the system designer from having to forsee every eventuality at the design stage.Frequent reference is made in this context to artificial intelligence (AI), knowledge-based and expert systems. Although these topics are more readily associated with computer science, it is the automated factory, in general, and the robot, in particular, that will benefit from success in these fields. In this part of the paper we try to sharpen up this perspective, while in part II we aim to discuss the history of artificial intelligence in this context. In part III we discuss the industrial prospects for the field.

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Instruction Modeling

Instruction Modeling ◽

10.1093/oso/9780190910709.003.0004 ◽

2020 ◽

pp. 78-106

Author(s):

George A. Khachatryan

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Expert Systems ◽

Classroom Teachers ◽

High Quality ◽

Teaching Expertise ◽

Intelligence System ◽

The Core ◽

Russian Mathematics ◽

Human Decision

This chapter describes the core ideas behind instruction modeling. A promising way to improve mathematics instruction is to import successful approaches from other countries; however, it is exceptionally difficult to do this, since instructional traditions are cultural and the volume of teaching expertise that needs to be transferred is vast. Computers offer a possible way to ease the barriers. Expert systems (invented c. 1970) are a type of artificial intelligence system that uses rules to mimic human decision-making. Following the pattern suggested by expert systems, an instruction modeler studies high-quality offline instruction and then designs computer programs that aim to recreate this instruction. Many important activities cannot be automated, and therefore instruction modeling is necessarily blended learning: some instruction takes place online, while other activities are led by classroom teachers. To illustrate these ideas, this chapter describes several instruction modeling programs created by Reasoning Mind. It also discusses Russian mathematics education, explaining why it is a successful instructional tradition and a suitable choice for instruction modeling.

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