Governing by Humans, Not by Robots

2021 ◽  
pp. 116-134
Author(s):  
George Gantzias
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Business Intelligence ◽  
Business Organizations ◽  
Business Systems ◽  
Digital Capitalism ◽  
Digital Era ◽  
Human Decision ◽  
A Company

Artificial intelligence and robots together with fake news have challenged irrevocably not only the traditional business organizations and representative democracy but also the role of regulatory mechanisms in digital capitalism. In 2020, companies will need to develop a new culture (i.e., the business intelligence culture[BIC]) in order to understand that human resources, currently one of the lowest rungs in a company ladder, will be elevated to the same position as research and development. This chapter examines and analyses artificial intelligence, robots, and human decision-making process together with the role of automatic decision-making algorithms in business systems. It considers critical questions regarding global regulation, ethical standards, public interest, and democracy. It examines the need for regulation in digital capitalism. Finally, it outlines the models business intelligence culture (BIC) and collective will democracy (CWD) as methodological tools to analyze humans and robots' governance in the digital era.

Who’s making the decisions? How managers can harness artificial intelligence and remain in charge

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pooya Tabesh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Digital Transformation ◽  
Decision Processes ◽  
Decision Makers ◽  
Content Type ◽  
Human Decision ◽  
Practical Implications

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.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

scholarly journals How should we regulate artificial intelligence?

2018 ◽  
Vol 376 (2128) ◽  
pp. 20170360 ◽  
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'.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Artificial Intelligence Inroads Into HR

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.

The Contribution of Obeya for Business Intelligence

2021 ◽  
pp. 244-269
Author(s):  
Gonçalo Sousa ◽  
José Carlos Sá ◽  
Gilberto Santos ◽  
Francisco J. G. Silva ◽  
Luís Pinto Ferreira
Keyword(s):  
Decision Making ◽  
Business Intelligence ◽  
Performance Indicators ◽  
Paradigm Shift ◽  
Normal Functioning ◽  
Important Data ◽  
Planning Team ◽  
Weekly Meeting ◽  
Data Update ◽  
A Company

The main objective of the study is to minimize interdepartmental communication, potentiation of fast and efficient decision making, and computerization of data. Using software such as MS Excel® and MS Power BI®, a Power BI® tool was conceived to be capable of incorporating, for the entire company, the dashboards that collect the main KPIs of each department. After the tool was implemented, the company's paradigm shift was noticeable. Quickly, the weekly meeting of the planning team began to take place using the MS Power BI® dashboard. In this way, processes were automated and the important data for the normal functioning of the company became accessible to all departments, thus minimizing interdepartmental communication. The chapter shows an Obeya Digital that was implemented in a company in which all the performance indicators of each department are incorporated. In this way, information becomes accessible to all employees and manual data update processes are minimized.

Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence

Organization ◽  
2019 ◽  
Vol 26 (5) ◽  
pp. 655-672 ◽  
Author(s):  
Verena Bader ◽  
Stephan Kaiser
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
User Interface ◽  
User Involvement ◽  
Traditional View ◽  
Telecommunications Company

Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans’ attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans’ decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.

scholarly journals FUZZY LOGIC AND ARTIFICIAL INTELLIGENCE TOOLS IN THE CONTEXT OF TOURISM ANALYSIS

2020 ◽  
pp. 601-617
Author(s):  
Simona Hašková ◽  
◽  
Jakub Horák ◽  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Fuzzy Approach ◽  
Managerial Decision ◽  
Managerial Decision Making ◽  
The Czech Republic ◽  
Travel And Tourism ◽  
Tourism Sector ◽  
Qualitative Nature

Qualitative and quantitative approaches to multicriteria evaluation and managerial decision- making often ignore the specifics of the role of the human factor. This article summarizes management methods that reflect not only numerical inputs but also data of a qualitative nature while considering their applicability in the tourism sector. Some of them can be assorted within the classes of Artificial intelligence. The focus is on the fuzzy approach at the theoretical and application level. The fuzzy approach is used to evaluate the degree of country travel and tourism competitiveness of selected European and Asian countries based on subjective rankings from the viewpoint of travelling persons. The results indicate that among countries under review, China is ranked as a highly competitive country in travel & tourism. Conditional competitive countries in terms of travel & tourism are the Czech Republic, Pakistan, Russia, and Turkey.

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