scholarly journals Administrative Decisions in the Era of Artificial Intelligence

2020 ◽  
Vol 11 ◽  
pp. 253-271
Author(s):  
Mateusz Pszczyński
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Public Administration ◽  
Rapid Development ◽  
The State ◽  
Economic Life ◽  
Decision Makers ◽  
Administrative Discretion ◽  
Building Trust

The rapid development of cybernetics allows the use of artificial intelligence in many areas of social and economic life. The State can also harness algorithms and machine learning for its actions. Automatic decision making should be one of the stages in the development and improvement of public administration. While it is easy to implement these solutions in the case of related decisions, decisions made under administrative discretion, general clauses or valuation standards pose a challenge. The correct transformation of paper-based public administration into automatic public administration requires a change in decision makers’ thinking, the introduction of new solutions, and building trust in artificial intelligence. Therefore, new solutions have to be built in accordance with the principles of transparency, accountability, equality, goodness and justice. Artificial intelligence making automatic decisions on behalf of the State must be a tool to support the execution of public tasks concerning citizens which is based on trust towards AI and public administration.

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.

Artificial Intelligence for Decision Makers

2019 ◽  
pp. 0000-0000
Author(s):  
Viktor Elliot ◽  
Mari Paananen ◽  
Miroslaw Staron
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Accounting Data ◽  
Ethical Concerns ◽  
Key Concepts ◽  
Basic Understanding ◽  
Visualization Tools

We propose an exercise with the purpose of providing a basic understanding of key concepts within AI and extending the understanding of AI beyond mathematics. The exercise allows participants to carry out analysis based on accounting data using visualization tools as well as to develop their own machine learning algorithms that can mimic their decisions. Finally, we also problematize the use of AI in decision-making, with such aspects as biases in data and/or ethical concerns.

Artificial Intelligence in Utilitarian vs. Hedonic Contexts: The “Word-of-Machine” Effect

2020 ◽  
pp. 002224292095734
Author(s):  
Chiara Longoni ◽  
Luca Cian
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Natural Language Processing ◽  
Transaction Costs ◽  
Language Processing ◽  
Word Of Mouth ◽  
Rapid Development ◽  
Policy Makers ◽  
Trade Offs

Rapid development and adoption of AI, machine learning, and natural language processing applications challenge managers and policy makers to harness these transformative technologies. In this context, the authors provide evidence of a novel “word-of-machine” effect, the phenomenon by which utilitarian/hedonic attribute trade-offs determine preference for, or resistance to, AI-based recommendations compared with traditional word of mouth, or human-based recommendations. The word-of-machine effect stems from a lay belief that AI recommenders are more competent than human recommenders in the utilitarian realm and less competent than human recommenders in the hedonic realm. As a consequence, importance or salience of utilitarian attributes determine preference for AI recommenders over human ones, and importance or salience of hedonic attributes determine resistance to AI recommenders over human ones (Studies 1–4). The word-of machine effect is robust to attribute complexity, number of options considered, and transaction costs. The word-of-machine effect reverses for utilitarian goals if a recommendation needs matching to a person’s unique preferences (Study 5) and is eliminated in the case of human–AI hybrid decision making (i.e., augmented rather than artificial intelligence; Study 6). An intervention based on the consider-the-opposite protocol attenuates the word-of-machine effect (Studies 7a–b).

About some of the factors for public administration effectiveness

2020 ◽  
Vol 22 (1) ◽  
pp. 26-32
Author(s):  
ALIKHAN М. BAIMENOV ◽  
Keyword(s):  
Public Administration ◽  
Corporate Culture ◽  
Work Experience ◽  
Information Technologies ◽  
Selection Process ◽  
Rapid Development ◽  
The State ◽  
Information Flows ◽  
Factors Affecting ◽  
State Apparatus

The article emphasizes that modern governments, working in unique socio-economic, historical and cultural conditions, must take into account global trends, as well as the growth of citizens’ expectations associated with the rapid development of information technologies and other factors. In such circumstances, special attention is paid to the effectiveness of public administration. The article discusses some of the significant factors impacting the effectiveness of public administration, such as the professionalization of the state apparatus, the legibility of the institutional framework, the optimization of information flows and corporate culture. In accordance with this, on the basis of work experience in the public administration system and analysis of civil service reforms in the countries of the region, the main challenges and possible solutions are shown. In the professionalization of the state apparatus, the importance of the merit principles in the selection and promotion stages of personnel through the empowerment of human resource (HR) management services, the integrity of tools and approaches at all stages of selection process, and the responsibility of the selection board are noted. The author focuses on the need to ensure a balance of powers, responsibility and resources, delimitation of powers between political and administrative civil servants, optimization of information flows. Particular importance is paid to corporate culture, which is one of the main factors affecting the efficiency of the state apparatus. It is noted that central values of corporate culture and leadership in state bodies of the countries of our region, along with generally accepted in the modern leadership theory, should be respect for the dignity, work and time of employees.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Artificial intelligence and machine learning in design of mechanical materials

2021 ◽  
Author(s):  
Kai Guo ◽  
Zhenze Yang ◽  
Chi-Hua Yu ◽  
Markus J. Buehler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
State Of The Art ◽  
The State

This review revisits the state of the art of research efforts on the design of mechanical materials using machine learning.

Machine Learning and Artificial Intelligence for Surgical Decision Making

2021 ◽  
Vol 22 (6) ◽  
pp. 626-634
Author(s):  
Saskya Byerly ◽  
Lydia R. Maurer ◽  
Alejandro Mantero ◽  
Leon Naar ◽  
Gary An ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals AIoT Application for Food Recommendation based on Emotion Analysis

2021 ◽  
pp. 44-53
Author(s):  
Shivangi Ruhela ◽  
Pragati Chaudhary ◽  
Rishija Shrivas ◽  
Deepti Chopra
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Data Exchange ◽  
The Internet ◽  
Human Intervention ◽  
User Interactions ◽  
Emotion Analysis ◽  
Efficiency Performance ◽  
Analyze Data

Artificial Intelligence(AI) and Internet of Things(IoT) are popular domains in Computer Science. AIoT converges AI and IoT, thereby applying AI into IoT. When ‘things’ are programmed and connected to the Internet, IoT comes into place. But when these IoT systems, can analyze data and have decision-making potential without human intervention, AIoT is achieved. AI powers IoT through Decision-Making and Machine Learning, IoT powers AI through data exchange and connectivity. With the AI’s brain and IoT’s body, the systems can have shot-up efficiency, performance and learning from user interactions. Some studies show that, by 2022, AIoT devices such as drones to save rainforests or fully automated cars, would be ruling the computer industries. The paper discusses AIoT at a greater depth, focuses on few case studies of AIoT for better understanding on practical levels, and lastly, proposes an idea for a model which suggests food through emotion analysis.

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