In AI we trust? Perceptions about automated decision-making by artificial intelligence

The integration of internet of things, artificial intelligence, and blockchain enabled the monitoring of structural health with unattended and automated means. Remote monitoring mandates intelligent automated decision-making capability, which is still absent in present solutions. The proposed solution in this chapter contemplates the architecture of smart sensors, customized for individual structures, to regulate the monitoring of structural health through stress, strain, and bolted joints looseness. Long range sensors are deployed for transmitting the messages a longer distance than existing techniques. From the simulated results, different sensors record the monitoring information and transmit to the blockchain platform in terms of pressure points, temperature, pre-tension force, and the architecture deems the criticality of transactions. Blockchain platform will also be responsible for storage and accessibility of information from a decentralized medium, automation, and security.

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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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Integration of Artificial Intelligence into Dempster Shafer Theory: A Review on Decision Making in Condition Monitoring

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.773-774.154 ◽

2015 ◽

Vol 773-774 ◽

pp. 154-157 ◽

Cited By ~ 4

Author(s):

Muhammad Firdaus Rosli ◽

Lim Meng Hee ◽

M. Salman Leong

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Condition Monitoring ◽

Support Vector ◽

Dempster Shafer Theory ◽

Basic Probability ◽

Human Interpretation ◽

The Individual ◽

Shafer Theory ◽

Automated Decision Making

Machines are the heart of most industries. By ensuring the health of machines, one could easily increase the company revenue and eliminates any safety threat related to machinery catastrophic failures. In condition monitoring (CM), questions often arise during decision making time whether the machine is still safe to run or not? Traditional CM approach depends heavily on human interpretation of results whereby decision is made solely based on the individual experience and knowledge about the machines. The advent of artificial intelligence (AI) and automated ways for decision making in CM provides a more objective and unbiased approach for CM industry and has become a topic of interest in the recent years. This paper reviews the techniques used for automated decision making in CM with emphasis given on Dempster-Shafer (D-S) evident theory and other basic probability assignment (BPA) techniques such as support vector machine (SVM) and etc.

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Sustainability challenges of Artificial Intelligence and Policy Implications

Ökologisches Wirtschaften - Fachzeitschrift ◽

10.14512/oewo360136 ◽

2021 ◽

Vol 36 (O1) ◽

pp. 36-40

Author(s):

Friederike Rohde ◽

Maike Gossen ◽

Josephin Wagner ◽

Tilman Santarius

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Sustainable Development ◽

Sustainable Development Goals ◽

Policy Implications ◽

Development Goals ◽

Sustainability Challenges ◽

Automated Decision Making

Automated decision-making based on Artificial Intelligence is associated with growing expectations and is to contribute to sustainable development goals. Which opportunities and risks for the environment, economy and society are associated with Artificial Intelligence-based applications and how can they be governed?

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From Poisons to Antidotes: Algorithms as Democracy Boosters

European Journal of Risk Regulation ◽

10.1017/err.2021.57 ◽

2022 ◽

pp. 1-25

Author(s):

Paolo Cavaliere ◽

Graziella Romeo

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Political Institutions ◽

Theoretical Framework ◽

Traditional Authority ◽

Political Processes ◽

Viable Solution ◽

Decision Making Processes ◽

Automated Decision Making ◽

Public Decision

Abstract Under what conditions can artificial intelligence contribute to political processes without undermining their legitimacy? Thanks to the ever-growing availability of data and the increasing power of decision-making algorithms, the future of political institutions is unlikely to be anything similar to what we have known throughout the last century, possibly with parliaments deprived of their traditional authority and public decision-making processes largely unaccountable. This paper discusses and challenges these concerns by suggesting a theoretical framework under which algorithmic decision-making is compatible with democracy and, most relevantly, can offer a viable solution to counter the rise of populist rhetoric in the governance arena. Such a framework is based on three pillars: (1) understanding the civic issues that are subjected to automated decision-making; (2) controlling the issues that are assigned to AI; and (3) evaluating and challenging the outputs of algorithmic decision-making.

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Towards Transparency by Design for Artificial Intelligence

Science and Engineering Ethics ◽

10.1007/s11948-020-00276-4 ◽

2020 ◽

Vol 26 (6) ◽

pp. 3333-3361

Author(s):

Heike Felzmann ◽

Eduard Fosch-Villaronga ◽

Christoph Lutz ◽

Aurelia Tamò-Larrieux

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Design Principles ◽

Lessons Learned ◽

Practical Application ◽

Privacy By Design ◽

Ex Ante ◽

Practical Guidance ◽

Automated Decision Making

AbstractIn this article, we develop the concept of Transparency by Design that serves as practical guidance in helping promote the beneficial functions of transparency while mitigating its challenges in automated-decision making (ADM) environments. With the rise of artificial intelligence (AI) and the ability of AI systems to make automated and self-learned decisions, a call for transparency of how such systems reach decisions has echoed within academic and policy circles. The term transparency, however, relates to multiple concepts, fulfills many functions, and holds different promises that struggle to be realized in concrete applications. Indeed, the complexity of transparency for ADM shows tension between transparency as a normative ideal and its translation to practical application. To address this tension, we first conduct a review of transparency, analyzing its challenges and limitations concerning automated decision-making practices. We then look at the lessons learned from the development of Privacy by Design, as a basis for developing the Transparency by Design principles. Finally, we propose a set of nine principles to cover relevant contextual, technical, informational, and stakeholder-sensitive considerations. Transparency by Design is a model that helps organizations design transparent AI systems, by integrating these principles in a step-by-step manner and as an ex-ante value, not as an afterthought.

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Discrimination in the age of artificial intelligence

AI & Society ◽

10.1007/s00146-021-01192-2 ◽

2021 ◽

Author(s):

Bert Heinrichs

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Recent Literature ◽

Moral Deliberation ◽

Philosophical Debate ◽

Epistemic Opacity ◽

Definition Of ◽

Automated Decision Making ◽

Common Understanding

AbstractIn this paper, I examine whether the use of artificial intelligence (AI) and automated decision-making (ADM) aggravates issues of discrimination as has been argued by several authors. For this purpose, I first take up the lively philosophical debate on discrimination and present my own definition of the concept. Equipped with this account, I subsequently review some of the recent literature on the use AI/ADM and discrimination. I explain how my account of discrimination helps to understand that the general claim in view of the aggravation of discrimination is unwarranted. Finally, I argue that the use of AI/ADM can, in fact, increase issues of discrimination, but in a different way than most critics assume: it is due to its epistemic opacity that AI/ADM threatens to undermine our moral deliberation which is essential for reaching a common understanding of what should count as discrimination. As a consequence, it turns out that algorithms may actually help to detect hidden forms of discrimination.

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Artificial Intelligence: the right to protection from discrimination caused by algorithms, machine learning and automated decision-making

ERA Forum ◽

10.1007/s12027-019-00582-w ◽

2019 ◽

Vol 20 (4) ◽

pp. 585-598

Author(s):

Robin Allen ◽

Dee Masters

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

The Right ◽

Automated Decision Making

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Beyond Tools and Procedures

10.4018/978-1-7998-8467-5.ch002 ◽

2022 ◽

pp. 16-23

Author(s):

Ivana Bartoletti ◽

Lucia Lucchini

Keyword(s):

Artificial Intelligence ◽

Decision Making ◽

Court Cases ◽

Daily Lives ◽

Viable Approach ◽

Almost All ◽

Automated Decision Making ◽

Business Framework

As artificial intelligence (AI) is increasingly being deployed in almost all aspects of our daily lives, the discourse around the pervasiveness of algorithmic tools and automated decision-making appears to be almost a trivial one. This chapter investigates limits and opportunities within existing debates and examines the rapidly evolving legal landscape and recent court cases. The authors suggest that a viable approach to fairness, which ultimately remains a choice that organizations have to make, could be rooted in a new measurable and accountable responsible business framework.

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