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'.

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

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.

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

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.

scholarly journals Going Beyond Panaceas: The Diversity of Land Observatory Forms in Africa

Land ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 70 ◽  
Author(s):  
Quentin Grislain ◽  
Jeremy Bourgoin ◽  
Ward Anseeuw ◽  
Perrine Burnod ◽  
Eva Hershaw ◽  
...  
Keyword(s):  
Decision Making ◽  
Participant Observation ◽  
Video Conferencing ◽  
Analytical Framework ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Information Production ◽  
Dynamic Data ◽  
Ex Post ◽  
Ex Ante

In recent decades, mechanisms for observation and information production have proliferated in an attempt to meet the growing needs of stakeholders to access dynamic data for the purposes of informed decision-making. In the land sector, a growing number of land observatories are producing data and ensuring its transparency. We hypothesize that these structures are being developed in response to the need for information and knowledge, a need that is being driven by the scale and diversity of land issues. Based on the results of a study conducted on land observatories in Africa, this paper presents existing and past land observatories on the continent and proposes to assess their diversity through an analysis of core dimensions identified in the literature. The analytical framework was implemented through i) an analysis of existing literature on land observatories, ii) detailed assessments of land observatories based on semi-open interviews conducted via video conferencing, iii) fieldwork and visits to several observatories, and iv) participant observation through direct engagement and work at land observatories. We emphasize that the analytical framework presented here can be used as a tool by land observatories to undertake ex-post self-evaluations that take the observatory’s trajectory into account, or in the case of proposed new land observatories, to undertake ex-ante analyses and design the pathway towards the intended observatory.

scholarly journals The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

2021 ◽  
Vol 20 ◽  
pp. 153303382110163
Author(s):  
Danju Huang ◽  
Han Bai ◽  
Li Wang ◽  
Yu Hou ◽  
Lan Li ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Systematic Review ◽  
Neural Networks ◽  
Decision Making ◽  
Deep Learning ◽  
Radiation Oncology ◽  
Clinical Work ◽  
Radiation Oncologists ◽  
Basic Concepts ◽  
Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

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.

DEONTOLOGY, THRESHOLDS, AND EFFICIENCY

Legal Theory ◽  
2011 ◽  
Vol 17 (4) ◽  
pp. 301-317 ◽  
Author(s):  
Christopher T. Wonnell
Keyword(s):  
Decision Making ◽  
Degrees Of Freedom ◽  
Moral Judgments ◽  
Rule Following ◽  
Threshold Functions ◽  
Ex Post ◽  
Ex Ante ◽  
The Many ◽  
The Relationship ◽  
Public Decision

This article explores four topics raised by Eyal Zamir and Barak Medina's treatment of constrained deontology. First, it examines whether mathematical threshold functions are the proper way to think about limits on deontology, given the discontinuities of our moral judgments and the desired phenomenology of rule-following. Second, it asks whether constrained deontology is appropriate for public as well as private decision-making, taking issue with the book's conclusion that deontological options are inapplicable to public decision-making, whereas deontological constraints are applicable. Third, it examines the issue of the relationship between deontology and efficiency, asking whether deontological constraints should yield in situations where everyone would expect to benefit from their suspension, either ex ante or ex post. Finally, the article concludes that constrained deontology is susceptible to political abuse because of the many degrees of freedom involved in identifying constrained actions and the point at which those constraints yield to consequentialist benefits.

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.

Long-Term Decision-Making: Biological and Psychological Evidence

2009 ◽  
Vol 9 (3) ◽  
pp. 9-19 ◽  
Author(s):  
Thomas Princen
Keyword(s):  
Decision Making ◽  
Time Horizon ◽  
Cultural Factors ◽  
Structural Factors ◽  
Short Term ◽  
Environmental Decision Making ◽  
Human Decision ◽  
Psychological Evidence ◽  
Widespread Belief

A central conundrum in the need to infuse a long-term perspective into climate policy and other environmental decision-making is the widespread belief that humans are inherently short-term thinkers. An analysis of human decision-making informed by evolved adaptations—biological, psychological and cultural—suggests that humans actually have a long-term thinking capacity. In fact, the human time horizon encompasses both the immediate and the future (near and far term). And yet this very temporal duality makes people susceptible to manipulation; it carries its own politics, a politics of the short term. A “legacy politics” would extend the prevailing time horizon by identifying structural factors that build on evolved biological and cultural factors.

Artificial Intelligence, Smart Classrooms and Online Education in the 21st Century

2019 ◽  
Vol 21 (3) ◽  
pp. 66-79 ◽  
Author(s):  
Ikedinachi A. P. WOGU ◽  
Sanjay Misra ◽  
Patrick A. Assibong ◽  
Esther Fadeke Olu-Owolabi ◽  
Rytis Maskeliūnas ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Online Education ◽  
Human Development ◽  
21St Century ◽  
Education Sector ◽  
Factor Method ◽  
Ex Post ◽  
The Impact ◽  
Smart Classrooms

The advent of artificial intelligence (AI) technology in the education sector has largely taken over conventional classrooms and revolutionized the way education is conducted to the admiration of many. Other scholars however, believe that such early celebration of AI benefits is unfounded and inimical to the education sector since the adoption of modern AI teaching systems now raises long-term issues about the relevance of teachers and their classrooms in 21st Century AI education. The Marxian Alienation Theory was adopted for the article. The Ex-post factor method and Derrida's critical method of analysis was utilized for attaining the objectives of the article. The article faults recent attempts at eulogizing the impact of AI innovations in the education sector and on human development. Extensive research is proposed as necessary for contemporary scholars of AI and education technologist before proper appropriation can be made about its gains in education and on human development.

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