45 found

Year:

  1.  2
    “Super-Intelligent” Machine: Technological Exuberance or the Road to Subjection.Peter Brödner - 2018 - AI and Society 33 (3):335-346.
    Looking back on the development of computer technology, particularly in the context of manufacturing, we can distinguish three big waves of technological exuberance with a wave length of roughly 30 years: In the first wave, during the 1950s, mainframe computers at that time were conceptualized as “electronic brains” and envisaged as central control unit of an “automatic factory”. Thirty years later, during the 1980s, knowledge-based systems in computer-integrated manufacturing were adored as the computational core of the “unmanned factory”. Both waves (...)
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  2.  4
    Symbiosis or Assimilation: Critical Reflections on the Ontological Self at the Precipice of Total Data.Peter J. Carew - 2018 - AI and Society 33 (3):357-368.
    Contemporary data practices are inducing a convergent saturation point wherein every human action, reaction, interaction, transaction, thought or desire is quantified, reified, recorded and used. Physical or virtual, all is recorded, known or unknown, seen or unseen, until data permeates every facet of our shared human existence. The implications of this eventuality are potentially so far reaching that the very notion or concept of who we are might be fundamentally altered, resulting in new ontologies of the self in a world (...)
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  3.  6
    Digital Innovation and the Fourth Industrial Revolution: Epochal Social Changes?Loris Caruso - 2018 - AI and Society 33 (3):379-392.
    ITC technologies have come to comprehensively represent images and expectations of the future. Hopes of ongoing progress, economic growth, skill upgrading and possibly also democratisation are attached to new ICTs as well as fears of totalitarian control, alienation, job loss and insecurity. Currently, with the terms "Industry 4.0." and ‘Fourth Industrial Revolution”, public institutions, private institutions, and literature refer to the inchoate transformation of production of goods and services resulting from the application of a new wave of technological innovations: interconnected (...)
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  4.  3
    Economic Language and Economy Change: With Implications for Cyber-Physical Systems.Alan Cottey - 2018 - AI and Society 33 (3):323-333.
    The implementation of cyber-physical and similar systems depends on prevailing social and economic conditions. It is here argued that, if the effect of these technologies is to be benign, the current neo-liberal economy must change to a radically more cooperative model. In this paper, economy change means a thorough change to a qualitatively different kind of economy. It is contrasted with economic change, which is the kind of minor change usually considered in mainstream discourse. The importance of language is emphasised, (...)
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  5.  6
    What is Wrong About Robocops as Consultants? A Technology-Centric Critique of Predictive Policing.Martin Degeling & Bettina Berendt - 2018 - AI and Society 33 (3):347-356.
    Fighting crime has historically been a field that drives technological innovation, and it can serve as an example of different governance styles in societies. Predictive policing is one of the recent innovations that covers technical trends such as machine learning, preventive crime fighting strategies, and actual policing in cities. However, it seems that a combination of exaggerated hopes produced by technology evangelists, media hype, and ignorance of the actual problems of the technology may have boosted sales of software that supports (...)
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  6.  5
    Digitalisation and Employment in Manufacturing.Daniela Freddi - 2018 - AI and Society 33 (3):393-403.
    The present work tackles the issue of the effects of digitalisation on employment. This issue has been attracting a growing interest, in particular because of the anxiety generated by the idea that digital technologies could cancel a large number of jobs. Although I agree with argument put forward in opposition to the existence of a causal link between technological innovation and increased productivity at the macroeconomic level, I believe that the novelty and pervasiveness of digital technologies require more in-depth micro-level (...)
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  7.  6
    Cyber-Physical System.Francesco Garibaldo & Emilio Rebecchi - 2018 - AI and Society 33 (3):299-311.
  8.  4
    Nothing but a Human.Francesco Garibaldo & Emilio Rebecchi - 2018 - AI and Society 33 (3):313-321.
    The dream of the perpetual motion charms us since millennia, the desire of machines substituting men was present already in the imperial China and the classical Rome; the medieval alchemists tried to build automata, automata showed up in the Renaissance princes’ plays. In the Aladdin fable, the sorcerer satisfies on the instant all wishes of the lamp’s owner. In other words, the fiction of omnipotence accompanies humanity from the very beginning. Is God omnipotent? So, why not humanity? Building automatic factories, (...)
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  9.  8
    From Industry 4.0 to Society 4.0, There and Back.Tatiana Mazali - 2018 - AI and Society 33 (3):405-411.
    The new industrial paradigm Industry 4.0, or smart industry, is at the core of contemporary debates. The public debate on Industry 4.0 typically offers two main perspectives: the technological one and the one about industrial policies. On the contrary, the discussion on the social and organizational effects of the new paradigm is still underdeveloped. The article specifically examines this aspect, and analyzes the change that workers are subject to, along with the work organization, smart digital factories. The study originates from (...)
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  10.  10
    An Overview of Work Analysis Instruments for Hybrid Production Workplaces.Sarah L. Müller, Mohammad A. Shehadeh, Stefan Schröder, Anja Richert & Sabina Jeschke - 2018 - AI and Society 33 (3):425-432.
    With increasing technological improvements, production processes are becoming more and more automated. Nevertheless, full automation is improbable in the medium term since human abilities cannot yet be completely replaced. Therefore, it is likely that so-called hybrid human–robot teams will assume the future production. This raises questions regarding the shaping of future production and the effects it will have on the employees, workstations, and the companies as a whole. The project “Work in the Industry of the Future” addresses the entirely new (...)
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  11.  6
    The Fourth Industrial Revolution and Implications for Innovative Cluster Policies.Sang-Chul Park - 2018 - AI and Society 33 (3):433-445.
    The Fourth Industrial Revolution has become a global buzz word since the World Economic Forum adopted it as an annual issue in 2016. It is represented by hyper automation and hyper connectivity based on artificial intelligence, big data, robotics, and Internet of things. AI, big data, and robotics can contribute to developing hyper automation that can increase productivity and intensify industrial production. Particularly, robots using AI can make decision by themselves as human being on complicated processes. Along with the hyper (...)
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  12.  5
    Cyberculture, Symbiosis, and Syncretism.Luís Moniz Pereira - 2018 - AI and Society 33 (3):447-452.
    The impact of Cyberculture, of digital devices on young people as extensions of the body, can be seen in terms of the decreasing structuring of thoughts and information, increasing impulsivity in perception and action, and the development of more primitive defense mechanisms. These adverse impacts result in the feeling of isolation and devaluation, frustration of present and uncertainty of the future, exteriorization and floating identities, mimetic and adhesive identifications, less cohesion of the self, and decreasing tolerance of the other. This (...)
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  13.  11
    Anthropomorphism in Social Robotics: Empirical Results on Human–Robot Interaction in Hybrid Production Workplaces.Anja Richert, Sarah Müller, Stefan Schröder & Sabina Jeschke - 2018 - AI and Society 33 (3):413-424.
    New forms of artificial intelligence on the one hand and the ubiquitous networking of “everything with everything” on the other hand characterize the fourth industrial revolution. This results in a changed understanding of human–machine interaction, in new models for production, in which man and machine together with virtual agents form hybrid teams. The empirical study “Socializing with robots” aims to gain insight especially into conditions of development and processes of hybrid human–machine teams. In the experiment, human–robot actions and interactions were (...)
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  14.  2
    Digitalisation and the Regulation of Work: Theoretical Issues and Normative Challenges.Angelo Salento - 2018 - AI and Society 33 (3):369-378.
    This paper presents an introductory overview of the main issues that the digitalisation of industrial enterprises known as Industry 4.0 raises for social sciences. First, it will show that this technological transition—which, however, is unfinished and is seen to be in continuity with the so-called “third industrial revolution”—cannot be interpreted with reference to a deterministic approach. It can be analysed more usefully as a range of decisions affecting the industrial policies of national states, the conception and design of machines, their (...)
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  15.  8
    Environmental Monitoring Using a Robotized Wireless Sensor Network.Sevil A. Ahmed, Vasil L. Popov, Andon V. Topalov & Nikola G. Shakev - 2018 - AI and Society 33 (2):207-214.
    Big cities and growing industrial areas bring high risk of different kinds of pollutions which would implicate to the quality of life of the society. Discovering and monitoring of polluted areas using autonomous mobile robots is nowadays a frequently considered solution concerning both environmental and human safety problems. Being part of a distributed control system, such robots can help to improve the efficiency of the existing conventional pollution prevention systems. On the other hand, during the last decade, wireless sensor networks (...)
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  16.  7
    Fuzzy Modelling and Model Reference Neural Adaptive Control of the Concentration in a Chemical Reactor.M. Bahita & K. Belarbi - 2018 - AI and Society 33 (2):189-196.
    This simulation study is a fuzzy model-based neural network control method. The basic idea is to consider the application of a special type of neural networks based on radial basis function, which belongs to a class of associative memory neural networks. The novelty of this approach is the use of an RBF neural network controller in a model reference adaptive control architecture, based on a one-step-ahead Takagi–Sugeno fuzzy model. The objective is to control the concentration in a continuous stirred-tank reactor (...)
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  17.  6
    Environment Change, Economy Change and Reducing Conflict at Source.A. Cottey - 2018 - AI and Society 33 (2):215-228.
    At a time when fossil fuel burning, nationalism, ethnic and religious intolerance, and other retrograde steps are being promoted, the prospects for world peace and environmental systems stability may appear dim. Exactly because of this is it the more important to continue to examine the sources of conflict. A major obstacle to general progress is the currently dominant economic practice and theory, which is here called the economy-as-usual, or economics-as-usual, as appropriate. A special obstacle to constructive change is the language (...)
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  18.  11
    3D Metal Printing Technology: The Need to Re-Invent Design Practice.Thomas Duda & L. Venkat Raghavan - 2018 - AI and Society 33 (2):241-252.
    3D printing or additive manufacturing is a novel method of manufacturing parts directly from digital model using layer-by-layer material build-up approach. This tool-less manufacturing method can produce fully dense metallic parts in short time, with high precision. Features of additive manufacturing like freedom of part design, part complexity, light weighting, part consolidation, and design for function are garnering particular interests in metal additive manufacturing for aerospace, oil and gas, marine, and automobile applications. Powder bed fusion, in which each powder bed (...)
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  19.  6
    Cluster Consensus in Multi-Agent Networks with Mutual Information Exchange.Ö Feyza Erkan & Mehmet Akar - 2018 - AI and Society 33 (2):197-205.
    The emergence of new technologies such as the Internet of things and the Cloud transforms the way we interact. Whether it be human to human interaction or human to machine interaction, the size of the networks keeps growing. As the networks get more complex nowadays with many interconnected components, it is necessary to develop distributed scalable algorithms so as to minimize the computation required in decision making in such large-scale systems. In this paper, we consider a setup where each agent (...)
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  20.  10
    Mitigating Risks of Digitalization Through Managed Industrial Security Services.Christoph Jansen & Sabina Jeschke - 2018 - AI and Society 33 (2):163-173.
    Digitalization has become a cornerstone of competitiveness in the industrial arena, especially in the cases of small lot sizes with many variants in the goods produced. Managers of industrial facilities have to handle the complexity that comes along with Industry 4.0 in diverse dimensions to leverage the potentials of digitalization for their sites. This article describes major drivers of this complexity in current industrial automation to outline the environment of today’s challenges for managers of this technical transition—and shows how managed (...)
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  21.  11
    Internet of Things and Big Data: The Disruption of the Value Chain and the Rise of New Software Ecosystems.Norbert Jesse - 2018 - AI and Society 33 (2):229-239.
    IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction between (...)
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  22.  4
    GDP Growth Vs. Criminal Phenomena: Data Mining of Japan 1926–2013.Xingan Li, Henry Joutsijoki, Jorma Laurikkala & Martti Juhola - 2018 - AI and Society 33 (2):261-274.
    The aim of this article is to inquire about potential relationship between change of crime rates and change of gross domestic product growth rate, based on historical statistics of Japan. This national-level study used a dataset covering 88 years and 13 attributes. The data were processed with the self-organizing map, separation power checked by our ScatterCounter method, assisted by other clustering methods and statistical methods for obtaining comparable results. The article is an exploratory application of the SOM in research of (...)
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  23.  10
    System-Thinking Approach to the Applied Interaction Between Transport and Economy.Ylber Limani - 2018 - AI and Society 33 (2):275-289.
    This paper discusses the correlation balance of transport as a dynamic system and the economic growth of specific regions and countries expressed in gross domestic product. The contemporary transformation processes of the input resources to the desired outcomes need new intelligent approaches based on new information system techniques. These research determinations are specifically focused on achieving the objective of providing with the analyses concerned with giving a more estimated answer to some of the complex questions related to the economic dynamics, (...)
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  24.  15
    In Mod We Trust? Human Trust, Bitcoin, and the Burning Waste of Time.David Morris - 2018 - AI and Society 33 (2):291-292.
  25.  6
    Re-Approaching Fuzzy Cognitive Maps to Increase the Knowledge of a System.Vassiliki Mpelogianni & Peter P. Groumpos - 2018 - AI and Society 33 (2):175-188.
    Fuzzy cognitive maps is a system modeling methodology which applies mostly in complex dynamic systems by describing causal relationships that exist between its parameters called concepts. Fuzzy cognitive map theories have been used in many applications but they present several drawbacks and deficiencies. These limitations are addressed and analyzed fuzzy cognitive map theories are readdressed. A new novel approach in modelling fuzzy cognitive maps is proposed to increase the knowledge of the system and overcome some of its limitations. The state (...)
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  26.  5
    International Stability in a Digital World: Emerging Trends in Machine Intelligence, Environmental Sustainability and Society.Larry Stapleton - 2018 - AI and Society 33 (2):159-162.
  27.  3
    Reflections on Networks, Human Behaviour, and Social Dynamics in the Digital Age.Theodore Tsekeris, Charalambos Tsekeris & Ioannis Katerelos - 2018 - AI and Society 33 (2):253-260.
    This article offers a critical discussion in the form of debate among experts in the fields of networks, human behaviour, and social analysis about key issues that arguably affect the human nature and society in the digital age. Based on the responses of Nicholas Christakis to an interview given to the authors, some key questions, applications, and limitations regarding the research on digital networks are discussed, together with hot issues related to the nature of digital data and experimentation in contemporary (...)
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  28.  6
    Where and When AI and CI Meet: Exploring the Intersection of Artificial and Collective Intelligence Towards the Goal of Innovating How We Govern.Stefaan G. Verhulst - 2018 - AI and Society 33 (2):293-297.
  29.  4
    On the Need of Opening Up Crowdsourced Emergency Management Systems.Marco Avvenuti, Stefano Cresci, Fabio Del Vigna & Maurizio Tesconi - 2018 - AI and Society 33 (1):55-60.
    Nowadays, social media analysis systems are feeding on user contributed data, either for beneficial purposes, such as emergency management, or for user profiling and mass surveillance. Here, we carry out a discussion about the power and pitfalls of public accessibility to social media-based systems, with specific regards to the emergency management application EARS. We investigate whether opening such systems to the population at large would further strengthen the link between communities of volunteer citizens, intelligent systems, and decision makers, thus going (...)
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  30.  8
    Community Digital Storytelling for Collective Intelligence: Towards a Storytelling Cycle of Trust.Sarah Copeland & Aldo de Moor - 2018 - AI and Society 33 (1):101-111.
    Digital storytelling has become a popular method for curating community, organisational, and individual narratives. Since its beginnings over 20 years ago, projects have sprung up across the globe, where authentic voice is found in the narration of lived experiences. Contributing to a Collective Intelligence for the Common Good, the authors of this paper ask how shared stories can bring impetus to community groups to help identify what they seek to change, and how digital storytelling can be effectively implemented in community (...)
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  31.  5
    Community Media 4 Kenya: A Partnership Approach to Building Collective Intelligence.Peter Day - 2018 - AI and Society 33 (1):81-89.
  32.  7
    Rat Running the G20: Collective Intelligence for Navigating the Disrupted City.Carlos Estrada-Grajales, Peta Mitchell, Marcus Foth & Christine Satchell - 2018 - AI and Society 33 (1):133-146.
  33.  5
    Bigger Data, Less Wisdom: The Need for More Inclusive Collective Intelligence in Social Service Provision.Alexander Fink - 2018 - AI and Society 33 (1):61-70.
    Social service organizations have long used data in their efforts to support people in need for the purposes of advocacy, tracking, and intervention. Increasingly, such organizations are joining forces to provide wrap-around services to clients in order to “move the needle” on intractable social problems. Groups using these strategies, called Collective Impact, develop shared metrics to guide their work, sharing data, finances, infrastructure, and services. A major emphasis of these efforts is on tracking clients and measuring impacts. This study explores (...)
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  34.  5
    Design for Collective Intelligence: Pop-Up Communities in MOOCs.Muriel Garreta-Domingo, Peter B. Sloep, Davinia Hérnandez-Leo & Yishay Mor - 2018 - AI and Society 33 (1):91-100.
  35.  13
    Pop-Up Political Advocacy Communities on Reddit.Com: SandersForPresident and The Donald.Richard A. Mills - 2018 - AI and Society 33 (1):39-54.
    This paper explores two reddit communities that supported Bernie Sanders and Donald Trump, respectively, in the run up to the 2016 US Presidential election campaign. Much of the paper is dedicated to explaining how reddit functions, describing the behaviour of the subreddit communities in question and then asking whether these demonstrated collective intelligence. Subreddit communities submit and vote on content, through their votes they make collective decisions about which content will be broadcast to their community. Large subreddit communities that formed (...)
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  36.  14
    Can Small Groups Avoid the Tragedy of the Commons?Rogerio Scabim Morano, Edmilson Alves de Moraes & Rafael Ricardo Jacomossi - 2018 - AI and Society 33 (1):71-80.
    In this research, an agent-based simulation seeks to discuss the tragedy of the commons, collective intelligence and institutions developed by social groups. The concept of the tragedy of the commons states that you can always expect environmental degradation when many individuals freely exploit a scarce resource of common use. Hardin proposes two alternatives to deal with it: state or privatized administration. However, it is possible another alternative of self-coordination when the social groups are small. That is, the tragedy of the (...)
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  37.  3
    Creativity and Design to Articulate Difference in the Conflicted City: Collective Intelligence in Bogota’s Grassroots Organisations.Leonardo Parra-Agudelo, Jaz Hee-Jeong Choi, Marcus Foth & Carlos Estrada - 2018 - AI and Society 33 (1):147-158.
    This paper presents a critical reflection on insights into the ongoing endeavours for community engagement by Ayara and MAL; two urban grassroot organisations in Bogota, Colombia, where a long history of internal conflicts has resulted in diverse human right violations. The paper presents examples of the grassroots organisations’ unique methods of engagement that promotes building collective intelligence from the bottom–up through creative collaboration and design processes, leading to rebuilding social fabrics that support the common good for the people of Bogota.
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  38.  9
    Collective Intelligence for Promoting Changes in Behaviour: A Case Study on Energy Conservation.Lara S. G. Piccolo, Anna De Liddo, Gregoire Burel, Miriam Fernandez & Harith Alani - 2018 - AI and Society 33 (1):15-25.
  39.  9
    DoGood: Examining Gamification, Civic Engagement, and Collective Intelligence.Sebastian Rehm, Marcus Foth & Peta Mitchell - 2018 - AI and Society 33 (1):27-37.
    The mobile internet provides new and easier ways for people to organise themselves, raise issues, take action, and interact with their city. However, lack of information or motivation often prevents citizens from regularly contributing to the common good. In this paper, we present DoGood, a mobile app that aims at motivating citizens to join civic activities in their local community. Our study asks to what extent gamification can motivate users to participate in civic activities. The term civic activity is not (...)
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  40.  18
    Coordination Technology for Active Support Networks: Context, Needfinding, and Design.Stanley J. Rosenschein & Todd Davies - 2018 - AI and Society 33 (1):113-123.
    Coordination is a key problem for addressing goal–action gaps in many human endeavors. We define interpersonal coordination as a type of communicative action characterized by low interpersonal belief and goal conflict. Such situations are particularly well described as having collectively “intelligent”, “common good” solutions, viz., ones that almost everyone would agree constitute social improvements. Coordination is useful across the spectrum of interpersonal communication—from isolated individuals to organizational teams. Much attention has been paid to coordination in teams and organizations. In this (...)
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  41.  6
    Collective Intelligence for the Common Good: Cultivating the Seeds for an Intentional Collaborative Enterprise.Douglas Schuler, Anna De Liddo, Justin Smith & Fiorella De Cindio - 2018 - AI and Society 33 (1):1-13.
  42.  7
    The Positioning Cards: On Affect, Public Design, and the Common.Maurizio Teli, Antonella De Angeli & Maria Menéndez-Blanco - 2018 - AI and Society 33 (1):125-132.
    In this paper, we present a design tool, the positioning cards that we have developed, validated, and used in different projects. These cards are built to allow CI4CG and Participatory Design researchers to discuss the political alignment of design projects, in iterative processes of design involving people in the definition of the technological features to be implemented. The background of the cards is the conceptualization of contemporary participatory design as public design, engaging with societally relevant phenomena outside the traditional environment (...)
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  43.  12
    Promoting Inequality? Self-Monitoring Applications and the Problem of Social Justice.Katrin Paldan, Hanno Sauer & Nils-Frederic Wagner - 2018 - AI and Society:1-11.
    When it comes to improving the health of the general population, mHealth technologies with self-monitoring and intervention components hold a lot of promise. We argue, however, that due to various factors such as access, targeting, personal resources or incentives, self-monitoring applications run the risk of increasing health inequalities, thereby creating a problem of social justice. We review empirical evidence for “intervention-generated” inequalities, present arguments that self-monitoring applications are still morally acceptable, and develop approaches to avoid the promotion of health inequalities (...)
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  44. Classification of Global Catastrophic Risks Connected with Artificial Intelligence.Alexey Turchin & Denkenberger David - 2018 - AI and Society.
    A classification of the global catastrophic risks of AI is presented, along with a comprehensive list of previously identified risks. This classification allows the identification of several new risks. We show that at each level of AI’s intelligence power, separate types of possible catastrophes dominate. Our classification demonstrates that the field of AI risks is diverse, and includes many scenarios beyond the commonly discussed cases of a paperclip maximizer or robot-caused unemployment. Global catastrophic failure could happen at various levels of (...)
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  45.  19
    Machine Learning, Inductive Reasoning, and Reliability of Generalisations.Petr Spelda - 2018 - AI and Society:1-9.
    The present paper shows how statistical learning theory and machine learning models can be used to enhance understanding of AI-related epistemological issues regarding inductive reasoning and reliability of generalisations. Towards this aim, the paper proceeds as follows. First, it expounds Price’s dual image of representation in terms of the notions of e-representations and i-representations that constitute subject naturalism. For Price, this is not a strictly anti-representationalist position but rather a dualist one (e- and i-representations). Second, the paper links this debate (...)
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