AI and Society

ISSNs: 0951-5666, 1425-5655

107 found

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  1.  5
    Tensions in transparent urban AI: designing a smart electric vehicle charge point.Kars Alfrink, Ianus Keller, Neelke Doorn & Gerd Kortuem - 2023 - AI and Society 38 (3):1049-1065.
    The increasing use of artificial intelligence (AI) by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent (...)
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  2.  3
    Urban AI depends: the need for (wider) urban strategies.Alessandro Aurigi - 2023 - AI and Society 38 (3):1245-1247.
  3.  9
    The emergence and evolution of urban AI.Michael Batty - 2023 - AI and Society 38 (3):1045-1048.
  4.  5
    Everyday data cultures: beyond Big Critique and the technological sublime.Jean Burgess - 2023 - AI and Society 38 (3):1243-1244.
  5.  3
    Street surface condition of wealthy and poor neighborhoods: the case of Los Angeles.Pooyan Doozandeh, Limeng Cui & Rui Yu - 2023 - AI and Society 38 (3):1185-1192.
    Are wealthy neighborhoods visually more attractive than poorer neighborhoods? Past studies provided a positive answer to this question for characteristics such as green space and visible pollution. The condition of streets is one of the characteristics that can not only contribute to neighborhoods’ aesthetics, but can also affect residents’ health and mobility. In this study, we investigate whether street condition of wealthy neighborhoods is different from poorer neighborhoods. We resolved the difficulty of data collection using a dataset that utilized artificial (...)
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  6.  10
    Watch out! Cities as data engines.Fabio Duarte & Barbro Fröding - 2023 - AI and Society 38 (3):1249-1250.
  7.  7
    Cyclists and autonomous vehicles at odds.Alexander Gaio & Federico Cugurullo - 2023 - AI and Society 38 (3):1223-1237.
    Consequential historical decisions that shaped transportation systems and their influence on society have many valuable lessons. The decisions we learn from and choose to make going forward will play a key role in shaping the mobility landscape of the future. This is especially pertinent as artificial intelligence (AI) becomes more prevalent in the form of autonomous vehicles (AVs). Throughout urban history, there have been cyclical transport oppressions of previous-generation transportation methods to make way for novel transport methods. These cyclical oppressions (...)
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  8.  4
    The system of autono‑mobility: computer vision and urban complexity—reflections on artificial intelligence at urban scale.Fabio Iapaolo - 2023 - AI and Society 38 (3):1111-1122.
    Focused on city-scale automation, and using self-driving cars (SDCs) as a case study, this article reflects on the role of AI—and in particular, computer vision systems used for mapping and navigation—as a catalyst for urban transformation. Urban research commonly presents AI and cities as having a one-way cause-and-effect relationship, giving undue weight to AI’s impact on cities and overlooking the role of cities in shaping AI. Working at the intersection of data science and social research, this paper aims to counter (...)
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  9.  3
    Understanding citizen perceptions of AI in the smart city.Anu Lehtiö, Maria Hartikainen, Saara Ala-Luopa, Thomas Olsson & Kaisa Väänänen - 2023 - AI and Society 38 (3):1123-1134.
    Artificial intelligence (AI) is embedded in a wide variety of Smart City applications and infrastructures, often without the citizens being aware of the nature of their “intelligence”. AI can affect citizens’ lives concretely, and thus, there may be uncertainty, concerns, or even fears related to AI. To build acceptable futures of Smart Cities with AI-enabled functionalities, the Human-Centered AI (HCAI) approach offers a relevant framework for understanding citizen perceptions. However, only a few studies have focused on clarifying the citizen perceptions (...)
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  10.  6
    Katherine Crawford: Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence.Aale Luusua - 2023 - AI and Society 38 (3):1257-1259.
  11.  6
    Urban AI: understanding the emerging role of artificial intelligence in smart cities.Aale Luusua, Johanna Ylipulli, Marcus Foth & Alessandro Aurigi - 2023 - AI and Society 38 (3):1039-1044.
  12.  2
    Advancing residents’ use of shared spaces in Nordic superblocks with intelligent technologies.Jouko Makkonen, Rita Latikka, Laura Kaukonen, Markus Laine & Kaisa Väänänen - 2023 - AI and Society 38 (3):1167-1184.
    To support the sustainability of future cities, residents’ living spaces need to be built and used efficiently, while supporting residents’ communal wellbeing. Nordic superblock is a new planning, housing, and living concept in which residents of a neighborhood—a combination of city blocks—share yards, common spaces and utilities. Sharing living spaces is an essential element of this approach. In this study, our goal was to study the ways in which intelligent technology solutions—such as proactive, data-driven Artificial Intelligence (AI) applications—could support and (...)
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  13.  1
    Human–machine coordination in mixed traffic as a problem of Meaningful Human Control.Giulio Mecacci, Simeon C. Calvert & Filippo Santoni de Sio - 2023 - AI and Society 38 (3):1151-1166.
    The urban traffic environment is characterized by the presence of a highly differentiated pool of users, including vulnerable ones. This makes vehicle automation particularly difficult to implement, as a safe coordination among those users is hard to achieve in such an open scenario. Different strategies have been proposed to address these coordination issues, but all of them have been found to be costly for they negatively affect a range of human values (e.g. safety, democracy, accountability…). In this paper, we claim (...)
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  14.  7
    We have to talk about emotional AI and crime.Lena Podoletz - 2023 - AI and Society 38 (3):1067-1082.
    Emotional AI is an emerging technology used to make probabilistic predictions about the emotional states of people using data sources, such as facial (micro)-movements, body language, vocal tone or the choice of words. The performance of such systems is heavily debated and so are the underlying scientific methods that serve as the basis for many such technologies. In this article I will engage with this new technology, and with the debates and literature that surround it. Working at the intersection of (...)
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  15.  3
    Time to re-humanize algorithmic systems.Minna Ruckenstein - 2023 - AI and Society 38 (3):1241-1242.
  16.  5
    Contestations in urban mobility: rights, risks, and responsibilities for Urban AI.Nitin Sawhney - 2023 - AI and Society 38 (3):1083-1098.
    Cities today are dynamic urban ecosystems with evolving physical, socio-cultural, and technological infrastructures. Many contestations arise from the effects of inequitable access and intersecting crises currently faced by cities, which may be amplified by the algorithmic and data-centric infrastructures being introduced in urban contexts. In this article, I argue for a critical lens into how inter-related urban technologies, big data and policies, constituted as Urban AI, offer both challenges and opportunities. I examine scenarios of contestations in _urban mobility_, defined broadly (...)
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  17.  6
    All knowledge is not smart: racial and environmental injustices within legacies of smart cities.Hira Sheikh - 2023 - AI and Society 38 (3):1251-1252.
  18.  8
    The Polyopticon: a diagram for urban artificial intelligences.Stephanie Sherman - 2023 - AI and Society 38 (3):1209-1222.
    Smart city discourses often invoke the Panopticon, a disciplinary architecture designed by Jeremy Bentham and popularly theorized by Michel Foucault, as a model for understanding the social impact of AI technologies. This framing focuses attention almost exclusively on the negative ramifications of Urban AI, correlating ubiquitous surveillance, centralization, and data consolidation with AI development, and positioning technologies themselves as the driving factor shaping privacy, sociality, equity, access, and autonomy in the city. This paper describes an alternative diagram for Urban AI—the (...)
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  19.  2
    Assemblage thinking as a methodology for studying urban AI phenomena.Yu-Shan Tseng - 2023 - AI and Society 38 (3):1099-1110.
    This paper seeks to bypass assumptions that researchers in critical algorithmic studies and urban studies find it difficult to study algorithmic systems due to their black-boxed nature. In addition, it seeks to work against the assumption that advocating for transparency in algorithms is, therefore, the key for achieving an enhanced understanding of the role of algorithmic technologies on modern life. Drawing on applied assemblage thinking via the concept of the urban assemblage, I demonstrate how the notion of urban assemblage can (...)
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  20.  4
    Urban-semantic computer vision: a framework for contextual understanding of people in urban spaces.Anthony Vanky & Ri Le - 2023 - AI and Society 38 (3):1193-1207.
    Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common (Ridgeway 2018, Coifman et al. 1998, Sun et al. 2020). Despite the often-discussed biases in the algorithms' training and unequally borne benefits (Khosla et al. 2012), almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these (...)
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  21.  5
    Will AI end privacy? How do we avoid an Orwellian future.Toby Walsh - 2023 - AI and Society 38 (3):1239-1240.
  22.  11
    Artificial intelligence in local governments: perceptions of city managers on prospects, constraints and choices.Tan Yigitcanlar, Duzgun Agdas & Kenan Degirmenci - 2023 - AI and Society 38 (3):1135-1150.
    Highly sophisticated capabilities of artificial intelligence (AI) have skyrocketed its popularity across many industry sectors globally. The public sector is one of these. Many cities around the world are trying to position themselves as leaders of urban innovation through the development and deployment of AI systems. Likewise, increasing numbers of local government agencies are attempting to utilise AI technologies in their operations to deliver policy and generate efficiencies in highly uncertain and complex urban environments. While the popularity of AI is (...)
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  23.  5
    Federico Cugurullo (2021): Frankenstein Urbanism: Eco, Smart and Autonomous Cities, Artificial Intelligence and the End of the City.Johanna Ylipulli - 2023 - AI and Society 38 (3):1253-1255.
  24.  52
    Posthuman perception of artificial intelligence in science fiction: an exploration of Kazuo Ishiguro’s Klara and the Sun.A. K. Ajeesh & S. Rukmini - 2023 - AI and Society 38 (2):853-860.
    Our fascination with artificial intelligence (AI), robots and sentient machines has a long history, and references to such humanoids are present even in ancient myths and folklore. The advancements in digital and computational technology have turned this fascination into apprehension, with the machines often being depicted as a binary to the human. However, the recent domains of academic enquiry such as transhumanism and posthumanism have produced many a literature in the genre of science fiction (SF) that endeavours to alter this (...)
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  25.  6
    From the ground up: developing a practical ethical methodology for integrating AI into industry.Marc M. Anderson & Karën Fort - 2023 - AI and Society 38 (2):631-645.
    In this article we present a new approach to practical artificial intelligence (AI) ethics in heavy industry, which was developed in the context of an EU Horizons 2020 multi partner project. We begin with a review of the concept of Industry 4.0, discussing the limitations of the concept, and of iterative categorization of heavy industry generally, for a practical human centered ethical approach. We then proceed to an overview of actual and potential AI ethics approaches to heavy industry, suggesting that (...)
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  26.  5
    COVID-19, artificial intelligence, ethical challenges and policy implications.Muhammad Anshari, Mahani Hamdan, Norainie Ahmad, Emil Ali & Hamizah Haidi - 2023 - AI and Society 38 (2):707-720.
    As the COVID-19 outbreak remains an ongoing issue, there are concerns about its disruption, the level of its disruption, how long this pandemic is going to last, and how innovative technological solutions like Artificial Intelligence (AI) and expert systems can assist to deal with this pandemic. AI has the potential to provide extremely accurate insights for an organization to make better decisions based on collected data. Despite the numerous advantages that may be achieved by AI, the use of AI can (...)
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  27.  20
    Training philosopher engineers for better AI.Brian Ball & Alexandros Koliousis - 2023 - AI and Society 38 (2):861-868.
    There is a deluge of AI-assisted decision-making systems, where our data serve as proxy to our actions, suggested by AI. The closer we investigate our data (raw input, or their learned representations, or the suggested actions), we begin to discover “bugs”. Outside of their test, controlled environments, AI systems may encounter situations investigated primarily by those in other disciplines, but experts in those fields are typically excluded from the design process and are only invited to attest to the ethical features (...)
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  28.  4
    From AI for people to AI for the world and the universe.Seth D. Baum & Andrea Owe - 2023 - AI and Society 38 (2):679-680.
    Recent work in AI ethics often calls for AI to advance human values and interests. The concept of “AI for people” is one notable example. Though commendable in some respects, this work falls short by excluding the moral significance of nonhumans. This paper calls for a shift in AI ethics to more inclusive paradigms such as “AI for the world” and “AI for the universe”. The paper outlines the case for more inclusive paradigms and presents implications for moral philosophy and (...)
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  29.  5
    Evidence-based AI, ethics and the circular economy of knowledge.Caterina Berbenni-Rehm - 2023 - AI and Society 38 (2):889-895.
    Everything we do in life involves a connection with information, experience and know-how: together these represent the most valuable of intangible human assets encompassing our history, cultures and wisdom. However, the more easily new technologies gather information, the more we are confronted with our limited capacity to distinguish between what is essential, important or merely ‘nice-to-have’. This article presents the case study of a multilingual Knowledge Management System, the Business enabling e-Platform that gathers and protects tacit knowledge, as the key (...)
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  30.  13
    When Something Goes Wrong: Who is Responsible for Errors in ML Decision-making?Andrea Berber & Sanja Srećković - 2023 - AI and Society 38 (2):1-13.
    Because of its practical advantages, machine learning (ML) is increasingly used for decision-making in numerous sectors. This paper demonstrates that the integral characteristics of ML, such as semi-autonomy, complexity, and non-deterministic modeling have important ethical implications. In particular, these characteristics lead to a lack of insight and lack of comprehensibility, and ultimately to the loss of human control over decision-making. Errors, which are bound to occur in any decision-making process, may lead to great harm and human rights violations. It is (...)
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  31.  9
    Cognitive architectures for artificial intelligence ethics.Steve J. Bickley & Benno Torgler - 2023 - AI and Society 38 (2):501-519.
    As artificial intelligence (AI) thrives and propagates through modern life, a key question to ask is how to include humans in future AI? Despite human involvement at every stage of the production process from conception and design through to implementation, modern AI is still often criticized for its “black box” characteristics. Sometimes, we do not know what really goes on inside or how and why certain conclusions are met. Future AI will face many dilemmas and ethical issues unforeseen by their (...)
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  32.  10
    An explanation space to align user studies with the technical development of Explainable AI.Garrick Cabour, Andrés Morales-Forero, Élise Ledoux & Samuel Bassetto - 2023 - AI and Society 38 (2):869-887.
    Providing meaningful and actionable explanations for end-users is a situated problem requiring the intersection of multiple disciplines to address social, operational, and technical challenges. However, the explainable artificial intelligence community has not commonly adopted or created tangible design tools that allow interdisciplinary work to develop reliable AI-powered solutions. This paper proposes a formative architecture that defines the explanation space from a user-inspired perspective. The architecture comprises five intertwined components to outline explanation requirements for a task: (1) the end-users’ mental models, (...)
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  33.  2
    Will Big Data and personalized medicine do the gender dimension justice?Antonio Carnevale, Emanuela A. Tangari, Andrea Iannone & Elena Sartini - 2023 - AI and Society 38 (2):829-841.
    Over the last decade, humans have produced each year as much data as were produced throughout the entire history of humankind. These data, in quantities that exceed current analytical capabilities, have been described as “the new oil,” an incomparable source of value. This is true for healthcare, as well. Conducting analyses of large, diverse, medical datasets promises the detection of previously unnoticed clinical correlations and new diagnostic or even therapeutic possibilities. However, using Big Data poses several problems, especially in terms (...)
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  34.  3
    Fairness & friends in the data science era.Barbara Catania, Giovanna Guerrini & Chiara Accinelli - 2023 - AI and Society 38 (2):721-731.
    The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should deal with data responsibly, guaranteeing nondiscrimination (...)
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  35.  4
    Applying ethics to AI in the workplace: the design of a scorecard for Australian workplace health and safety.Andreas Cebulla, Zygmunt Szpak, Catherine Howell, Genevieve Knight & Sazzad Hussain - 2023 - AI and Society 38 (2):919-935.
    Artificial Intelligence (AI) is taking centre stage in economic growth and business operations alike. Public discourse about the practical and ethical implications of AI has mainly focussed on the societal level. There is an emerging knowledge base on AI risks to human rights around data security and privacy concerns. A separate strand of work has highlighted the stresses of working in the gig economy. This prevailing focus on human rights and gig impacts has been at the expense of a closer (...)
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  36.  21
    Trust and ethics in AI.Hyesun Choung, Prabu David & Arun Ross - 2023 - AI and Society 38 (2):733-745.
    With the growing influence of artificial intelligence (AI) in our lives, the ethical implications of AI have received attention from various communities. Building on previous work on trust in people and technology, we advance a multidimensional, multilevel conceptualization of trust in AI and examine the relationship between trust and ethics using the data from a survey of a national sample in the U.S. This paper offers two key dimensions of trust in AI—human-like trust and functionality trust—and presents a multilevel conceptualization (...)
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  37.  6
    A principle-based approach to AI: the case for European Union and Italy.Francesco Corea, Fabio Fossa, Andrea Loreggia, Stefano Quintarelli & Salvatore Sapienza - 2023 - AI and Society 38 (2):521-535.
    As Artificial Intelligence (AI) becomes more and more pervasive in our everyday life, new questions arise about its ethical and social impacts. Such issues concern all stakeholders involved in or committed to the design, implementation, deployment, and use of the technology. The present document addresses these preoccupations by introducing and discussing a set of practical obligations and recommendations for the development of applications and systems based on AI techniques. With this work we hope to contribute to spreading awareness on the (...)
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  38.  3
    Detecting racial inequalities in criminal justice: towards an equitable deep learning approach for generating and interpreting racial categories using mugshots.Rahul Kumar Dass, Nick Petersen, Marisa Omori, Tamara Rice Lave & Ubbo Visser - 2023 - AI and Society 38 (2):897-918.
    Recent events have highlighted large-scale systemic racial disparities in U.S. criminal justice based on race and other demographic characteristics. Although criminological datasets are used to study and document the extent of such disparities, they often lack key information, including arrestees’ racial identification. As AI technologies are increasingly used by criminal justice agencies to make predictions about outcomes in bail, policing, and other decision-making, a growing literature suggests that the current implementation of these systems may perpetuate racial inequalities. In this paper, (...)
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  39.  18
    Responsibility of AI Systems.Mehdi Dastani & Vahid Yazdanpanah - 2023 - AI and Society 38 (2):843-852.
    To support the trustworthiness of AI systems, it is essential to have precise methods to determine what or who is to account for the behaviour, or the outcome, of AI systems. The assignment of responsibility to an AI system is closely related to the identification of individuals or elements that have caused the outcome of the AI system. In this work, we present an overview of approaches that aim at modelling responsibility of AI systems, discuss their advantages and shortcomings to (...)
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  40.  5
    Toward trustworthy programming for autonomous concurrent systems.Lavindra de Silva & Alan Mycroft - 2023 - AI and Society 38 (2):963-965.
  41.  5
    AI4People or People4AI? On human adaptation to AI at work.Emma Engstrom & Karim Jebari - 2023 - AI and Society 38 (2):967-968.
  42.  5
    Social influence for societal interest: a pro-ethical framework for improving human decision making through multi-stakeholder recommender systems.Matteo Fabbri - 2023 - AI and Society 38 (2):995-1002.
    In the contemporary digital age, recommender systems (RSs) play a fundamental role in managing information on online platforms: from social media to e-commerce, from travels to cultural consumptions, automated recommendations influence the everyday choices of users at an unprecedented scale. RSs are trained on users’ data to make targeted suggestions to individuals according to their expected preference, but their ultimate impact concerns all the multiple stakeholders involved in the recommendation process. Therefore, whilst RSs are useful to reduce information overload, their (...)
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  43.  4
    Enhancing human agency through redress in Artificial Intelligence Systems.Rosanna Fanni, Valerie Eveline Steinkogler, Giulia Zampedri & Jo Pierson - 2023 - AI and Society 38 (2):537-547.
    Recently, scholars across disciplines raised ethical, legal and social concerns about the notion of human intervention, control, and oversight over Artificial Intelligence (AI) systems. This observation becomes particularly important in the age of ubiquitous computing and the increasing adoption of AI in everyday communication infrastructures. We apply Nicholas Garnham's conceptual perspective on mediation to users who are challenged both individually and societally when interacting with AI-enabled systems. One way to increase user agency are mechanisms to contest faulty or flawed AI (...)
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  44.  6
    Investing in AI for social good: an analysis of European national strategies.Francesca Foffano, Teresa Scantamburlo & Atia Cortés - 2023 - AI and Society 38 (2):479-500.
    Artificial Intelligence (AI) has become a driving force in modern research, industry and public administration and the European Union (EU) is embracing this technology with a view to creating societal, as well as economic, value. This effort has been shared by EU Member States which were all encouraged to develop their own national AI strategies outlining policies and investment levels. This study focuses on how EU Member States are approaching the promise to develop and use AI for the good of (...)
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  45.  9
    A machine learning approach to recognize bias and discrimination in job advertisements.Richard Frissen, Kolawole John Adebayo & Rohan Nanda - 2023 - AI and Society 38 (2):1025-1038.
    In recent years, the work of organizations in the area of digitization has intensified significantly. This trend is also evident in the field of recruitment where job application tracking systems (ATS) have been developed to allow job advertisements to be published online. However, recent studies have shown that recruiting in most organizations is not inclusive, being subject to human biases and prejudices. Most discrimination activities appear early but subtly in the hiring process, for instance, exclusive phrasing in job advertisement discourages (...)
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  46.  20
    Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.Benedetta Giovanola & Simona Tiribelli - 2023 - AI and Society 38 (2):549-563.
    The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently (...)
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  47.  3
    Indexing, enriching, and understanding Brazilian missing person cases from data of distributed repositories on the web.Jorão Gomes, Heder Soares Bernardino, Jairo Francisco de Souza & Enayat Rajabi - 2023 - AI and Society 38 (2):565-579.
    For decision making in government, it is necessary to have well-structured sources of information. In several countries, it is difficult to access government data as the information are dispersed, disconnected, and poorly structured. For this reason, this work presents a framework to gather, unify, and enrich missing person data from distributed web sources. The framework allows inserting new tasks specific to the user’s domain to improve data quality. In this study, Brazilian missing person data from non-governmental organizations (NGOs) and governmental (...)
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  48.  2
    An experiential account of a large-scale interdisciplinary data analysis of public engagement.Julian “Iñaki” Goñi, Claudio Fuentes & Maria Paz Raveau - 2023 - AI and Society 38 (2):581-593.
    This article presents our experience as a multidisciplinary team systematizing and analyzing the transcripts from a large-scale (1.775 conversations) series of conversations about Chile’s future. This project called “Tenemos Que Hablar de Chile” [We have to talk about Chile] gathered more than 8000 people from all municipalities, achieving gender, age, and educational parity. In this sense, this article takes an experiential approach to describe how certain interdisciplinary methodological decisions were made. We sought to apply analytical variables derived from social science (...)
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  49.  5
    Psychological targeting: nudge or boost to foster mindful and sustainable consumption?Erik Hermann - 2023 - AI and Society 38 (2):961-962.
  50.  13
    How virtue signalling makes us better: moral preferences with respect to autonomous vehicle type choices.Robin Kopecky, Michaela Jirout Košová, Daniel D. Novotný, Jaroslav Flegr & David Černý - 2023 - AI and Society 38 (2):937-946.
    One of the moral questions concerning autonomous vehicles (henceforth AVs) is the choice between types that differ in their built-in algorithms for dealing with rare situations of unavoidable lethal collision. It does not appear to be possible to avoid questions about how these algorithms should be designed. We present the results of our study of moral preferences (N = 2769) with respect to three types of AVs: (1) selfish, which protects the lives of passenger(s) over any number of bystanders; (2) (...)
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  51.  13
    Algorithmic fairness through group parities? The case of COMPAS-SAPMOC.Francesca Lagioia, Riccardo Rovatti & Giovanni Sartor - 2023 - AI and Society 38 (2):459-478.
    Machine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria (e.g., demographic parity, equality of opportunity, treatment equality) as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to (...)
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  52.  4
    A call for epistemic analysis of cultural theories for AI methods.Masoumeh Mansouri - 2023 - AI and Society 38 (2):969-971.
  53.  10
    Exposing implicit biases and stereotypes in human and artificial intelligence: state of the art and challenges with a focus on gender.Ludovica Marinucci, Claudia Mazzuca & Aldo Gangemi - 2023 - AI and Society 38 (2):747-761.
    Biases in cognition are ubiquitous. Social psychologists suggested biases and stereotypes serve a multifarious set of cognitive goals, while at the same time stressing their potential harmfulness. Recently, biases and stereotypes became the purview of heated debates in the machine learning community too. Researchers and developers are becoming increasingly aware of the fact that some biases, like gender and race biases, are entrenched in the algorithms some AI applications rely upon. Here, taking into account several existing approaches that address the (...)
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  54.  7
    The ethics of algorithms from the perspective of the cultural history of consciousness: first look.Carlos Andres Salazar Martinez & Olga Lucia Quintero Montoya - 2023 - AI and Society 38 (2):763-775.
    Theories related to cognitive sciences, Human-in-the-loop Cyber-physical systems, data analysis for decision-making, and computational ethics make clear the need to create transdisciplinary learning, research, and application strategies to bring coherence to the paradigm of a truly human-oriented technology. Autonomous objects assume more responsibilities for individual and collective phenomena, they have gradually filtered into routines and require the incorporation of ethical practice into the professions related to the development, modeling, and design of algorithms. To make this possible, it is pertinent and (...)
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  55.  2
    The psychological and ethological antecedents of human consent to techno-empowerment of autonomous office assistants.Artur Modliński - 2023 - AI and Society 38 (2):647-663.
    Human organizations’ adoption of the paradigm of the Fourth Industrial Revolution is associated with the growth of techno-empowerment, which is the process of transferring autonomy in decision-making to intelligent machines. Particular persuasive strategies have been identified that may coax people to use intelligent devices. However, there is a substantial research gap regarding what antecedents influence human intention to assign decision-making autonomy to artificial agents. In this study, ethological and evolutionary concepts are applied to explain the drivers for autonomous assistants’ techno-empowerment. (...)
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  56.  9
    Every word you say: algorithmic mediation and implications of data-driven scholarly communication.Luciana Monteiro-Krebs, Bieke Zaman, David Geerts & Sônia Elisa Caregnato - 2023 - AI and Society 38 (2):1003-1012.
    Implications of algorithmic mediation can be studied through the artefact itself, peoples’ practices, and the social/political/economical arrangements that affect and are affected by such interactions. Most studies in Academic social media (ASM) focus on one of these elements at a time, either examining design elements or the users’ behaviour on and perceptions of such platforms. We take a multi-faceted approach using affordances as a lens to analyze practices and arrangements traversed by algorithmic mediation. Following our earlier studies that examined the (...)
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  57.  3
    Toward safe AI.Andres Morales-Forero, Samuel Bassetto & Eric Coatanea - 2023 - AI and Society 38 (2):685-696.
    Since some AI algorithms with high predictive power have impacted human integrity, safety has become a crucial challenge in adopting and deploying AI. Although it is impossible to prevent an algorithm from failing in complex tasks, it is crucial to ensure that it fails safely, especially if it is a critical system. Moreover, due to AI’s unbridled development, it is imperative to minimize the methodological gaps in these systems’ engineering. This paper uses the well-known Box-Jenkins method for statistical modeling as (...)
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  58.  6
    The importance of transparency in naming conventions, designs, and operations of safety features: from modern ADAS to fully autonomous driving functions.Mohsin Murtaza, Chi-Tsun Cheng, Mohammad Fard & John Zeleznikow - 2023 - AI and Society 38 (2):983-993.
    This paper investigates the importance of standardising and maintaining the transparency of advanced driver-assistance systems (ADAS) functions nomenclature, designs, and operations in all categories up until fully autonomous vehicles. The aim of this paper is to reveal the discrepancies in ADAS functions across automakers and discuss the underlying issues and potential solutions. In this pilot study, user manuals of various brands are reviewed systematically and critical analyses of common ADAS functions are conducted. The result shows that terminologies used to describe (...)
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  59.  6
    Empathetic AI for ethics-in-the-small.Vivek Nallur & Graham Finlay - 2023 - AI and Society 38 (2):973-974.
  60.  2
    A machine learning approach to detecting fraudulent job types.Marcel Naudé, Kolawole John Adebayo & Rohan Nanda - 2023 - AI and Society 38 (2):1013-1024.
    Job seekers find themselves increasingly duped and misled by fraudulent job advertisements, posing a threat to their privacy, security and well-being. There is a clear need for solutions that can protect innocent job seekers. Existing approaches to detecting fraudulent jobs do not scale well, function like a black-box, and lack interpretability, which is essential to guide applicants’ decision-making. Moreover, commonly used lexical features may be insufficient as the representation does not capture contextual semantics of the underlying document. Hence, this paper (...)
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  61.  5
    Redefining culture in cultural robotics.Mark L. Ornelas, Gary B. Smith & Masoumeh Mansouri - 2023 - AI and Society 38 (2):777-788.
    Cultural influences are pervasive throughout human behaviour, and as human–robot interactions become more common, roboticists are increasingly focusing attention on how to build robots that are culturally competent and culturally sustainable. The current treatment of culture in robotics, however, is largely limited to the definition of culture as national culture. This is problematic for three reasons: it ignores subcultures, it loses specificity and hides the nuances in cultures, and it excludes refugees and stateless persons. We propose to shift the focus (...)
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  62.  8
    Artificial agents’ explainability to support trust: considerations on timing and context.Guglielmo Papagni, Jesse de Pagter, Setareh Zafari, Michael Filzmoser & Sabine T. Koeszegi - 2023 - AI and Society 38 (2):947-960.
    Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial agents’ reliability. Particularly, this (...)
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  63.  12
    Ethical artificial intelligence framework for a good AI society: principles, opportunities and perils.Pradeep Paraman & Sanmugam Anamalah - 2023 - AI and Society 38 (2):595-611.
    The justification and rationality of this paper is to present some fundamental principles, theories, and concepts that we believe moulds the nucleus of a good artificial intelligence (AI) society. The morally accepted significance and utilitarian concerns that stems from the inception and realisation of an AI’s structural foundation are displayed in this study. This paper scrutinises the structural foundation, fundamentals, and cardinal righteous remonstrations, as well as the gaps in mechanisms towards novel prospects and perils in determining resilient fundamentals, accountability, (...)
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  64.  4
    Word embeddings are biased. But whose bias are they reflecting?Davor Petreski & Ibrahim C. Hashim - 2023 - AI and Society 38 (2):975-982.
    From Curriculum Vitae parsing to web search and recommendation systems, Word2Vec and other word embedding techniques have an increasing presence in everyday interactions in human society. Biases, such as gender bias, have been thoroughly researched and evidenced to be present in word embeddings. Most of the research focuses on discovering and mitigating gender bias within the frames of the vector space itself. Nevertheless, whose bias is reflected in word embeddings has not yet been investigated. Besides discovering and mitigating gender bias, (...)
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  65.  8
    Intelligent service robots for elderly or disabled people and human dignity: legal point of view.Katarzyna Pfeifer-Chomiczewska - 2023 - AI and Society 38 (2):789-800.
    This article aims to present the problem of the impact of artificial intelligence on respect for human dignity in the sphere of care for people who, for various reasons, are described as particularly vulnerable, especially seniors and people with various disabilities. In recent years, various initiatives and works have been undertaken on the European scene to define the directions in which the development and use of artificial intelligence should go. According to the human-centric approach, artificial intelligence should be developed, used (...)
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  66.  1
    Immune moral models? Pro-social rule breaking as a moral enhancement approach for ethical AI.Rajitha Ramanayake, Philipp Wicke & Vivek Nallur - 2023 - AI and Society 38 (2):801-813.
    We are moving towards a future where Artificial Intelligence (AI) based agents make many decisions on behalf of humans. From healthcare decision-making to social media censoring, these agents face problems, and make decisions with ethical and societal implications. Ethical behaviour is a critical characteristic that we would like in a human-centric AI. A common observation in human-centric industries, like the service industry and healthcare, is that their professionals tend to break rules, if necessary, for pro-social reasons. This behaviour among humans (...)
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  67.  6
    Minding the gap(s): public perceptions of AI and socio-technical imaginaries.Laura Sartori & Giulia Bocca - 2023 - AI and Society 38 (2):443-458.
    Deepening and digging into the social side of AI is a novel but emerging requirement within the AI community. Future research should invest in an “AI for people”, going beyond the undoubtedly much-needed efforts into ethics, explainability and responsible AI. The article addresses this challenge by problematizing the discussion around AI shifting the attention to individuals and their awareness, knowledge and emotional response to AI. First, we outline our main argument relative to the need for a socio-technical perspective in the (...)
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  68.  13
    Artificial Intelligence/Consciousness: being and becoming John Malkovich.Amar Singh & Shipra Tholia - 2023 - AI and Society 38 (2):697-706.
    For humans, Artificial Intelligence operates more like a Rorschach test, as it is expected that intelligent machines will reflect humans' cognitive and physical behaviours. The concept of intelligence, however, is often confused with consciousness, and it is believed that the progress of intelligent machines will eventually result in them becoming conscious in the future. Nevertheless, what is overlooked is how the exploration of Artificial Intelligence also pertains to the development of human consciousness. An excellent example of this can be seen (...)
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  69.  8
    Applying AI for social good: Aligning academic journal ratings with the United Nations Sustainable Development Goals (SDGs).David Steingard, Marcello Balduccini & Akanksha Sinha - 2023 - AI and Society 38 (2):613-629.
    This paper offers three contributions to the burgeoning movements of AI for Social Good (AI4SG) and AI and the United Nations Sustainable Development Goals (SDGs). First, we introduce the SDG-Intense Evaluation framework (SDGIE) that aims to situate variegated automated/AI models in a larger ecosystem of computational approaches to advance the SDGs. To foster knowledge collaboration for solving complex social and environmental problems encompassed by the SDGs, the SDGIE framework details a benchmark structure of data-algorithm-output to effectively standardize AI approaches to (...)
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  70.  8
    AI ageism: a critical roadmap for studying age discrimination and exclusion in digitalized societies.Justyna Stypinska - 2023 - AI and Society 38 (2):665-677.
    In the last few years, we have witnessed a surge in scholarly interest and scientific evidence of how algorithms can produce discriminatory outcomes, especially with regard to gender and race. However, the analysis of fairness and bias in AI, important for the debate of AI for social good, has paid insufficient attention to the category of age and older people. Ageing populations have been largely neglected during the turn to digitality and AI. In this article, the concept of AI ageism (...)
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  71.  9
    The future of ethics in AI: challenges and opportunities.Angelo Trotta, Marta Ziosi & Vincenzo Lomonaco - 2023 - AI and Society 38 (2):439-441.
  72.  6
    Against the new space race: global AI competition and cooperation for people.Inga Ulnicane - 2023 - AI and Society 38 (2):681-683.
    This Open Forum contribution critically interrogates the use of space race rhetoric in current discussions about artificial intelligence (AI). According to this rhetoric, similar to the space race of the twentieth century, AI development is portrayed as a rivalry among superpowers where one country will win and reap major benefits, while others will be left behind. Using this rhetoric to frame AI development tends to prioritize narrow and short-term economic interests over broader and longer-term societal needs. Three particularly problematic aspects (...)
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  73.  13
    AI for the public. How public interest theory shifts the discourse on AI.Theresa Züger & Hadi Asghari - 2023 - AI and Society 38 (2):815-828.
    AI for social good is a thriving research topic and a frequently declared goal of AI strategies and regulation. This article investigates the requirements necessary in order for AI to actually serve a public interest, and hence be socially good. The authors propose shifting the focus of the discourse towards democratic governance processes when developing and deploying AI systems. The article draws from the rich history of public interest theory in political philosophy and law, and develops a framework for ‘public (...)
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  74.  28
    Frankenstein: a creation of artificial intelligence?Jennings Byrd & Paige Paquette - 2023 - AI and Society 38 (1):331-342.
    Throughout Mary Shelley’s early life, she was exposed to numerous well-known and influential people regarding cultural, political, and socio-economic matters. As she began writing, these influences undoubtedly played a role in her narrative. Her novel, _Frankenstein_, written during the time of the first Industrial Revolution in Britain, was one such novel that exhibited her political and economic influences through science fiction. This article addresses many of those influences, including the introduction of the machine into manufacturing. It further addresses how Frankenstein’s (...)
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  75.  31
    Beyond the frame problem: what (else) can Heidegger do for AI?Mario Andrés Chalita & Alexander Sedzielarz - 2023 - AI and Society 38 (1):173-184.
    About three decades ago, AI theory underwent a sharp turn as a consequence of criticism that pointed out the problem of externalism in the cognitivist position. Hubert Dreyfus, undoubtedly the main exponent of this criticism, opened the possibility of a Heideggerian reading using the frame problem to bring to light obscurities that otherwise would have been very difficult to detect. However, the question still remains of whether or not Heidegger’s philosophy can serve as the source of a positive contribution to (...)
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  76.  18
    Empiricism in the foundations of cognition.Timothy Childers, Juraj Hvorecký & Ondrej Majer - 2023 - AI and Society 38 (1):67-87.
    This paper traces the empiricist program from early debates between nativism and behaviorism within philosophy, through debates about early connectionist approaches within the cognitive sciences, and up to their recent iterations within the domain of deep learning. We demonstrate how current debates on the nature of cognition via deep network architecture echo some of the core issues from the Chomsky/Quine debate and investigate the strength of support offered by these various lines of research to the empiricist standpoint. Referencing literature from (...)
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  77.  1
    The AI gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations.Josh Cowls, Andreas Tsamados, Mariarosaria Taddeo & Luciano Floridi - 2023 - AI and Society 38 (1):283-307.
    In this article, we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change, and it can contribute to combatting the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and (...)
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  78.  6
    Paradox of choice and sharing personal information.Takeshi Ebina & Keita Kinjo - 2023 - AI and Society 38 (1):121-132.
    The purpose of this study is to investigate the relationship between a firm’s strategy and consumers’ decisions in the presence of the paradox of choice and sharing personal information. The paradox of choice implies that having too many choices does not necessarily ensure happiness and sometimes having less is more. A new model is constructed introducing a factor of information sharing into the model of a previous study that embedded the paradox of choice only (Kinjo and Ebina in AI Soc (...)
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  79.  2
    Toy story or children story? Putting children and their rights at the forefront of the artificial intelligence revolution.E. Fosch-Villaronga, S. van der Hof, C. Lutz & A. Tamò-Larrieux - 2023 - AI and Society 38 (1):133-152.
    Policymakers need to start considering the impact smart connected toys (SCTs) have on children. Equipped with sensors, data processing capacities, and connectivity, SCTs targeting children increasingly penetrate pervasively personal environments. The network of SCTs forms the Internet of Toys (IoToys) and often increases children's engagement and playtime experience. Unfortunately, this young part of the population and, most of the time, their parents are often unaware of SCTs’ far-reaching capacities and limitations. The capabilities and constraints of SCTs create severe side effects (...)
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  80.  9
    Towards an effective transnational regulation of AI.Daniel J. Gervais - 2023 - AI and Society 38 (1):391-410.
    Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...)
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  81.  4
    Moving the AI needle: from chaos to engagement.Karamjit S. Gill - 2023 - AI and Society 38 (1):1-4.
  82.  3
    Ethical considerations and statistical analysis of industry involvement in machine learning research.Thilo Hagendorff & Kristof Meding - 2023 - AI and Society 38 (1):35-45.
    Industry involvement in the machine learning (ML) community seems to be increasing. However, the quantitative scale and ethical implications of this influence are rather unknown. For this purpose, we have not only carried out an informed ethical analysis of the field, but have inspected all papers of the main ML conferences NeurIPS, CVPR, and ICML of the last 5 years—almost 11,000 papers in total. Our statistical approach focuses on conflicts of interest, innovation, and gender equality. We have obtained four main (...)
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  83.  10
    The widening rift between aesthetics and ethics in the design of computational things.Sabrina Hauser, Johan Redström & Heather Wiltse - 2023 - AI and Society 38 (1):227-243.
    In the face of massively increased technological complexity, it is striking that so many of today’s computational and networked things follow design ideals honed decades ago in a much different context. These strong ideals prescribe a presentation of things as useful tools through design and a withdrawal of aspects of their functionality and complexity. Beginning in the mid-twentieth century, we trace this ‘withdrawal program’ as it has persisted in the face of increasing computational complexity. Currently, design is in a dilemma (...)
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  84.  26
    Artificial intelligence in fiction: between narratives and metaphors.Isabella Hermann - 2023 - AI and Society 38 (1):319-329.
    Science-fiction (SF) has become a reference point in the discourse on the ethics and risks surrounding artificial intelligence (AI). Thus, AI in SF—science-fictional AI—is considered part of a larger corpus of ‘AI narratives’ that are analysed as shaping the fears and hopes of the technology. SF, however, is not a foresight or technology assessment, but tells dramas for a human audience. To make the drama work, AI is often portrayed as human-like or autonomous, regardless of the actual technological limitations. Taking (...)
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  85.  10
    What can science fiction tell us about the future of artificial intelligence policy?Andrew Dana Hudson, Ed Finn & Ruth Wylie - 2023 - AI and Society 38 (1):197-211.
    This paper addresses the gap between familiar popular narratives describing Artificial Intelligence (AI), such as the trope of the killer robot, and the realistic near-future implications of machine intelligence and automation for technology policy and society. The authors conducted a series of interviews with technologists, science fiction writers, and other experts, as well as a workshop, to identify a set of key themes relevant to the near future of AI. In parallel, they led the analysis of almost 100 recent works (...)
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  86.  19
    Can we wrong a robot?Nancy S. Jecker - 2023 - AI and Society 38 (1):259-268.
    With the development of increasingly sophisticated sociable robots, robot-human relationships are being transformed. Not only can sociable robots furnish emotional support and companionship for humans, humans can also form relationships with robots that they value highly. It is natural to ask, do robots that stand in close relationships with us have any moral standing over and above their purely instrumental value as means to human ends. We might ask our question this way, ‘Are there ways we can act towards robots (...)
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  87.  8
    Processing of grid-based design representations: a qualitative analysis of concurrent think-aloud protocols.Gagan Deep Kaur - 2023 - AI and Society 38 (1):21-33.
    The squared paper or graphs are grid-based design representations used in engineering, industrial and craft design practices wherein designs are drawn over symmetrical grids. This paper reports grid-processing strategies undertaken by actors in a native craft practice, viz. Kashmiri carpet-weaving having three task contexts: (1) _design_, wherein designs are drawn on graph sheets and color scheme given by assigning practice-specific symbolic codes to the motifs by designers; (2) _coding_, wherein a cryptic script, called _talim_, is generated from these encoded graphs (...)
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  88.  6
    Toward a dataist future: tracing Scandinavian posthumanism in Real Humans.Mads Larsen - 2023 - AI and Society 38 (1):349-361.
    Artificial intelligence is likely to undermine the anthropocentrism of humanism, the master narrative that undergirds the modern world. Humanity will need a new story to structure our beliefs and cooperation around. As different regions explore posthumanist alternatives through fiction, they bring with them distinct traditions of thought. The Swedish TV series _Real Humans_ (2012–2014) and its British remake, _Humans_ (2015–2018), dramatize the challenge of freeing oneself from cultural presumptions. When negotiating personhood with humanoid robots, the Swedish protagonist family presupposes a (...)
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  89.  11
    Bosses without a heart: socio-demographic and cross-cultural determinants of attitude toward Emotional AI in the workplace.Peter Mantello, Manh-Tung Ho, Minh-Hoang Nguyen & Quan-Hoang Vuong - 2023 - AI and Society 38 (1):97-119.
    Biometric technologies are becoming more pervasive in the workplace, augmenting managerial processes such as hiring, monitoring and terminating employees. Until recently, these devices consisted mainly of GPS tools that track location, software that scrutinizes browser activity and keyboard strokes, and heat/motion sensors that monitor workstation presence. Today, however, a new generation of biometric devices has emerged that can sense, read, monitor and evaluate the affective state of a worker. More popularly known by its commercial moniker, Emotional AI, the technology stems (...)
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  90.  11
    Online public discourse on artificial intelligence and ethics in China: context, content, and implications.Yishu Mao & Kristin Shi-Kupfer - 2023 - AI and Society 38 (1):373-389.
    The societal and ethical implications of artificial intelligence (AI) have sparked discussions among academics, policymakers and the public around the world. What has gone unnoticed so far are the likewise vibrant discussions in China. We analyzed a large sample of discussions about AI ethics on two Chinese social media platforms. Findings suggest that participants were diverse, and included scholars, IT industry actors, journalists, and members of the general public. They addressed a broad range of concerns associated with the application of (...)
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  91.  8
    Ethics-based auditing of automated decision-making systems: intervention points and policy implications.Jakob Mökander & Maria Axente - 2023 - AI and Society 38 (1):153-171.
    Organisations increasingly use automated decision-making systems (ADMS) to inform decisions that affect humans and their environment. While the use of ADMS can improve the accuracy and efficiency of decision-making processes, it is also coupled with ethical challenges. Unfortunately, the governance mechanisms currently used to oversee human decision-making often fail when applied to ADMS. In previous work, we proposed that ethics-based auditing (EBA)—that is, a structured process by which ADMS are assessed for consistency with relevant principles or norms—can (a) help organisations (...)
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  92.  9
    Attitudes about Brain–Computer Interface (BCI) technology among Spanish rehabilitation professionals.Aníbal Monasterio Astobiza, David Rodriguez Arias-Vailhen, Txetxu Ausín, Mario Toboso, Manuel Aparicio & Daniel López - 2023 - AI and Society 38 (1):309-318.
    To assess—from a qualitative perspective—the perceptions and attitudes of Spanish rehabilitation professionals (e.g. rehabilitation doctors, speech therapists, physical therapists) about Brain–Computer Interface (BCI) technology. A qualitative, exploratory and descriptive study was carried out by means of interviews and analysis of textual content with mixed generation of categories and segmentation into frequency of topics. We present the results of three in-depth interviews that were conducted with Spanish speaking individuals who had previously completed a survey as part of a larger, 3-country/language, survey (...)
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  93.  40
    Operationalising AI ethics: barriers, enablers and next steps.Jessica Morley, Libby Kinsey, Anat Elhalal, Francesca Garcia, Marta Ziosi & Luciano Floridi - 2023 - AI and Society 38 (1):411-423.
    By mid-2019 there were more than 80 AI ethics guides available in the public domain. Despite this, 2020 saw numerous news stories break related to ethically questionable uses of AI. In part, this is because AI ethics theory remains highly abstract, and of limited practical applicability to those actually responsible for designing algorithms and AI systems. Our previous research sought to start closing this gap between the ‘what’ and the ‘how’ of AI ethics through the creation of a searchable typology (...)
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  94.  16
    Integrating AI ethics in wildlife conservation AI systems in South Africa: a review, challenges, and future research agenda.Irene Nandutu, Marcellin Atemkeng & Patrice Okouma - 2023 - AI and Society 38 (1):245-257.
    With the increased use of Artificial Intelligence (AI) in wildlife conservation, issues around whether AI-based monitoring tools in wildlife conservation comply with standards regarding AI Ethics are on the rise. This review aims to summarise current debates and identify gaps as well as suggest future research by investigating (1) current AI Ethics and AI Ethics issues in wildlife conservation, (2) Initiatives Stakeholders in AI for wildlife conservation should consider integrating AI Ethics in wildlife conservation. We find that the existing literature (...)
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  95.  5
    From posthumanism to ethics of artificial intelligence.Rajakishore Nath & Riya Manna - 2023 - AI and Society 38 (1):185-196.
    Posthumanism is one of the well-known and significant concepts in the present day. It impacted numerous contemporary fields like philosophy, literary theories, art, and culture for the last few decades. The movement has been concentrated around the technological development of present days due to industrial advancement in society and the current proliferated daily usage of technology. Posthumanism indicated a deconstruction of our radical conception of ‘human’, and it further shifts our societal value alignment system to a novel dimension. The majority (...)
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  96.  3
    Could artificial intelligence have consciousness? Some perspectives from neurology and parapsychology.Yew-Kwang Ng - 2023 - AI and Society 38 (1):425-436.
    The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology (...)
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  97.  5
    Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model.Salvatore Sapienza & Anton Vedder - 2023 - AI and Society 38 (1):5-20.
    Big data and Machine learning Techniques are reshaping the way in which food safety risk assessment is conducted. The ongoing ‘datafication’ of food safety risk assessment activities and the progressive deployment of probabilistic models in their practices requires a discussion on the advantages and disadvantages of these advances. In particular, the low level of trust in EU food safety risk assessment framework highlighted in 2019 by an EU-funded survey could be exacerbated by novel methods of analysis. The variety of processed (...)
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  98.  17
    Morals, ethics, and the technology capabilities and limitations of automated and self-driving vehicles.Joshua Siegel & Georgios Pappas - 2023 - AI and Society 38 (1):213-226.
    We motivate the desire for self-driving and explain its potential and limitations, and explore the need for—and potential implementation of—morals, ethics, and other value systems as complementary “capabilities” to the Deep Technologies behind self-driving. We consider how the incorporation of such systems may drive or slow adoption of high automation within vehicles. First, we explore the role for morals, ethics, and other value systems in self-driving through a representative hypothetical dilemma faced by a self-driving car. Through the lens of engineering, (...)
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  99.  28
    A neo-aristotelian perspective on the need for artificial moral agents (AMAs).Alejo José G. Sison & Dulce M. Redín - 2023 - AI and Society 38 (1):47-65.
    We examine Van Wynsberghe and Robbins (JAMA 25:719-735, 2019) critique of the need for Artificial Moral Agents (AMAs) and its rebuttal by Formosa and Ryan (JAMA 10.1007/s00146-020-01089-6, 2020) set against a neo-Aristotelian ethical background. Neither Van Wynsberghe and Robbins (JAMA 25:719-735, 2019) essay nor Formosa and Ryan’s (JAMA 10.1007/s00146-020-01089-6, 2020) is explicitly framed within the teachings of a specific ethical school. The former appeals to the lack of “both empirical and intuitive support” (Van Wynsberghe and Robbins 2019, p. 721) for (...)
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  100.  26
    Artificial intelligence is an oxymoron.Jakob Svensson - 2023 - AI and Society 38 (1):363-372.
    Departing from popular imaginations around artificial intelligence (AI), this article engages in the I in the AI acronym but from perspectives outside of mathematics, computer science and machine learning. When intelligence is attended to here, it most often refers to narrow calculating tasks. This connotation to calculation provides AI an image of scientificity and objectivity, particularly attractive in societies with a pervasive desire for numbers. However, as is increasingly apparent today, when employed in more general areas of our messy socio-cultural (...)
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  101.  9
    Socio-political stability, voter’s emotional expectations, and information management.Vladimir Tsyganov - 2023 - AI and Society 38 (1):269-281.
    The dependence of socio-political stability on the emotional expectations of voters is investigated. For this, a model of a socio-political system consisting of a society of voters and a democratically elected politician is considered. The neuropsychological model of the voter takes into account his emotional expectations. The social stability is guaranteed by the expectations of positive emotions of all voters. Socio-political stability means both the social stability and the re-election of politician. One type of voter is a Progressist who seeks (...)
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  102.  5
    Are wicked problems a lack of general collective intelligence?Andy E. Williams - 2023 - AI and Society 38 (1):343-348.
    A recently developed model of general collective intelligence defines a method for organizing humans or artificially intelligent agents that is believed to create the potential to exponentially increase the general problem-solving ability of groups of such entities over that of any individual entity. An analysis based on this model suggests that many and perhaps all “wicked problems” are collective optimization problems that cannot reliably be addressed without a system of collective optimization, but that might be reliably addressed through such a (...)
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  103.  7
    Beta-testing the ethics plugin.Keith Begley - 2023 - AI and Society:1-3.
    The three main kinds of theory in normative ethics, namely, consequentialism, deontology, and virtue ethics, are often presented as the ‘palette’ from which we may choose, or use as a starting point for an investigation. However, this way of doing ethics and philosophy, by the palette, may be leading some of us astray. It has led some to believe that all that there is to ethics, and to ethics of AI, is given in terms of these already devised petrified categories (...)
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  104.  4
    Ethics of using artificial intelligence (AI) in veterinary medicine.Simon Coghlan & Thomas Quinn - 2023 - AI and Society:1-12.
    This paper provides the first comprehensive analysis of ethical issues raised by artificial intelligence (AI) in veterinary medicine for companion animals. Veterinary medicine is a socially valued service, which, like human medicine, will likely be significantly affected by AI. Veterinary AI raises some unique ethical issues because of the nature of the client–patient–practitioner relationship, society’s relatively minimal valuation and protection of nonhuman animals and differences in opinion about responsibilities to animal patients and human clients. The paper examines how these distinctive (...)
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  105.  1
    Machine learning in bail decisions and judges’ trustworthiness.Alexis Morin-Martel - 2023 - AI and Society:1-12.
    The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...)
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  106.  5
    The ethics of conceptual, ontological, semantic and knowledge modeling.Robert J. Rovetto - 2023 - AI and Society:1-22.
    The ethics of artificial intelligence (AI) is a research topic with both theoretical and practical significance. However, the ethical and moral aspects of conceptual, ontological, semantic, and knowledge modeling, more specifically, and which are sometimes found in AI applications, is not being given sufficient attention. I argue that it should. Whether considering using or developing these meaning-focused models, there are ethical aspects. This paper offers a preliminary outline about this potentially new research field, discussing: some questions and areas of concern, (...)
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  107.  19
    Lorenzo Magnani: Discoverability—the urgent need of an ecology of human creativity. [REVIEW]Jeffrey White - 2023 - AI and Society:1-2.
    Discoverability: the urgent need of an ecology of human creativity from the prolific Lorenzo Magnani is worthy of direct attention. The message may be of special interest to philosophers, ethicists and organizing scientists involved in the development of AI and related technologies which are increasingly directed at reinforcing conditions against which Magnani directly warns, namely the “overcomputationalization” of life marked by the gradual encroachment of technologically “locked strategies” into everyday decision-making until “freedom, responsibility, and ownership of our destinies” are ceded (...)
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