- New
-
Topics
- All Categories
- Metaphysics and Epistemology
- Value Theory
- Science, Logic, and Mathematics
- Science, Logic, and Mathematics
- Logic and Philosophy of Logic
- Philosophy of Biology
- Philosophy of Cognitive Science
- Philosophy of Computing and Information
- Philosophy of Mathematics
- Philosophy of Physical Science
- Philosophy of Social Science
- Philosophy of Probability
- General Philosophy of Science
- Philosophy of Science, Misc
- History of Western Philosophy
- Philosophical Traditions
- Philosophy, Misc
- Other Academic Areas
- Journals
- Submit material
- More
Philosophy of Artificial Intelligence
Edited by Eric Dietrich (State University of New York at Binghamton)
Assistant editor: Michelle Thomas (University of Western Ontario)
About this topic
| Summary |
The philosophy of artificial intelligence is a collection of issues primarily concerned with whether or not AI is possible -- with whether or not it is possible to build an intelligent thinking machine. Also of concern is whether humans and other animals are best thought of as machines (computational robots, say) themselves. The most important of the "whether-possible" problems lie at the intersection of theories of the semantic contents of thought and the nature of computation. A second suite of problems surrounds the nature of rationality. A third suite revolves around the seeming “transcendent” reasoning powers of the human mind. These problems derive from Kurt Gödel's famous Incompleteness Theorem. A fourth collection of problems concerns the architecture of an intelligent machine. Should a thinking computer use discrete or continuous modes of computing and representing, is having a body necessary, and is being conscious necessary. This takes us to the final set of questions. Can a computer be conscious? Can a computer have a moral sense? Would we have duties to thinking computers, to robots? For example, is it moral for humans to even attempt to build an intelligent machine? If we did build such a machine, would turning it off be the equivalent of murder? If we had a race of such machines, would it be immoral to force them to work for us? |
| Key works | Probably the most important attack on whether AI is possible is John Searle's famous Chinese Room Argument: Searle 1980. This attack focuses on the semantic aspects (mental semantics) of thoughts, thinking, and computing. For some replies to this argument, see the same 1980 journal issue as Searle's original paper. For the problem of the nature of rationality, see Pylyshyn 1987. An especially strong attack on AI from this angle is Jerry Fodor's work on the frame problem: Fodor 1987. On the frame problem in general, see McCarthy & Hayes 1969. For some replies to Fodor and advances on the frame problem, see Ford & Pylyshyn 1996. For the transcendent reasoning issue, a central and important paper is Hilary Putnam's Putnam 1960. This paper is arguably the source for the computational turn in 1960s-70s philosophy of mind. For architecture-of-mind issues, see, for starters: M. Spivey's The Contintuity of Mind, Oxford, which argues against the notion of discrete representations. See also, Gelder & Port 1995. For an argument for discrete representations, see, Dietrich & Markman 2003. For an argument that the mind's boundaries do not end at the body's boundaries, see, Clark & Chalmers 1998. For a statement of and argument for computationalism -- the thesis that the mind is a kind of computer -- see Shimon Edelman's excellent book Edelman 2008. See also Chapter 9 of Chalmers's book Chalmers 1996. |
| Introductions | Chinese Room Argument: Searle 1980. Frame problem: Fodor 1987, Computationalism and Godelian style refutation: Putnam 1960. Architecture: M. Spivey's The Contintuity of Mind, Oxford and Shimon Edelman's Edelman 2008. Ethical issues: Anderson & Anderson 2011 and Müller 2012. Conscious computers: Chalmers 2011. |
Show all references
Related
Subcategories
Artificial Minds (2,106 | 392)
The Turing Test (482)
The Chinese Room (272)
Machine Consciousness (370)
The Singularity* (106)
Mind Uploading* (50)
Artificial Minds, Misc (319)
Representation in Artificial Intelligence (842 | 182)
Areas of Artificial Intelligence (1,303 | 293)
Artificial Life* (258)
Computer Vision (10)
Cybernetics (205)
Dynamical Systems (226)
The Frame Problem (90)
Robotics (310)
Ethics of Artificial Intelligence (2,650 | 3)
Algorithmic Fairness (145)
Autonomous Vehicles (24)
Autonomous Weapons (44)
Machine Ethics (532)
Robot Ethics* (482)
Impact of Artificial Intelligence (1,034 | 112)
Autonomous Weapons* (44)
Autonomous Vehicles* (24)
Cyborgs (26)
Mind Uploading (50)
The Singularity (106)
Transhumanism (436)
Contents
15040 found
Order:
| 1 — 50 / 15040 |
Material to categorize
- Balancing AI and academic integrity: what are the positions of academic publishers and universities?Bashar Haruna Gulumbe, Shuaibu Muhammad Audu & Abubakar Muhammad Hashim - forthcoming - AI and Society:1-10.This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in (...)
- An elemental ethics for artificial intelligence: water as resistance within AI’s value chain.Sebastián Lehuedé - forthcoming - AI and Society:1-14.Research and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques the (...)
- We are Building Gods: AI as the Anthropomorphised Authority of the Past.Carl Öhman - 2024 - Minds and Machines 34 (1):1-18.This article argues that large language models (LLMs) should be interpreted as a form of gods. In a theological sense, a god is an immortal being that exists beyond time and space. This is clearly nothing like LLMs. In an anthropological sense, however, a god is rather defined as the personified authority of a group through time—a conceptual tool that molds a collective of ancestors into a unified agent or voice. This is exactly what LLMs are. They are products of (...)
- The Man Behind the Curtain: Appropriating Fairness in AI.Marcin Korecki, Guillaume Köstner, Emanuele Martinelli & Cesare Carissimo - 2024 - Minds and Machines 34 (1):1-30.Our goal in this paper is to establish a set of criteria for understanding the meaning and sources of attributing (un)fairness to AI algorithms. To do so, we first establish that (un)fairness, like other normative notions, can be understood in a proper primary sense and in secondary senses derived by analogy. We argue that AI algorithms cannot be said to be (un)fair in the proper sense due to a set of criteria related to normativity and agency. However, we demonstrate how (...)
- Anthropomorphising Machines and Computerising Minds: The Crosswiring of Languages between Artificial Intelligence and Brain & Cognitive Sciences.Luciano Floridi & Anna C. Nobre - 2024 - Minds and Machines 34 (1):1-9.The article discusses the process of “conceptual borrowing”, according to which, when a new discipline emerges, it develops its technical vocabulary also by appropriating terms from other neighbouring disciplines. The phenomenon is likened to Carl Schmitt’s observation that modern political concepts have theological roots. The authors argue that, through extensive conceptual borrowing, AI has ended up describing computers anthropomorphically, as computational brains with psychological properties, while brain and cognitive sciences have ended up describing brains and minds computationally and informationally, as (...)
- Philosophical Lessons for Emotion Recognition Technology.Rosalie Waelen - 2024 - Minds and Machines 34 (1):1-13.Emotion recognition technology uses artificial intelligence to make inferences about a person’s emotions, on the basis of their facial expressions, body language, tone of voice, or other types of input. Underlying such technology are a variety of assumptions about the manifestation, nature, and value of emotions. To assure the quality and desirability of emotion recognition technology, it is important to critically assess the assumptions embedded in the technology. Within philosophy, there is a long tradition of epistemological, ontological, phenomenological, and ethical (...)
- Gamification, Side Effects, and Praise and Blame for Outcomes.Sven Nyholm - 2024 - Minds and Machines 34 (1):1-21.Abstract“Gamification” refers to adding game-like elements to non-game activities so as to encourage participation. Gamification is used in various contexts: apps on phones motivating people to exercise, employers trying to encourage their employees to work harder, social media companies trying to stimulate user engagement, and so on and so forth. Here, I focus on gamification with this property: the game-designer (a company or other organization) creates a “game” in order to encourage the players (the users) to bring about certain outcomes (...)
- Towards a Benchmark for Scientific Understanding in Humans and Machines.Kristian Gonzalez Barman, Sascha Caron, Tom Claassen & Henk de Regt - 2024 - Minds and Machines 34 (1):1-16.Scientific understanding is a fundamental goal of science. However, there is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of scientific understanding. In this paper, we propose a framework to create a benchmark for scientific understanding, utilizing tools from philosophy of science. We adopt a behavioral conception of understanding, according to which genuine understanding should be recognized (...)
- Epistemology Goes AI: A Study of GPT-3’s Capacity to Generate Consistent and Coherent Ordered Sets of Propositions on a Single-Input-Multiple-Outputs Basis.Marcelo de Araujo, Guilherme de Almeida & José Luiz Nunes - 2024 - Minds and Machines 34 (1):1-18.The more we rely on digital assistants, online search engines, and AI systems to revise our system of beliefs and increase our body of knowledge, the less we are able to resort to some independent criterion, unrelated to further digital tools, in order to asses the epistemic reliability of the outputs delivered by them. This raises some important questions to epistemology in general and pressing questions to applied to epistemology in particular. In this paper, we propose an experimental method for (...)
- Imagining and governing artificial intelligence: the ordoliberal way—an analysis of the national strategy ‘AI made in Germany’.Jens Hälterlein - forthcoming - AI and Society:1-12.National Artificial Intelligence (AI) strategies articulate imaginaries of the integration of AI into society and envision the governing of AI research, development and applications accordingly. To integrate these central aspects of national AI strategies under one coherent perspective, this paper presented an analysis of Germany’s strategy ‘AI made in Germany’ through the conceptual lens of ordoliberal political rationality. The first part of the paper analyses how the guiding vision of a human-centric AI not only adheres to ethical and legal principles (...)
- Adaptable robots, ethics, and trust: a qualitative and philosophical exploration of the individual experience of trustworthy AI.Stephanie Sheir, Arianna Manzini, Helen Smith & Jonathan Ives - forthcoming - AI and Society:1-14.Much has been written about the need for trustworthy artificial intelligence (AI), but the underlying meaning of trust and trustworthiness can vary or be used in confusing ways. It is not always clear whether individuals are speaking of a technology’s trustworthiness, a developer’s trustworthiness, or simply of gaining the trust of users by any means. In sociotechnical circles, trustworthiness is often used as a proxy for ‘the good’, illustrating the moral heights to which technologies and developers ought to aspire, at (...)
- Contentless Representationalism? A Neglected Option Between Radical Enactivist and Predictive Processing Accounts of Representation.Dionysis Christias - 2024 - Minds and Machines 34 (1):1-21.
- Philosophy of technology for the lost age of freedom: a critical treatise on human essence and uncertain future. Rajan - forthcoming - AI and Society:1-18.All theories of world creation, whether scientific, philosophical, or religious, can readily acknowledge the fact that humans have primarily evolved to engage with nature, the individual self, fellow human beings, society, and other naturalistic aspect of existence. Nevertheless, several novel challenges ascend when the human mind engages with technology, media, machines, and related concepts such as—ChatGPT, artificial intelligence, and to name a few. For that reason, we need philosophy and critical assessment of the uncovered essence of advanced technologies, media and (...)
- Is Explainable AI Responsible AI?Isaac Taylor - forthcoming - AI and Society.When artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that is, a situation in which nobody is morally responsible for the actions and outcomes that result. Since the responsibility gap might be thought to result from individuals lacking knowledge of the future behavior of AI systems, it can be and has been suggested that deploying explainable artificial intelligence (XAI) techniques will help us to avoid it. These techniques provide humans (...)
- Correction to: Are we inventing ourselves out of our own usefulness? Striking a balance between creativity and AI.Noel Carroll - forthcoming - AI and Society:1-1.
- The ontological quandary of deepfakes.Adeniyi Fasoro - forthcoming - AI and Society:1-9.Deepfakes, as hyperrealistic digital fabrications, reveal gaps and uncertainties in existing ontological frameworks. Neither simply images nor realities, deepfakes occupy an ambiguous metaphysical position between concepts such as representation/simulation, human/machine, and real/artificial. Their emergent generation via AI and experiential traction as credible synthetic media underscores limitations in prevailing paradigms reliant on purified binaries and anthropocentric assumptions. Rather than anomalies, deepfakes epitomize the imperative for new ontological cartographies and conceptual vocabularies attuned to increasingly unbounded algorithmic creation. The paper surveys debates about (...)
- Intelligence in animals, humans and machines: a heliocentric view of intelligence?Halfdan Holm & Soumya Banerjee - forthcoming - AI and Society:1-3.
- Chatbots, search engines, and the sealing of knowledges.Nora Freya Lindemann - forthcoming - AI and Society:1-14.In 2023, online search engine provider Microsoft integrated a language model that provides direct answers to search queries into its search engine Bing. Shortly afterwards, Google also introduced a similar feature to its search engine with the launch of Google Gemini. This introduction of direct answers to search queries signals an important and significant change in online search. This article explores the implications of this new search paradigm. Drawing on Donna Haraway’s theory of _Situated Knowledges_ and Rainer Mühlhoff’s concept of (...)
- A metaphysical account of agency for technology governance.Sadjad Soltanzadeh - forthcoming - AI and Society:1-12.The way in which agency is conceptualised has implications for understanding human–machine interactions and the governance of technology, especially artificial intelligence (AI) systems. Traditionally, agency is conceptualised as a capacity, defined by intrinsic properties, such as cognitive or volitional facilities. I argue that the capacity-based account of agency is inadequate to explain the dynamics of human–machine interactions and guide technology governance. Instead, I propose to conceptualise agency as impact. Agents as impactful entities can be identified at different levels: from the (...)
- Challenges as catalysts: how Waymo’s Open Dataset Challenges shape AI development.Sam Hind, Fernando N. van der Vlist & Max Kanderske - forthcoming - AI and Society:1-17.Artificial intelligence (AI) and machine learning (ML) are becoming increasingly significant areas of research for scholars in science and technology studies (STS) and media studies. In March 2020, Waymo, Google/Alphabet’s autonomous vehicle project, introduced the ‘Open Dataset Virtual Challenge’, an annual competition leveraging their Waymo Open Dataset. This freely accessible dataset comprises annotated autonomous vehicle data from their own Waymo vehicles. Yearly, Waymo has continued to host iterations of this challenge, inviting teams of computer scientists to tackle evolving machine learning (...)
- Innovation, risk and control: The true trend is ‘from tool to purpose’—A discussion on the standardization of AI.Oriana Chaves - forthcoming - AI and Society:1-12.In this text, our question is what is the current regulatory trend in countries that are not considered central in the development of artificial intelligence, such as Brazil: a preventive approach, or an experimental approach? We will analyze the bills (PL) that are being processed in legislative houses at the state level, and at the federal level, highlighting some elements, such as: Delimitation of the object (conceptualization), fundamental principles, ethical guidelines, relationship with human work, human supervision, and guidelines for public (...)
- Responsible research in artificial intelligence: lessons from the past.Puneet Sharma - forthcoming - AI and Society:1-2.
- Explanation and connectionist models.Catherine Stinson - 2018 - In Mark Sprevak & Matteo Colombo (eds.), The Routledge Handbook of the Computational Mind. Routledge. pp. 120-133.This chapter explores the epistemic roles played by connectionist models of cognition, and offers a formal analysis of how connectionist models explain. It looks at how other types of computational models explain. Classical artificial intelligence (AI) programs explain using abductive reasoning, or inference to the best explanation; they begin with the phenomena to be explained, and devise rules that can produce the right outcome. The chapter also looks at several examples of connectionist models of cognition, observing what sorts of constraints (...)
- Philosophy of education in a changing digital environment: an epistemological scope of the problem.Raigul Salimova, Jamilya Nurmanbetova, Maira Kozhamzharova, Mira Manassova & Saltanat Aubakirova - forthcoming - AI and Society:1-12.The relevance of this study's topic is supported by the argument that a philosophical understanding of the fundamental concepts of epistemology as they pertain to the educational process is crucial as the educational setting becomes increasingly digitalised. This paper aims to explore the epistemological component of the philosophy of learning in light of the educational process digitalisation. The research comprised a sample of 462 university students from Kazakhstan, with 227 participants assigned to the experimental and 235 to the control groups. (...)
- NHỮNG TRIẾT LÝ NHÂN VĂN TRONG TẦM NHÌN XÃ HỘI 5.0 TẠI NHẬT BẢN VÀ MỘT VÀI GỢI Ý CHO VIỆT NAM.Manh-Tung Ho & Phuong Thao Luu - manuscriptBài viết này tóm lược các điểm quan trọng và những triết lý xã hội trong Tầm nhìn Xã hội 5.0 (Society 5.0) của Nhật Bản, đồng thời đưa ra bài học cho Việt Nam trong việc hình thành một xã hội “lấy dân làm gốc”, được hiện thực hoá bởi trí tuệ nhân tạo (AI). Nhằm tiến tới một xã hội nơi con người được đặt làm trung tâm đồng thời chung sống hài hoà với công nghệ ngày càng (...)
- Engineering AI for provable retention of objectives over time.Adeniyi Fasoro - 2024 - AI Magazine 45 (2):1-11.I argue that ensuring artificial intelligence (AI) retains alignment with human values over time is critical yet understudied. Most research focuses on static alignment, neglecting crucial retention dynamics enabling stability during learning and autonomy. This paper elucidates limitations constraining provable retention, arguing key gaps include formalizing dynamics, transparency of advanced systems, participatory scaling, and risks of uncontrolled recursive self-improvement. I synthesize technical and ethical perspectives into a conceptual framework grounded in control theory and philosophy to analyze dynamics. I argue priorities (...)
- Book review: Luca Possati (2021): “The algorithmic unconscious: how psychoanalysis helps in understanding AI” (Routledge). [REVIEW]Marc Cheong - 2024 - AI and Society 39 (2):819-821.
- The misdirected approach of open source algorithms.Joshua L. M. Brand - 2024 - AI and Society 39 (2):807-808.
- Algorithmic biases: caring about teens’ neurorights.José M. Muñoz & José Ángel Marinaro - 2024 - AI and Society 39 (2):809-810.
- The scientist of the scientist.Tomer Simon - 2024 - AI and Society 39 (2):803-804.
- Thinking about the mind-technology problem.Manh-Tung Ho - 2024 - AI and Society 39 (2):823-824.
- Neurorights: the Chilean constitutional change.Allan McCay - 2024 - AI and Society 39 (2):797-798.
- Toward the symbiocene through artificial intelligence.Amar Singh & Shipra Tholia - 2024 - AI and Society 39 (2):805-806.
- From the essence of humanity to the essence of intelligence, and AI in the future society.Yehui Zhang - forthcoming - AI and Society:1-9.Fear and concerns regarding AI and robots have existed for a long time, and the emergence of strong artificial intelligence, on par with human intelligence, is likely just a few decades away. The primary purpose of this article is to establish a theoretical framework for navigating the relationship between humans and this advanced form of artificial intelligence. This article first points out that the most fundamental characteristic of life is its continuous process of evolution and iteration. By analyzing the developmental (...)
- Hic sunt leones. User orientation as a design principle for emerging institutions on social media platforms.Lavinia Marin & Constantin Vică - forthcoming - AI and Society:1-14.The phenomenon of missed interactions between online users is a specific issue occurring when users of different language games interact on social media platforms. We use the lens of institutional theory to analyze this phenomenon and argue that current online institutions will necessarily fail to regulate user interactions in a way that creates common meanings because online institutions are not set up to deal with the multiplicity of language games and forms of life co-existing in the online social space. We (...)
- The open texture of ‘algorithm’ in legal language.Davide Baldini & Matteo De Benedetto - forthcoming - AI and Society.In this paper, we will survey the different uses of the term algorithm in contemporary legal practice. We will argue that the concept of algorithm currently exhibits a substantial degree of open texture, co-determined by the open texture of the concept of algorithm itself and by the open texture inherent to legal discourse. We will substantiate our argument by virtue of a case study, in which we analyze a recent jurisprudential case where the first and second-degree judges have carved-out contrasting (...)
- Citizens’ data afterlives: Practices of dataset inclusion in machine learning for public welfare.Helene Friis Ratner & Nanna Bonde Thylstrup - forthcoming - AI and Society:1-11.Public sector adoption of AI techniques in welfare systems recasts historic national data as resource for machine learning. In this paper, we examine how the use of register data for development of predictive models produces new ‘afterlives’ for citizen data. First, we document a Danish research project’s practical efforts to develop an algorithmic decision-support model for social workers to classify children’s risk of maltreatment. Second, we outline the tensions emerging from project members’ negotiations about which datasets to include. Third, we (...)
- Sticks and stones may break my bones, but words will never hurt me!—Navigating the cybersecurity risks of generative AI.Abdur Rahman Bin Shahid & Ahmed Imteaj - forthcoming - AI and Society:1-2.
- Opening the black boxes of the black carpet in the era of risk society: a sociological analysis of AI, algorithms and big data at work through the case study of the Greek postal services.Christos Kouroutzas & Venetia Palamari - forthcoming - AI and Society:1-14.This article draws on contributions from the Sociology of Science and Technology and Science and Technology Studies, the Sociology of Risk and Uncertainty, and the Sociology of Work, focusing on the transformations of employment regarding expanded automation, robotization and informatization. The new work patterns emerging due to the introduction of software and hardware technologies, which are based on artificial intelligence, algorithms, big data gathering and robotic systems are examined closely. This article attempts to “open the black boxes” of the “black (...)
- The shift of Artificial Intelligence research from academia to industry: implications and possible future directions.Miguel Angelo de Abreu de Sousa - forthcoming - AI and Society:1-10.The movement of Artificial Intelligence (AI) research from universities to big corporations has had a significant impact on the development of the field. In the past, AI research was primarily conducted in academic institutions, which foster a culture of peer reviewing and collaboration to enhance quality improvements. The growing interest in AI among corporations, especially regarding Machine Learning (ML) technology, has shifted the focus of research from quality to quantity. Corporations have the resources to invest in large-scale ML projects and (...)
- Assessing the impact of heat vulnerability on urban public spaces using a fuzzy-based unified computational technique.Rajeev Kumar & Saswat Kishore Mishra - forthcoming - AI and Society:1-18.Over the years, the urban heat vulnerability has evolved as a pressing global concern for researchers and policymakers alike. Numerous studies have aimed at mitigating the adverse effects of urban heat vulnerability on public health and safety. However, the critical task of selecting the most fitting indicator for urban heat islands in public spaces is not emphasized in the existing studies, considering the diverse indices available. Beyond identification, studies that delve into the prioritization of these indices and the determination of (...)
- Emerging contours of geopolitics and state in the digital era.Arun Teja Polcumpally, Megha Shrivastava & Shashank S. Patel - forthcoming - AI and Society:1-5.This review essay provides a critical analysis of the book ‘The Great Tech Game,’ authored by Anirudh Suri. For the analysis, other literature published in a similar area is considered and pitched the arguments against the ones made in the book. During the year this book was released, there were numerous debates on accountability and trust in frontier digital technologies like AI. These debates have reached a systemic level where the entire global community is divided into two camps headed by (...)
- When will the blind be able to take their first steps with GDR guidance under artificial intelligence?Meimei Chen & Bin Hong - forthcoming - AI and Society:1-3.
- Using rhetorical strategies to design prompts: a human-in-the-loop approach to make AI useful.Nupoor Ranade, Marly Saravia & Aditya Johri - forthcoming - AI and Society:1-22.The growing capabilities of artificial intelligence (AI) word processing models have demonstrated exceptional potential to impact language related tasks and functions. Their fast pace of adoption and probable effect has also given rise to controversy within certain fields. Models, such as GPT-3, are a particular concern for professionals engaged in writing, particularly as their engagement with these technologies is limited due to lack of ability to control their output. Most efforts to maximize and control output rely on a process known (...)
- Eliza and the artist.Karamjit S. Gill - forthcoming - AI and Society:1-4.
- Give the machine a chance, human experts ain’t that great….Petr Špecián & Lucy Císař Brown - 2024 - AI and Society 2024:1-2.Despite their flaws, large language models (LLMs) deserve a fair chance to prove their mettle against human experts, who are often plagued with biases, conflicts of interest, and other frailties. For epistemically unprivileged laypeople struggling to access expert knowledge, the accessibility advantages of LLMs could prove crucial. While complaints about LLMs' inconsistencies and arguments for human superiority are often justified (for now), they distract from the urgent need to prepare for the likely scenario of LLMs' continued ascent. Experimentation with both (...)
- AI safety: necessary, but insufficient and possibly problematic.Deepak P. - forthcoming - AI and Society:1-3.
- Bridging divides: Empathy-augmenting technologies and cultural soul-searching.Manh-Tung Ho & Manh-Toan Ho - forthcoming - AI and Society:1-3.
- AI statecraft heating-up: the automation of governance through Canada’s Chinook case study.Nicolas Chartier-Edwards, Marek Blottiere & Jonathan Roberge - forthcoming - AI and Society:1-10.In the years 2020–2021, journalists, lawyers, scholars, and civil society actors noticed an unusual spike in the refusal of francophone African immigrants in Québec, Canada. While Immigration, refugee and citizenship Canada’s systemic racism problem were already documented, the novelty appeared to be how standardized and sometimes, “nonsensical” the reasons given to many of the applicants were. This eventually prompted a lawsuit against IRCC in which it was revealed that a new piece of software called “Chinook” had been deployed since 2018, (...)
- Responsible automatically processable regulation.Clement Guitton, Simon Mayer, Aurelia Tamo-Larrieux, Dimitri Van Landuyt, Eduard Fosch-Villaronga, Irene Kamara & Przemysław Pałka - forthcoming - AI and Society:1-16.Driven by the increasing availability and deployment of ubiquitous computing technologies across our private and professional lives, implementations of automatically processable regulation (APR) have evolved over the past decade from academic projects to real-world implementations by states and companies. There are now pressing issues that such encoded regulation brings about for citizens and society, and strategies to mitigate these issues are required. However, _comprehensive yet practically operationalizable_ frameworks to navigate the complex interactions and evaluate the risks of projects that implement (...)
| 1 — 50 / 15040 |
PhilPapers logo by Andrea Andrews and Meghan Driscoll.
This site uses cookies and Google Analytics (see our terms & conditions for details regarding the privacy implications). Use of this site is subject to terms & conditions.
All rights reserved by The PhilPapers Foundation
Server: philpapers-web-7c76586df8-nx2x8 uwo