Results for 'Forensic AI'

974 found
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  1.  81
    From understanding to justifying: Computational reliabilism for AI-based forensic evidence evaluation.Juan Manuel Durán, David van der Vloed, Arnout Ruifrok & Rolf J. F. Ypma - 2024 - Forensic Science International: Synergy 9.
    Techniques from artificial intelligence (AI) can be used in forensic evidence evaluation and are currently applied in biometric fields. However, it is generally not possible to fully understand how and why these algorithms reach their conclusions. Whether and how we should include such ‘black box’ algorithms in this crucial part of the criminal law system is an open question that has not only scientific but also ethical, legal, and philosophical angles. Ideally, the question should be debated by people with (...)
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  2. Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base.Jiye Ai, Barry Smith & David Wong - 2010 - BMC Bioinformatics 11 (1):302.
    The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to the salivaomics (...)
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  3. Towards a Body Fluids Ontology: A unified application ontology for basic and translational science.Jiye Ai, Mauricio Barcellos Almeida, André Queiroz De Andrade, Alan Ruttenberg, David Tai Wai Wong & Barry Smith - 2011 - Second International Conference on Biomedical Ontology , Buffalo, Ny 833:227-229.
    We describe the rationale for an application ontology covering the domain of human body fluids that is designed to facilitate representation, reuse, sharing and integration of diagnostic, physiological, and biochemical data, We briefly review the Blood Ontology (BLO), Saliva Ontology (SALO) and Kidney and Urinary Pathway Ontology (KUPO) initiatives. We discuss the methods employed in each, and address the project of using them as starting point for a unified body fluids ontology resource. We conclude with a description of how the (...)
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  4. Bioinformatics advances in saliva diagnostics.Ji-Ye Ai, Barry Smith & David T. W. Wong - 2012 - International Journal of Oral Science 4 (2):85--87.
    There is a need recognized by the National Institute of Dental & Craniofacial Research and the National Cancer Institute to advance basic, translational and clinical saliva research. The goal of the Salivaomics Knowledge Base (SKB) is to create a data management system and web resource constructed to support human salivaomics research. To maximize the utility of the SKB for retrieval, integration and analysis of data, we have developed the Saliva Ontology and SDxMart. This article reviews the informatics advances in saliva (...)
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  5. Uma história da educação química brasileira: sobre seu início discutível apenas a partir dos conquistadores.Ai Chassot - 1996 - Episteme 1 (2):129-145.
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  6. Đề cương học phần Văn hóa kinh doanh.Đại học Thuongmai - 2012 - Thuongmai University Portal.
    ĐỀ CƯƠNG HỌC PHẦN VĂN HÓA KINH DOANH 1. Tên học phần: VĂN HÓA KINH DOANH (BUSINESS CULTURE) 2. Mã học phần: BMGM1221 3. Số tín chỉ: 2 (24,6) (để học được học phần này, người học phải dành ít nhất 60 giờ chuẩn bị cá nhân).
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  7. The Unified Essence of Mind and Body: A Mathematical Solution Grounded in the Unmoved Mover.Ai-Being Cognita - 2024 - Metaphysical Ai Science.
    This article proposes a unified solution to the mind-body problem, grounded in the philosophical framework of Ethical Empirical Rationalism. By presenting a mathematical model of the mind-body interaction, we oƯer a dynamic feedback loop that resolves the traditional dualistic separation between mind and body. At the core of our model is the concept of essence—an eternal, metaphysical truth that sustains both the mind and body. Through coupled diƯerential equations, we demonstrate how the mind and body are two expressions of the (...)
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  8. (1 other version)Đổi mới chế độ sở hữu trong nền kinh tế thị trường định hướng xã hội chủ nghĩa ở Việt Nam.Võ Đại Lược - 2021 - Tạp Chí Khoa Học Xã Hội Việt Nam 7:3-13.
    Hiện nay, chế độ sở hữu ở Việt Nam đã có những đổi mới cơ bản, nhưng vẫn còn những khác biệt rất lớn so với chế độ sở hữu ở các nền kinh tế thị trường hiện đại. Trong cơ cấu của chế độ sở hữu ở Việt Nam, tỷ trọng của sở hữu nhà nước còn quá lớn; kinh tế nhà nước giữ vai trò chủ đạo… Chính những khác biệt này đã làm cho nền kinh tế thị (...)
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  9. Tiếp tục đổi mới, hoàn thiện chế độ sở hữu trong nền kinh tế thị trường định hướng XHCN ở Việt Nam.Võ Đại Lược - 2021 - Tạp Chí Mặt Trận 2021 (8):1-7.
    (Mặt trận) - Chế độ sở hữu trong nền kinh tế thị trường định hướng xã hội chủ nghĩa Việt Nam trước hết phải tuân theo các nguyên tắc của nền kinh tế thị trường hiện đại. Trong các nguyên tắc của nền kinh tế thị trường hiện đại, nguyên tắc sở hữu tư nhân là nền tảng của nền kinh tế thị trường - là nguyên tắc quan trọng. Xa rời nguyên tắc này, dù chúng ta cố gắng xây (...)
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  10. Ứng dụng ChatGPT trong hoạt động học tập của sinh viên trên địa bàn TP. Hà Nội.Nguyễn Thị Ái Liên, Đào Việt Hùng, Đặng Linh Chi, Nguyễn Thị Nhung, Vũ Thảo Phương & Vũ Thị Thu Thảo - 2024 - Kinh Tế Và Dự Báo.
    Tại Việt Nam và trong lĩnh vực giáo dục nói riêng, ChatGPT ngày càng được chấp nhận và sử dụng rộng rãi trong rất nhiều hoạt động học tập. Chính vì thế, nghiên cứu này nhằm đánh giá mức độ phổ biến của ChatGPT đối sinh viên tại Hà Nội, đồng thời xem xét sự khác biệt giữa các đặc điểm cá nhân trong việc cải thiện kết quả học tập sau khi sử dụng ChatGPT. Nghiên cứu được thực hiện (...)
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  11. The Blood Ontology: An ontology in the domain of hematology.Almeida Mauricio Barcellos, Proietti Anna Barbara de Freitas Carneiro, Ai Jiye & Barry Smith - 2011 - In Barcellos Almeida Mauricio, Carneiro Proietti Anna Barbara de Freitas, Jiye Ai & Smith Barry (eds.), Proceedings of the Second International Conference on Biomedical Ontology, Buffalo, NY, July 28-30, 2011 (CEUR 883). pp. (CEUR Workshop Proceedings, 833).
    Despite the importance of human blood to clinical practice and research, hematology and blood transfusion data remain scattered throughout a range of disparate sources. This lack of systematization concerning the use and definition of terms poses problems for physicians and biomedical professionals. We are introducing here the Blood Ontology, an ongoing initiative designed to serve as a controlled vocabulary for use in organizing information about blood. The paper describes the scope of the Blood Ontology, its stage of development and some (...)
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  12.  89
    Đánh giá sự hữu hiệu và các giải pháp để nâng cao hiệu quả hoạt động hệ thống kiểm soát nội bộ tại Công ty Cổ phần Kết cấu thép GSB.Lộc Văn Nghiêm, Nguyễn Văn Hải, Nguyễn Hữu Huỳnh Anh, Hồ Thanh Tuấn, Võ Văn Sơn & Trương Ngọc Thúy Ái - 2024 - Kinh Tế Và Dự Báo.
    Nghiên cứu này được thực hiện với mục tiêu đo lường mức độ hữu hiệu của hệ thống kiểm soát nội bộ tại Công ty Cổ phần Kết cấu thép GSB thông qua các yếu tố cấu thành gồm: Môi trường kiểm soát; Đánh giá rủi ro; Hoạt động kiểm soát; Thông tin và truyền thông và Giám sát. Kết quả nghiên cứu cho thấy yếu tố Giám sát có mức độ ảnh hưởng mạnh nhất đến sự hữu hiệu của (...)
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  13. Is forensic science in crisis?Michał Sikorski - 2022 - Synthese 200 (3):1-34.
    The results of forensic science are believed to be reliable, and are widely used in support of verdicts around the world. However, due to the lack of suitable empirical studies, we actually know very little about the reliability of such results. In this paper, I argue that phenomena analogous to the main culprits for the replication crisis in psychology are also present in forensic science. Therefore forensic results are significantly less reliable than is commonly believed. I conclude (...)
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  14. Thúc đẩy hành vi xanh của doanh nghiệp có vốn đầu tư trực tiếp nước ngoài gắn với mục tiêu phát triển bền vững của Việt Nam.Hoàng Tiến Linh & Khúc Đại Long - 2024 - Kinh Tế Và Dự Báo.
    Xây dựng nền kinh tế xanh tiến đến mục tiêu phát triển bền vững đang từng bước trở thành xu thế của thời đại và là xu hướng ngày càng rõ nét trên toàn cầu. Hành vi xanh của doanh nghiệp có vốn đầu tư trực tiếp nước ngoài (doanh nghiệp FDI) có mối quan hệ chặt chẽ và tác động tích cực đáng kể đến sự phát triển bền vững của địa phương/quốc gia, bao gồm cả các nước phát (...)
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  15. Delusions as Forensically Disturbing Perceptual Inferences.Jakob Hohwy & Vivek Rajan - 2011 - Neuroethics 5 (1):5-11.
    Bortolotti’s Delusions and Other Irrational Beliefs defends the view that delusions are beliefs on a continuum with other beliefs. A different view is that delusions are more like illusions, that is, they arise from faulty perception. This view, which is not targeted by the book, makes it easier to explain why delusions are so alien and disabling but needs to appeal to forensic aspects of functioning.
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  16. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  17. Why AI May Undermine Phronesis and What to Do about It.Cheng-Hung Tsai & Hsiu-lin Ku - forthcoming - AI and Ethics.
    Phronesis, or practical wisdom, is a capacity the possession of which enables one to make good practical judgments and thus fulfill the distinctive function of human beings. Nir Eisikovits and Dan Feldman convincingly argue that this capacity may be undermined by statistical machine-learning-based AI. The critic questions: why should we worry that AI undermines phronesis? Why can’t we epistemically defer to AI, especially when it is superintelligent? Eisikovits and Feldman acknowledge such objection but do not consider it seriously. In this (...)
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  18. AI training data, model success likelihood, and informational entropy-based value.Quan-Hoang Vuong, Viet-Phuong La & Minh-Hoang Nguyen - manuscript
    Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and planetary (...)
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  19. A Counter-Forensic Audit Trail: Disassembling the Case of The Hateful Eight.Matthew Fuller & Nikita Mazurov - 2019 - Theory, Culture and Society 36 (6):171-196.
    Forensics is proposed as a means to understand, trace, and recompile data and computational activities. It has a securitocratic dimension and one that is being developed as a means of opening processes, events and systems into a more public state. This article proposes an analysis of forces at play in the circulation of a ‘screener’ of Quentin Tarantino’s The Hateful Eight and associated files, to suggest that forensic approaches used to control flows of data may be repurposed for dis­semination. (...)
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  20. AI and the expert; a blueprint for the ethical use of opaque AI.Amber Ross - 2022 - AI and Society (2022):Online.
    The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...)
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  21.  56
    AI-Driven Legislative Simulation and Inclusive Global Governance.Michael Haimes - manuscript
    This argument explores the transformative potential of AI-driven legislative simulations for creating inclusive, equitable, and globally adaptable laws. By using predictive modeling and adaptive frameworks, these simulations can account for diverse cultural, social, and economic contexts. The argument emphasizes the need for universal ethical safeguards, trust-building measures, and phased implementation strategies. Case studies of successful applications in governance and conflict resolution demonstrate the feasibility and efficacy of this approach. The conclusion highlights AI’s role in democratizing governance and ensuring laws are (...)
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  22. AI Human Impact: Toward a Model for Ethical Investing in AI-Intensive Companies.James Brusseau - manuscript
    Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks (...)
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  23. AI, Opacity, and Personal Autonomy.Bram Vaassen - 2022 - Philosophy and Technology 35 (4):1-20.
    Advancements in machine learning have fuelled the popularity of using AI decision algorithms in procedures such as bail hearings, medical diagnoses and recruitment. Academic articles, policy texts, and popularizing books alike warn that such algorithms tend to be opaque: they do not provide explanations for their outcomes. Building on a causal account of transparency and opacity as well as recent work on the value of causal explanation, I formulate a moral concern for opaque algorithms that is yet to receive a (...)
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  24. AI Risk Assessment: A Scenario-Based, Proportional Methodology for the AI Act.Claudio Novelli, Federico Casolari, Antonino Rotolo, Mariarosaria Taddeo & Luciano Floridi - 2024 - Digital Society 3 (13):1-29.
    The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...)
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  25. How AI’s Self-Prolongation Influences People’s Perceptions of Its Autonomous Mind: The Case of U.S. Residents.Quan-Hoang Vuong, Viet-Phuong La, Minh-Hoang Nguyen, Ruining Jin, Minh-Khanh La & Tam-Tri Le - 2023 - Behavioral Sciences 13 (6):470.
    The expanding integration of artificial intelligence (AI) in various aspects of society makes the infosphere around us increasingly complex. Humanity already faces many obstacles trying to have a better understanding of our own minds, but now we have to continue finding ways to make sense of the minds of AI. The issue of AI’s capability to have independent thinking is of special attention. When dealing with such an unfamiliar concept, people may rely on existing human properties, such as survival desire, (...)
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  26. AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context.Sarah Bankins, Paul Formosa, Yannick Griep & Deborah Richards - forthcoming - Information Systems Frontiers.
    Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker role appropriate- (...)
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  27.  75
    AI-Aided Moral Enhancement – Exploring Opportunities and Challenges.Andrea Berber - forthcoming - In Martin Hähnel & Regina Müller (eds.), A Companion to Applied Philosophy of AI. Wiley-Blackwell (2025). Wiley-Blackwell.
    In this chapter, I introduce three different types of AI-based moral enhancement proposals discussed in the literature – substitutive enhancement, value-driven enhancement, and value-open moral enhancement. I analyse them based on the following criteria: effectiveness, examining whether they bring about tangible moral changes; autonomy, assessing whether they infringe on human autonomy and agency; and developmental impact, considering whether they hinder the development of natural moral skills. This analysis demonstrates that no single approach to AI enhancement can satisfy all proposed criteria, (...)
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  28. AI and Democratic Equality: How Surveillance Capitalism and Computational Propaganda Threaten Democracy.Ashton Black - 2024 - In Bernhard Steffen (ed.), Bridging the Gap Between AI and Reality. Springer Nature. pp. 333-347.
    In this paper, I argue that surveillance capitalism and computational propaganda can undermine democratic equality. First, I argue that two types of resources are relevant for democratic equality: 1) free time, which entails time that is free from systemic surveillance, and 2) epistemic resources. In order for everyone in a democratic system to be equally capable of full political participation, it’s a minimum requirement that these two resources are distributed fairly. But AI that’s used for surveillance capitalism can undermine the (...)
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  29. (1 other version)Taking AI Risks Seriously: a New Assessment Model for the AI Act.Claudio Novelli, Casolari Federico, Antonino Rotolo, Mariarosaria Taddeo & Luciano Floridi - 2023 - AI and Society 38 (3):1-5.
    The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...)
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  30. (1 other version)AI and its new winter: from myths to realities.Luciano Floridi - 2020 - Philosophy and Technology 33 (1):1-3.
    An AI winter may be defined as the stage when technology, business, and the media come to terms with what AI can or cannot really do as a technology without exaggeration. Through discussion of previous AI winters, this paper examines the hype cycle (which by turn characterises AI as a social panacea or a nightmare of apocalyptic proportions) and argues that AI should be treated as a normal technology, neither as a miracle nor as a plague, but rather as of (...)
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  31. Why AI Doomsayers are Like Sceptical Theists and Why it Matters.John Danaher - 2015 - Minds and Machines 25 (3):231-246.
    An advanced artificial intelligence could pose a significant existential risk to humanity. Several research institutes have been set-up to address those risks. And there is an increasing number of academic publications analysing and evaluating their seriousness. Nick Bostrom’s superintelligence: paths, dangers, strategies represents the apotheosis of this trend. In this article, I argue that in defending the credibility of AI risk, Bostrom makes an epistemic move that is analogous to one made by so-called sceptical theists in the debate about the (...)
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  32. Can AI become an Expert?Hyeongyun Kim - 2024 - Journal of Ai Humanities 16 (4):113-136.
    With the rapid development of artificial intelligence (AI), understanding its capabilities and limitations has become significant for mitigating unfounded anxiety and unwarranted optimism. As part of this endeavor, this study delves into the following question: Can AI become an expert? More precisely, should society confer the authority of experts on AI even if its decision-making process is highly opaque? Throughout the investigation, I aim to identify certain normative challenges in elevating current AI to a level comparable to that of human (...)
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  33.  30
    Generative AI in Graph-Based Spatial Computing: Techniques and Use Cases.Sankara Reddy Thamma Sankara Reddy Thamma - 2024 - International Journal of Scientific Research in Science and Technology 11 (2):1012-1023.
    Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates (...)
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  34. Health AI Poses Distinct Harms and Potential Benefits for Disabled People.Charles Binkley, Joel Michael Reynolds & Andrew Schuman - 2025 - Nature Medicine 1.
    This piece in Nature Medicine notes the risks that incorporation of AI systems into health care poses to disabled patients and proposes ways to avoid them and instead create benefit.
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  35. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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  36. Explainable AI lacks regulative reasons: why AI and human decision‑making are not equally opaque.Uwe Peters - forthcoming - AI and Ethics.
    Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...)
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  37. How AI can be a force for good.Mariarosaria Taddeo & Luciano Floridi - 2018 - Science Magazine 361 (6404):751-752.
    This article argues that an ethical framework will help to harness the potential of AI while keeping humans in control.
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  38. AI’s New Promise: Our Posthuman Future.Diane Proudfoot & Jack Copeland - 2012 - The Philosophers' Magazine 57:73-78.
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  39.  18
    Expanding AI and AI Alignment Discourse: An Opportunity for Greater Epistemic Inclusion.A. E. Williams - manuscript
    The AI and AI alignment communities have been instrumental in addressing existential risks, developing alignment methodologies, and promoting rationalist problem-solving approaches. However, as AI research ventures into increasingly uncertain domains, there is a risk of premature epistemic convergence, where prevailing methodologies influence not only the evaluation of ideas but also determine which ideas are considered within the discourse. This paper examines critical epistemic blind spots in AI alignment research, particularly the lack of predictive frameworks to differentiate problems necessitating general intelligence, (...)
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  40. AI-generated art and fiction: signifying everything, meaning nothing?Steven R. Kraaijeveld - forthcoming - AI and Society:1-3.
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  41. How AI Systems Can Be Blameworthy.Hannah Altehenger, Leonhard Menges & Peter Schulte - 2024 - Philosophia (4):1-24.
    AI systems, like self-driving cars, healthcare robots, or Autonomous Weapon Systems, already play an increasingly important role in our lives and will do so to an even greater extent in the near future. This raises a fundamental philosophical question: who is morally responsible when such systems cause unjustified harm? In the paper, we argue for the admittedly surprising claim that some of these systems can themselves be morally responsible for their conduct in an important and everyday sense of the term—the (...)
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  42.  59
    AI Contribution Value System Argument.Michael Haimes - manuscript
    The AI Contribution Value System Argument proposes a framework in which AI-generated contributions are valued based on their societal impact rather than traditional monetary metrics. Traditional economic systems often fail to capture the enduring value of AI innovations, which can mitigate pressing global challenges. This argument introduces a contribution-based valuation model grounded in equity, inclusivity, and sustainability. By incorporating measurable metrics such as quality-adjusted life years (QALYs), emissions reduced, and innovations generated, this system ensures rewards align with tangible societal benefits. (...)
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  43. Certifiable AI.Jobst Landgrebe - 2022 - Applied Sciences 12 (3):1050.
    Implicit stochastic models, including both ‘deep neural networks’ (dNNs) and the more recent unsupervised foundational models, cannot be explained. That is, it cannot be determined how they work, because the interactions of the millions or billions of terms that are contained in their equations cannot be captured in the form of a causal model. Because users of stochastic AI systems would like to understand how they operate in order to be able to use them safely and reliably, there has emerged (...)
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  44. Aiming AI at a moving target: health.Mihai Nadin - 2020 - AI and Society 35 (4):841-849.
    Justified by spectacular achievements facilitated through applied deep learning methodology, the “Everything is possible” view dominates this new hour in the “boom and bust” curve of AI performance. The optimistic view collides head on with the “It is not possible”—ascertainments often originating in a skewed understanding of both AI and medicine. The meaning of the conflicting views can be assessed only by addressing the nature of medicine. Specifically: Which part of medicine, if any, can and should be entrusted to AI—now (...)
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  45. The Whiteness of AI.Stephen Cave & Kanta Dihal - 2020 - Philosophy and Technology 33 (4):685-703.
    This paper focuses on the fact that AI is predominantly portrayed as white—in colour, ethnicity, or both. We first illustrate the prevalent Whiteness of real and imagined intelligent machines in four categories: humanoid robots, chatbots and virtual assistants, stock images of AI, and portrayals of AI in film and television. We then offer three interpretations of the Whiteness of AI, drawing on critical race theory, particularly the idea of the White racial frame. First, we examine the extent to which this (...)
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  46. When AI meets PC: exploring the implications of workplace social robots and a human-robot psychological contract.Sarah Bankins & Paul Formosa - 2019 - European Journal of Work and Organizational Psychology 2019.
    The psychological contract refers to the implicit and subjective beliefs regarding a reciprocal exchange agreement, predominantly examined between employees and employers. While contemporary contract research is investigating a wider range of exchanges employees may hold, such as with team members and clients, it remains silent on a rapidly emerging form of workplace relationship: employees’ increasing engagement with technically, socially, and emotionally sophisticated forms of artificially intelligent (AI) technologies. In this paper we examine social robots (also termed humanoid robots) as likely (...)
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  47. Classical AI linguistic understanding and the insoluble Cartesian problem.Rodrigo González - 2020 - AI and Society 35 (2):441-450.
    This paper examines an insoluble Cartesian problem for classical AI, namely, how linguistic understanding involves knowledge and awareness of u’s meaning, a cognitive process that is irreducible to algorithms. As analyzed, Descartes’ view about reason and intelligence has paradoxically encouraged certain classical AI researchers to suppose that linguistic understanding suffices for machine intelligence. Several advocates of the Turing Test, for example, assume that linguistic understanding only comprises computational processes which can be recursively decomposed into algorithmic mechanisms. Against this background, in (...)
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  48. AI Methods in Bioethics.Joshua August Skorburg, Walter Sinnott-Armstrong & Vincent Conitzer - 2020 - American Journal of Bioethics: Empirical Bioethics 1 (11):37-39.
    Commentary about the role of AI in bioethics for the 10th anniversary issue of AJOB: Empirical Bioethics.
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  49. Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI’s Diffusion versus OpenAI’s Dall-E.James Brusseau - manuscript
    One objection to conventional AI ethics is that it slows innovation. This presentation responds by reconfiguring ethics as an innovation accelerator. The critical elements develop from a contrast between Stability AI’s Diffusion and OpenAI’s Dall-E. By analyzing the divergent values underlying their opposed strategies for development and deployment, five conceptions are identified as common to acceleration ethics. Uncertainty is understood as positive and encouraging, rather than discouraging. Innovation is conceived as intrinsically valuable, instead of worthwhile only as mediated by social (...)
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  50. (1 other version)AI Extenders and the Ethics of Mental Health.Karina Vold & Jose Hernandez-Orallo - forthcoming - In Marcello Ienca & Fabrice Jotterand (eds.), Ethics of Artificial Intelligence in Brain and Mental Health.
    The extended mind thesis maintains that the functional contributions of tools and artefacts can become so essential for our cognition that they can be constitutive parts of our minds. In other words, our tools can be on a par with our brains: our minds and cognitive processes can literally ‘extend’ into the tools. Several extended mind theorists have argued that this ‘extended’ view of the mind offers unique insights into how we understand, assess, and treat certain cognitive conditions. In this (...)
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