Results for 'Gendered AI'

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  1. The Struggle for AI’s Recognition: Understanding the Normative Implications of Gender Bias in AI with Honneth’s Theory of Recognition.Rosalie Waelen & Michał Wieczorek - 2022 - Philosophy and Technology 35 (2).
    AI systems have often been found to contain gender biases. As a result of these gender biases, AI routinely fails to adequately recognize the needs, rights, and accomplishments of women. In this article, we use Axel Honneth’s theory of recognition to argue that AI’s gender biases are not only an ethical problem because they can lead to discrimination, but also because they resemble forms of misrecognition that can hurt women’s self-development and self-worth. Furthermore, we argue that Honneth’s theory of recognition (...)
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  2. A plea for integrated empirical and philosophical research on the impacts of feminized AI workers.Hannah Read, Javier Gomez-Lavin, Andrea Beltrama & Lisa Miracchi Titus - 2022 - Analysis (1):89-97.
    Feminist philosophers have long emphasized the ways in which women’s oppression takes a variety of forms depending on complex combinations of factors. These include women’s objectification, dehumanization and unjust gendered divisions of labour caused in part by sexist ideologies regarding women’s social role. This paper argues that feminized artificial intelligence (feminized AI) poses new and important challenges to these perennial feminist philosophical issues. Despite the recent surge in theoretical and empirical attention paid to the ethics of AI in general, (...)
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  3. More than Skin Deep: a Response to “The Whiteness of AI”.Shelley Park - 2021 - Philosophy and Technology 34 (4):1961-1966.
    This commentary responds to Stephen Cave and Kanta Dihal’s call for further investigations of the whiteness of AI. My response focuses on three overlapping projects needed to more fully understand racial bias in the construction of AI and its representations in pop culture: unpacking the intersections of gender and other variables with whiteness in AI’s construction, marketing, and intended functions; observing the many different ways in which whiteness is scripted, and noting how white racial framing exceeds white casting and thus (...)
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  4. Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution.Flor Miriam Plaza-del Arco, Amanda Cercas Curry & Alba Curry - 2024 - Arxiv.
    Large language models (LLMs) reflect societal norms and biases, especially about gender. While societal biases and stereotypes have been extensively researched in various NLP applications, there is a surprising gap for emotion analysis. However, emotion and gender are closely linked in societal discourse. E.g., women are often thought of as more empathetic, while men's anger is more socially accepted. To fill this gap, we present the first comprehensive study of gendered emotion attribution in five state-of-the-art LLMs (open- and closed-source). (...)
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  5. Age and Gender Classification Using Deep Learning - VGG16.Aysha I. Mansour & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (7):50-59.
    Abstract: Age and gender classification has been around for a long time, and efforts are still being made to improve the findings. This has been the case since the inception of social media platforms. Visible understanding has become more important in the computer vision society with the emergence of AI increase in performance and help train a model to achieve age and gender classification. Although these networks built for the mobile platform are not always as accurate as the larger, more (...)
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  6. Religion and Gender – A Reflection on the Biblical Creation Accounts.Ubong Ekpenyong Eyo - 2012 - American Journal of Social Issues and Humanities 2 (1).
    It is the view of most people who claim the authoritative nature of the Bible that, women’s assigned secondary status in relation to men is ordained and supported in the Bible. Many have quoted different texts of the holy writ to support their culturally-biased position on issue of gender equality. Most often views in respect to gender issues are culturally-based and interpreted rather than divinely-based and interpreted. There is therefore the need to look back at Jesus’ words, “But at the (...)
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  7. Feminist Re-Engineering of Religion-Based AI Chatbots.Hazel T. Biana - 2024 - Philosophies 9 (1):20.
    Religion-based AI chatbots serve religious practitioners by bringing them godly wisdom through technology. These bots reply to spiritual and worldly questions by drawing insights or citing verses from the Quran, the Bible, the Bhagavad Gita, the Torah, or other holy books. They answer religious and theological queries by claiming to offer historical contexts and providing guidance and counseling to their users. A criticism of these bots is that they may give inaccurate answers and proliferate bias by propagating homogenized versions of (...)
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  8.  41
    Legal Definitions of Intimate Images in the Age of Sexual Deepfakes and Generative AI.Suzie Dunn - 2024 - McGill Law Journal 69:1-15.
    In January 2024, non-consensual deepfakes came to public attention with the spread of AI generated sexually abusive images of Taylor Swift. Although this brought new found energy to the debate on what some call non-consensual synthetic intimate images (i.e. images that use technology such as AI or photoshop to make sexual images of a person without their consent), female celebrities like Swift have had deepfakes like these made of them for years. In 2017, a Reddit user named “deepfakes” posted several (...)
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  9. Siri, Stereotypes, and the Mechanics of Sexism.Alexis Elder - 2022 - Feminist Philosophy Quarterly 8 (3).
    Feminized AIs designed for in-home verbal assistance are often subjected to gendered verbal abuse by their users. I survey a variety of features contributing to this phenomenon—from financial incentives for businesses to build products likely to provoke gendered abuse, to the impact of such behavior on household members—and identify a potential worry for attempts to criticize the phenomenon; while critics may be tempted to argue that engaging in gendered abuse of AI increases the chances that one will (...)
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  10. Ethics and Artificial Intelligence.Mark Ryan - 2021 - In Deborah C. Poff & Alex C. Michalos (eds.), Encyclopedia of Business and Professional Ethics. Springer Verlag. pp. 1-5.
    A subdiscipline has emerged around AI ethics, which is comprised of a wide array of individuals: computer scientists, ethicists, cognitive scientists, roboticists, legal professionals, economists, sociologists, gender, and race theorists. This has led to a very interesting branch of research, addressing issues surrounding the development and use of AI. This chapter will give a very brief snapshot of some of the most pertinent ethical concerns. Many of the issues in the Big Data Ethics chapter in this collection are often applicable (...)
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  11. É Possível Evitar Vieses Algorítmicos?Carlos Barth - 2021 - Revista de Filosofia Moderna E Contemporânea 8 (3):39-68.
    Artificial intelligence (AI) techniques are used to model human activities and predict behavior. Such systems have shown race, gender and other kinds of bias, which are typically understood as technical problems. Here we try to show that: 1) to get rid of such biases, we need a system that can understand the structure of human activities and;2) to create such a system, we need to solve foundational problems of AI, such as the common-sense problem. Additionally, when informational platforms uses these (...)
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  12. Algorithmic Political Bias in Artificial Intelligence Systems.Uwe Peters - 2022 - Philosophy and Technology 35 (2):1-23.
    Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...)
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  13. The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In Atoosa Kasirzadeh & Andrew Smart (eds.), ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
    The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can (...)
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  14. Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...)
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  15. Can machines think? The controversy that led to the Turing test.Bernardo Gonçalves - 2023 - AI and Society 38 (6):2499-2509.
    Turing’s much debated test has turned 70 and is still fairly controversial. His 1950 paper is seen as a complex and multilayered text, and key questions about it remain largely unanswered. Why did Turing select learning from experience as the best approach to achieve machine intelligence? Why did he spend several years working with chess playing as a task to illustrate and test for machine intelligence only to trade it out for conversational question-answering in 1950? Why did Turing refer to (...)
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  16. Guru Nanak's Teachings on Building Good Indo-Pak Relations.Devinder Pal Singh - 2022 - Abstracts of Sikh Studies, Chandigarh, India 26 (4):16-20.
    Currently, India-Pakistan relations have often been afflicted by cross-border terrorism, ceasefire violations, territorial disputes, etc. Improving bilateral ties is crucial for both countries, as it would mean stabilizing South Asia and improving the economies of both nations. A strong political will to mend the relationship at the current juncture is direly needed. A constructive approach and confidence-building measures between India and Pakistan can be crucial to improving their ties. About 550 years ago, Guru Nanak preached a special universal message of (...)
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  17. Triển vọng hội nhập quốc tế về khoa học xã hội tại Việt Nam: Phân tích từ dữ liệu Network of Vietnamese Social Sciences 2008-2018.Phạm Hùng Hiệp - 2020 - Tạp Chí Giáo Dục 462 (2):29-35.
    International integration is a mandatory requirement and a dispensible trend for Vietnamese science today. Specifically, when comparing between natural sciences and technology (NS-T) with social sciences (SS), many researchers suggested that SS in Vietnam have a lower level of integration than NS-T. However, according to our understanding, there have not been many statistical studies, estimates and quantitative evaluation of the integration level of Vietnam social science. In this article, using the Network of Vietnamese Social Sciences database developed by AI Social (...)
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  18. Non-Conscious Data Collection: A Critical Analysis of Risks and Public Perspectives.Matomäki Sofia - 2024 - Dissertation, Aalto University School of Business
    This literature review explores the issues and risks in non-conscious data collection and evaluates people’s attitudes towards it. In the modern world, data is one of the most valuable resources, yet studies focused on the potential negative implications of the new data-driven technologies are lacking. Therefore, this thesis conducts a comprehensive literature review to identify and assess risks in non-conscious data collection technologies that are most relevant and referenced in current literature. Accordingly, the most prominent risks are related to privacy (...)
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  19. 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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  20. 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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  21.  98
    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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  22. 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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  23. 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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  24. Building better Sex Robots: Lessons from Feminist Pornography.John Danaher - 2019 - In Yuefang Zhou & Martin H. Fischer (eds.), Ai Love You : Developments in Human-Robot Intimate Relationships. Springer Verlag.
    How should we react to the development of sexbot technology? Taking their cue from anti-porn feminism, several academic critics lament the development of sexbot technology, arguing that it objectifies and subordinates women, is likely to promote misogynistic attitudes toward sex, and may need to be banned or restricted. In this chapter I argue for an alternative response. Taking my cue from the sex positive ‘feminist porn’ movement, I argue that the best response to the development of ‘bad’ sexbots is to (...)
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  25.  63
    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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  26. Das gute Leben. Linke Perspektiven auf einen besseren Alltag.A. G. Gender-KIller (ed.) - 2007 - Münster, Deutschland: UNRAST-Verlag.
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  27. Das gute Leben. Linke Perspektiven auf einen besseren Alltag.A. G. Gender-Killer (ed.) - 2007 - Münster, Deutschland: Unrast Verlag.
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  28. 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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  29. 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 - Khoa Học Xã Hội Việt Nam 2021 (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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  30. 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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  31. 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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  32. 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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  33. AI and the expert; a blueprint for the ethical use of opaque AI.Amber Ross - forthcoming - AI and Society:1-12.
    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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  34. 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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  35. Gender and Gender Terms.Elizabeth Barnes - 2019 - Noûs 54 (3):704-730.
    Philosophical theories of gender are typically understood as theories of what it is to be a woman, a man, a nonbinary person, and so on. In this paper, I argue that this is a mistake. There’s good reason to suppose that our best philosophical theory of gender might not directly match up to or give the extensions of ordinary gender categories like ‘woman’.
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  36. On gender and philosophical intuition: Failure of replication and other negative results.Hamid Seyedsayamdost - 2015 - Philosophical Psychology 28 (5):642-673.
    In their paper titled “Gender and philosophical intuition,” Buckwalter and Stich argue that the intuitions of women and men differ significantly on various types of philosophical questions. Furthermore, men's intuitions, so the authors claim, are more in line with traditionally accepted solutions of classical problems. This inherent bias, so the argument goes, is one of the factors that leads more men than women to pursue degrees and careers in philosophy. These findings have received a considerable amount of attention and the (...)
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  37. 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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  38. 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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  39. The Gender Wars, Academic Freedom and Education.Judith Suissa & Alice Sullivan - 2021 - Journal of Philosophy of Education 55 (1):55-82.
    Philosophical arguments regarding academic freedom can sometimes appear removed from the real conflicts playing out in contemporary universities. This paper focusses on a set of issues at the front line of these conflicts, namely, questions regarding sex, gender and gender identity. We document the ways in which the work of academics has been affected by political activism around these questions and, drawing on our respective disciplinary expertise as a sociologist and a philosopher, elucidate the costs of curtailing discussion on fundamental (...)
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  40. 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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  41. AI Wellbeing.Simon Goldstein & Cameron Domenico Kirk-Giannini - manuscript
    Under what conditions would an artificially intelligent system have wellbeing? Despite its obvious bearing on the ethics of human interactions with artificial systems, this question has received little attention. Because all major theories of wellbeing hold that an individual’s welfare level is partially determined by their mental life, we begin by considering whether artificial systems have mental states. We show that a wide range of theories of mental states, when combined with leading theories of wellbeing, predict that certain existing artificial (...)
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  42. Transparent, explainable, and accountable AI for robotics.Sandra Wachter, Brent Mittelstadt & Luciano Floridi - 2017 - Science (Robotics) 2 (6):eaan6080.
    To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.
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  43. Gender Prediction from Retinal Fundus Using Deep Learning.Ashraf M. Taha, Qasem M. M. Zarandah, Bassem S. Abu-Nasser, Zakaria K. D. AlKayyali & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (5):57-63.
    Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...)
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  44. RESPONSIBLE AI: INTRODUCTION OF “NOMADIC AI PRINCIPLES” FOR CENTRAL ASIA.Ammar Younas - 2020 - Conference Proceeding of International Conference Organized by Jizzakh Polytechnical Institute Uzbekistan.
    We think that Central Asia should come up with its own AI Ethics Principles which we propose to name as “Nomadic AI Principles”.
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  45. AI Art is Theft: Labour, Extraction, and Exploitation, Or, On the Dangers of Stochastic Pollocks.Trystan S. Goetze - 2024 - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency:186-196.
    Since the launch of applications such as DALL-E, Midjourney, and Stable Diffusion, generative artificial intelligence has been controversial as a tool for creating artwork. While some have presented longtermist worries about these technologies as harbingers of fully automated futures to come, more pressing is the impact of generative AI on creative labour in the present. Already, business leaders have begun replacing human artistic labour with AI-generated images. In response, the artistic community has launched a protest movement, which argues that AI (...)
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  46. 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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  47. Medical AI: is trust really the issue?Jakob Thrane Mainz - 2024 - Journal of Medical Ethics 50 (5):349-350.
    I discuss an influential argument put forward by Hatherley in theJournal of Medical Ethics. Drawing on influential philosophical accounts of interpersonal trust, Hatherley claims that medical artificial intelligence is capable of being reliable, but not trustworthy. Furthermore, Hatherley argues that trust generates moral obligations on behalf of the trustee. For instance, when a patient trusts a clinician, it generates certain moral obligations on behalf of the clinician for her to do what she is entrusted to do. I make three objections (...)
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  48. Can AI Help Us to Understand Belief? Sources, Advances, Limits, and Future Directions.Andrea Vestrucci, Sara Lumbreras & Lluis Oviedo - 2021 - International Journal of Interactive Multimedia and Artificial Intelligence 7 (1):24-33.
    The study of belief is expanding and involves a growing set of disciplines and research areas. These research programs attempt to shed light on the process of believing, understood as a central human cognitive function. Computational systems and, in particular, what we commonly understand as Artificial Intelligence (AI), can provide some insights on how beliefs work as either a linear process or as a complex system. However, the computational approach has undergone some scrutiny, in particular about the differences between what (...)
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  49. 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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  50. Shame, Gender Violence, and Ethics: Terrors of Injustice.Lenart Skof & Shé M. Hawke (eds.) - 2021 - New York; London; Boulder; Lanham: Lexington Books.
    Through cutting-edge accounts and interdisciplinary critiques of shame, this collection responds to the epidemic of gendered violence that the world witnesses daily. Contributors expose and challenge how oppression and violence connect to regimes of injustice that have dominated modern times.
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