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 (...) domain and to saliva-related diagnostics following the principles of the OBO (Open Biomedical Ontologies) Foundry. The Saliva Ontology is an ongoing exploratory initiative. The ontology will be used to facilitate salivaomics data retrieval and integration across multiple fields of research together with data analysis and data mining. The ontology will be tested through its ability to serve the annotation ('tagging') of a representative corpus of salivaomics research literature that is to be incorporated into the SKB. Background Saliva (oral fluid) is an emerging biofluid for non-invasive diagnostics used in the detection of human diseases. The need to advance saliva research is strongly emphasized by the National Institute of Dental and Craniofacial Research (NIDCR), and is included in the NIDCR's 2004- 2009 expert panel long-term research agenda [1]. The ability to monitor health status, disease onset, progression, recurrence and treatment outcome through noninvasive means is highly important to advancing health care management. Saliva is a perfect medium to be explored for personalized individual medicine including diagnostics, offering a non-invasive, easy to obtain means for detecting and monitoring diseases. Saliva testing potentially allows the patient to collect their own saliva samples at home, yielding convenience for the patient and savings in health costs, and facilitating multiple sampling. Specimen collection is less objectionable to patients and easier in children and elderly individuals. Due to these advantages. (shrink)

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 (...) diagnostics made possible by the Saliva Ontology and SDxMart. (shrink)

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 (...) body fluids ontology initiative may provide support to basic and translational science. (shrink)

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).

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 (...) trường Việt Nam bị méo mó và giảm lợi thế cạnh tranh quốc gia. Do vậy, công cuộc đổi mới và hoàn thiện chế độ sở hữu ở Việt Nam phải hướng tới việc khắc phục những hạn chế trên và đạt tới sức cạnh tranh ngang hàng với các nền kinh tế thị trường hiện đại khác. (shrink)

(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 (...) dựng nền kinh tế thị trường thì đó vẫn là một nền kinh tế thị trường khó được quốc tế công nhận. Sở hữu tư nhân hiện đại là chế độ sở hữu được cổ phần hóa, được xã hội hóa rất cao, công khai, minh bạch trên các thị trường chứng khoán. Tính xã hội cao của chế độ cổ phần hóa đã được C.Mác đánh giá, xem như là chế độ sở hữu quá độ sang hình thái kinh tế - xã hội mới. (shrink)

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 (...) of its anticipated uses. (shrink)

本文深入探討人工智慧（AI）於實現共同福祉與幸福、公平與非歧視、理性公共討論及自主與控制之倫理與正義重要性與挑戰。以中央研究院LLM事件及國家科學技術委員會（NSTC）AI技術研發指導方針為基礎，本文 分析AI能否滿足人類共同利益與福祉。針對AI不公正，本文評估其於區域、產業及社會影響。並探討AI公平與非歧視挑戰，尤其偏差數據訓練問題，及後處理監管，強調理性公共討論之重要性。進而，本文探討理性公眾於 公共討論中之挑戰及應對，如STEM科學素養與技術能力教育之重要性。最後，本文提出“以人為本”方法，非僅依賴AI技術效用最大化，以實現AI正義。 -/- 關鍵詞：AI倫理與正義、公平與非歧視、偏差數據訓練、公共討論、自主性、以人為本的方法.

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on liability, privacy, intellectual property, and cybersecurity. It examines (...) the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

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 (...) to Hatherley’s claims: (1) At least one philosophical account of interagent trust implies that medical AI is capable of being trustworthy. (2) Even if this account should ultimately be rejected, it does not matter much because what we care mostly about is that medical AI is reliable. (3) It is false that trust in itself generates moral obligations on behalf of the trustee. (shrink)

The development and deployment of machine learning and artificial intelligence (AI) engender “AI colonialism”, a term that conceptually overlaps with “data colonialism”, as a form of injustice. AI colonialism is in need of decolonization for three reasons. Politically, because it enforces digital capitalism’s hegemony. Ecologically, as it negatively impacts the environment and intensifies the extraction of natural resources and consumption of energy. Epistemically, since the social systems within which AI is embedded reinforce Western universalism by imposing Western colonial values on (...) the global South when these manifest in the digital realm is a form of digital capitalism. These reasons require a new conceptualization of AI decolonization. First this paper draws from the historical debates on the concepts of colonialism and decolonization. Secondly it retrieves Achille Mbembe’s notion of decolonization as disenclosure to argue that the decolonization of AI will have to be the abolishment of political, ecological and epistemic borders erected and reinforced in the phases of its design, production, development of AI in the West and drawing from the knowledge from the global South. In conclusion, it is discussed how conceiving of decolonial AI as form of disenclosure opens up new ways to think about and intervene in colonial instantiations of AI development and deployment, in order to empower “the wretched of AI”, re-ecologise the unsustainable ecologies AI depends on and to counter the colonial power structures unreflective AI deployment risks to reinforce. (shrink)

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 (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

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 (...) boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (shrink)

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 (...) existence of God. And while this analogy is interesting in its own right, what is more interesting are its potential implications. It has been repeatedly argued that sceptical theism has devastating effects on our beliefs and practices. Could it be that AI-doomsaying has similar effects? I argue that it could. Specifically, and somewhat paradoxically, I argue that it could amount to either a reductio of the doomsayers position, or an important and additional reason to join their cause. I use this paradox to suggest that the modal standards for argument in the superintelligence debate need to be addressed. (shrink)

AI companies are racing to create artificial general intelligence, or “AGI.” If they succeed, the result will be human-level AI systems that can independently pursue high-level goals by formulating and executing long-term plans in the real world. Leading AI researchers agree that some of these systems will likely be “misaligned”–pursuing goals that humans do not desire. This goal mismatch will put misaligned AIs and humans into strategic competition with one another. As with present-day strategic competition between nations with incompatible goals, (...) the result could be violent and catastrophic conflict. Existing legal institutions are unprepared for the AGI world. New foundations for AGI governance are needed, and the time to begin laying them is now, before the critical moment arrives. -/- This Article begins to lay those new legal foundations. It is the first to think systematically about the dynamics of strategic competition between humans and misaligned AGI. The Article begins by showing, using formal game-theoretic models, that, by default, humans and AIs will be trapped in a prisoner’s dilemma. Both parties’ dominant strategy will be to permanently disempower or destroy the other, even though the costs of such conflict would be high. -/- The Article then argues that a surprising legal intervention could transform the game theoretic equilibrium and avoid conflict: AI rights. Not just any AI rights would promote human safety. Granting AIs the right not to be needlessly harmed–as humans have granted to certain non-human animals–would, for example, have little effect. Instead, to promote human safety, AIs should be given those basic private law rights–to make contracts, hold property, and bring tort claims–that law already extends to non-human corporations. Granting AIs these economic rights would enable long-run, small-scale, mutually-beneficial transactions between humans and AIs. This would, we show, facilitate a peaceful strategic equilibrium between humans and AIs for the same reasons economic interdependence tends to promote peace in international relations. Namely, the gains from trade far exceed those from war. Throughout, we argue that human safety, rather than AI welfare, provides the right framework for developing AI rights. This Article explores both the promise and the limits of AI rights as a legal tool for promoting human safety in an AGI world. (shrink)

To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.

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 (...) are inadequate for AI-intensive companies. To fully account for contemporary technology, the following categories of evaluation will be developed and featured as vital investing criteria: autonomy, dignity, privacy, performance. With these priorities established, the larger goal is a model for humanitarian investing in AI-intensive companies that is intellectually robust, manageable for analysts, useful for portfolio managers, and credible for investors. (shrink)

A morally acceptable course of AI development should avoid two dangers: creating unaligned AI systems that pose a threat to humanity and mistreating AI systems that merit moral consideration in their own right. This paper argues these two dangers interact and that if we create AI systems that merit moral consideration, simultaneously avoiding both of these dangers would be extremely challenging. While our argument is straightforward and supported by a wide range of pretheoretical moral judgments, it has far-reaching moral implications (...) for AI development. Although the most obvious way to avoid the tension between alignment and ethical treatment would be to avoid creating AI systems that merit moral consideration, this option may be unrealistic and is perhaps fleeting. So, we conclude by offering some suggestions for other ways of mitigating mistreatment risks associated with alignment. (shrink)

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 (...) systems have wellbeing. While we do not claim to demonstrate conclusively that AI systems have wellbeing, we argue that our metaphysical and moral uncertainty about AI wellbeing requires us dramatically to reassess our relationship with the intelligent systems we create. (shrink)

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, (...) to make assessments. Employing information-processing-based Bayesian Mindsponge Framework (BMF) analytics on a dataset of 266 residents in the United States, we found that the more people believe that an AI agent seeks continued functioning, the more they believe in that AI agent’s capability of having a mind of its own. Moreover, we also found that the above association becomes stronger if a person is more familiar with personally interacting with AI. This suggests a directional pattern of value reinforcement in perceptions of AI. As the information processing of AI becomes even more sophisticated in the future, it will be much harder to set clear boundaries about what it means to have an autonomous mind. (shrink)

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 (...) image generation is a kind of theft. This paper analyzes, substantiates, and critiques these arguments, concluding that AI image generators involve an unethical kind of labour theft. If correct, many other AI applications also rely upon theft. (shrink)

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, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

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 (...) more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...) autonomy, justice, and explicability can feed into national- and organisational-level microsocial contracts. We also outline the role of employees’ technology frames in this process. We then use an illustrative example to demonstrate how this multilevel normative background helps to inform the content of individuals’ PCs in the context of working with AI technologies. (shrink)

This paper delves into the significance and challenges of Artificial Intelligence (AI) ethics and justice in terms of Common Good and Well-being, fairness and non-discrimination, rational public deliberation, and autonomy and control. Initially, the paper establishes the groundwork for subsequent discussions using the Academia Sinica LLM incident and the AI Technology R&D Guidelines of the National Science and Technology Council(NSTC) as a starting point. In terms of justice and ethics in AI, this research investigates whether AI can fulfill human common (...) interests and welfare. Taking AI injustice as an example, I analyze the practical assessment of AI regarding regional, industrial, and social impacts. Further, this paper discusses the challenges of fairness and non-discrimination in AI, specifically addressing the issue of training on biased data, discussing the acquisition of bias by AI and post-processing supervision issues, and emphasizing the importance of rational public deliberation in this process. Then, this research examines the challenges and countermeasures the rational public faces in public deliberation, such as the importance of education in STEM scientific literacy and technological capabilities. Finally, in discussing AI and autonomy, I propose a 'Human-Centered Approach’ rather than relying solely on the 'Technological Utility Maximization' brought by AI to achieve substantial AI justice. -/- Keywords: AI Ethics and Justice, Fairness and Non-Discrimination, Biased Data Training, Public Deliberation, Autonomy, Human-Centered Approach . (shrink)

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 (...) the many solutions that human ingenuity has managed to devise. (shrink)

The ability to interact in a natural language profoundly changes devices’ interfaces and potential applications of speaking technologies. Concurrently, this phenomenon challenges our mainstream theories of knowledge, such as how to analyze linguistic outputs of devices under existing anthropocentric theoretical assumptions. In section 1, I present the topic of machines that speak, connecting between Descartes and Generative AI. In section 2, I argue that accepted testimonial theories of knowledge and justification commonly reject the possibility that a speaking technological artifact can (...) give testimony. In section 3, I identify three assumptions underlying the view that rejects conversational AIs – AI-based technologies that converse, as testifiers: conversational AIs (1) lack intentions, (2) cannot be normatively assessed, and (3) cannot constitute an object in trust relations, while humans can. In section 4, I propose the concept ‘AI-testimony’ for analyzing outputs of conversational AIs, suggesting three conditions for technologies to deliver AI-testimony: (1) content is propositional, (2) generated and delivered with no other human directly involved, (3) the output is perceived as phenomenologically similar to that of a human. I conclude that this concept overcomes the limitations of the anthropocentric concept of testimony, opening future directions of research without associating conversational AIs with human-like agency. (shrink)

For synergistic interactions between humans and artificial intelligence (AI) systems, AI outputs often need to be explainable to people. Explainable AI (XAI) systems are commonly tested in human user studies. However, whether XAI researchers consider potential cultural differences in human explanatory needs remains unexplored. We highlight psychological research that found significant differences in human explanations between many people from Western, commonly individualist countries and people from non-Western, often collectivist countries. We argue that XAI research currently overlooks these variations and that (...) many popular XAI designs implicitly and problematically assume that Western explanatory needs are shared cross-culturally. Additionally, we systematically reviewed over 200 XAI user studies and found that most studies did not consider relevant cultural variations, sampled only Western populations, but drew conclusions about human-XAI interactions more generally. We also analyzed over 30 literature reviews of XAI studies. Most reviews did not mention cultural differences in explanatory needs or flag overly broad cross-cultural extrapolations of XAI user study results. Combined, our analyses provide evidence of a cultural bias toward Western populations in XAI research, highlighting an important knowledge gap regarding how culturally diverse users may respond to widely used XAI systems that future work can and should address. (shrink)

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 (...) the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

Large Language Models (LLMs) are generative AI systems capable of producing original texts based on inputs about topic and style provided in the form of prompts or questions. The introduction of the outputs of these systems into human discursive practices poses unprecedented moral and political questions. The article articulates an analysis of the moral status of these systems and their interactions with human interlocutors based on the Habermasian theory of communicative action. The analysis explores, among other things, Habermas's inquiries into (...) the analogy between human minds and computers, and into the status of atypical participants in the linguistic community such as genetically modified subjects and animals. Major conclusions are the LLMs seem to qualify as authors that originally participate in discursive practices but do display only a structurally derivative form of communicative competence and fail to meet the status of communicative agents. In this sense, while the contribution of AI writing systems in public discourse and deliberation can support the process of mutual understanding within the community of speakers, the human actors involved in the development, use, and diffusion of these systems share a collective responsibility for the disclosure of AI authorship and verification and adjudication of validity claims. (shrink)

Better instruments to predict the future evolution of artificial intelligence (AI) are needed, as the destiny of our civilization depends on it. One of the ways to such prediction is the analysis of the convergent drives of any future AI, started by Omohundro. We show that one of the convergent drives of AI is a militarization drive, arising from AI’s need to wage a war against its potential rivals by either physical or software means, or to increase its bargaining power. (...) This militarization trend increases global catastrophic risk or even existential risk during AI takeoff, which includes the use of nuclear weapons against rival AIs, blackmail by the threat of creating a global catastrophe, and the consequences of a war between two AIs. As a result, even benevolent AI may evolve into potentially dangerous military AI. The type and intensity of militarization drive depend on the relative speed of the AI takeoff and the number of potential rivals. We show that AI militarization drive and evolution of national defense will merge, as a superintelligence created in the defense environment will have quicker takeoff speeds, but a distorted value system. We conclude with peaceful alternatives. (shrink)

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- (...) ness. In terms of decision makers, the use of human decision makers over AIs generally resulted in better perceptions of respectful treatment. In terms of decision valence, people experiencing positive over negative decisions generally resulted in better perceptions of respectful treatment. In instances where these cases conflict, on some indicators people preferred positive AI decisions over negative human decisions. Qualitative responses show how people identify justice concerns with both AI and human decision making. We outline implications for theory, practice, and future research. (shrink)

This is a commentary on Andrea Lavazza and Murilo Vilaça's article "Human Extinction and AI: What We Can Learn from the Ultimate Threat" (Lavazza & Vilaça, 2024). I discuss the potential concern that their proposal to create artificial successors to "insure" against the tragedy of human extinction might mean being too quick to accept that catastrophic prospect as inevitable, rather than single-mindedly focusing on avoiding it. I also consider the question of the value that we might reasonably assign to such (...) a future of artificial successors under the assumption that they do not enjoy phenomenal consciousness. (shrink)

Personal AI assistants are now nearly ubiquitous. Every leading smartphone operating system comes with a personal AI assistant that promises to help you with basic cognitive tasks: searching, planning, messaging, scheduling and so on. Usage of such devices is effectively a form of algorithmic outsourcing: getting a smart algorithm to do something on your behalf. Many have expressed concerns about this algorithmic outsourcing. They claim that it is dehumanising, leads to cognitive degeneration, and robs us of our freedom and autonomy. (...) Some people have a more subtle view, arguing that it is problematic in those cases where its use may degrade important interpersonal virtues. In this article, I assess these objections to the use of AI assistants. I will argue that the ethics of their use is complex. There are no quick fixes or knockdown objections to the practice, but there are some legitimate concerns. By carefully analysing and evaluating the objections that have been lodged to date, we can begin to articulate an ethics of personal AI use that navigates those concerns. In the process, we can locate some paradoxes in our thinking about outsourcing and technological dependence, and we can think more clearly about what it means to live a good life in the age of smart machines. (shrink)

We apply Ken Wilber's Integral Theory to AI governance, demonstrating its ability to systematize diverse approaches in the current multifaceted AI governance landscape. By analyzing ethical considerations, technological standards, cultural narratives, and regulatory frameworks through Integral Theory's four quadrants, we offer a comprehensive perspective on governance needs. This approach aligns AI governance with human values, psychological well-being, cultural norms, and robust regulatory standards. Integral Theory’s emphasis on interconnected individual and collective experiences addresses the deeper aspects of AI-related issues. Additionally, we (...) propose using Integral Theory as a methodology for literature reviews to overcome the fragmented understanding often seen in traditional reviews of AI governance. (shrink)

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 (...) systematic treatment in the literature: when such algorithms are used in life-changing decisions, they can obstruct us from effectively shaping our lives according to our goals and preferences, thus undermining our autonomy. I argue that this concern deserves closer attention as it furnishes the call for transparency in algorithmic decision-making with both new tools and new challenges. (shrink)

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 (...) chapter we suggest that using AI extenders, i.e., tightly coupled cognitive extenders that are imbued with machine learning and other ‘artificially intelligent’ tools, presents both new ethical challenges and opportunities for mental health. We focus on several mental health conditions that can develop differently by the use of AI extenders for people with cognitive disorders and then discuss some of the related opportunities and challenges. (shrink)

Recent AI progress led to a boost in beneficial applications from multiple research areas including VR. Simultaneously, in this newly unfolding deepfake era, ethically and security-relevant disagreements arose in the scientific community regarding the epistemic capabilities of present-day AI. However, given what is at stake, one can postulate that for a responsible approach, prior to engaging in a rigorous epistemic assessment of AI, humans may profit from a self-questioning strategy, an examination and calibration of the experience of their own epistemic (...) agency – especially to counteract both intentional misdirection by unethical actors and unintentional epistemic self-sabotage. In this paper, we expound on a new avenue of utilizing AIVR tools to advance an AI-related misdirection awareness of humans in the deepfake era. Firstly, we harness scientific knowledge from the psychology and neuroscience of magic where the study of misdirection techniques is center stage. Secondly, we connect the latter to creativity research linking human creative potential to inspiration from the seemingly impossible. Overall, AIVR could become an empowering experiential testbed for human epistemic agency enabling a better rational evaluation of AI capabilities. However, a misuse of the same type of tools could yield AIVR safety risks if not counteracted preemptively. (shrink)

This article argues that an ethical framework will help to harness the potential of AI while keeping humans in control.

This paper presents an argument that certain AI safety measures, rather than mitigating existential risk, may instead exacerbate it. Under certain key assumptions - the inevitability of AI failure, the expected correlation between an AI system's power at the point of failure and the severity of the resulting harm, and the tendency of safety measures to enable AI systems to become more powerful before failing - safety efforts have negative expected utility. The paper examines three response strategies: Optimism, Mitigation, and (...) Holism. Each faces challenges stemming from intrinsic features of the AI safety landscape that we term Bottlenecking, the Perfection Barrier, and Equilibrium Fluctuation. The surprising robustness of the argument forces a re-examination of core assumptions around AI safety and points to several avenues for further research. (shrink)

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 (...) Whiteness might simply reflect the predominantly White milieus from which these artefacts arise. Second, we argue that to imagine machines that are intelligent, professional, or powerful is to imagine White machines because the White racial frame ascribes these attributes predominantly to White people. Third, we argue that AI racialised as White allows for a full erasure of people of colour from the White utopian imaginary. Finally, we examine potential consequences of the racialisation of AI, arguing it could exacerbate bias and misdirect concern. (shrink)

Arthur C. Danto did not live to witness the proliferation of AI in artistic creation. However, his philosophy of art offers key ideas about art that can provide an interesting perspective on artwork generated by artificial intelligence (AI). In this article, I analyze how his ideas about contemporary art, intention, interpretation, and authorship could be applied to the ongoing debate about AI and artistic creation. At the same time, it is also interesting to consider whether the incorporation of AI into (...) artistic creation marks a new chapter in the narrative, which seems to have reached its end, or if it represents a significant rupture with past artistic traditions. (shrink)

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 (...) the first section, I explain Descartes’ view about language and mind. To show that Turing bites the bullet with his imitation game, in the second section I analyze this method to assess intelligence. Then, in the third section, I elaborate on Schank and Abelsons’ Script Applier Mechanism (SAM, hereby), which supposedly casts doubt on Descartes’ denial that machines can think. Finally, in the fourth section, I explore a challenge that any algorithmic decomposition of linguistic understanding faces. This challenge, I argue, is the core of the Cartesian problem: knowledge and awareness of meaning require a first-person viewpoint which is irreducible to the decomposition of algorithmic mechanisms. (shrink)

Can AI and humans genuinely communicate? In this article, after giving some background and motivating my proposal (§1–3), I explore a way to answer this question that I call the ‘mental-behavioral methodology’ (§4–5). This methodology follows the following three steps: First, spell out what mental capacities are sufficient for human communication (as opposed to communication more generally). Second, spell out the experimental paradigms required to test whether a behavior exhibits these capacities. Third, apply or adapt these paradigms to test whether (...) an AI displays the relevant behaviors. If the first two steps are successfully completed, and if the AI passes the tests with human-like results, this constitutes evidence that this AI and humans can genuinely communicate. This mental-behavioral methodology has the advantage that we don’t need to understand the workings of black-box algorithms, such as standard deep neural networks. This is comparable to the fact that we don’t need to understand how human brains work to know that humans can genuinely communicate. This methodology also has its disadvantages and I will discuss some of them (§6). (shrink)

In recent years, with the advances in machine learning and neuroscience, the abundances of sensors and emotion data, computer engineers have started to endow machines with ability to detect, classify, and interact with human emotions. Emotional artificial intelligence (AI), also known as a more technical term in affective computing, is increasingly more prevalent in our daily life as it is embedded in many applications in our mobile devices as well as in physical spaces. Critically, emotional AI systems have not only (...) the ability to not passively read and classify emotions, but most also have an ability to predict and ‘nudge’ users to certain desired emotional states. Drawing from Piredda (2020)’s account, we've examined various emerging emotion-sensing technologies such as recommender algorithms, personal AI assistants, work-related emotion trackers, and emotional AI toys and robots through the lens of affective artifacts. We show how these technologies fulfill the criteria of being affective artifacts: they can influence the affective states of the users (e.g., help regulate emotions of ourselves and others), some emotion-sensing algorithms and importantly, our manipulation of these algorithms, can intersect with the maintenance and construction of our sense of self and identity. (shrink)

Humans and AI systems are usually portrayed as separate sys- tems that we need to align in values and goals. However, there is a great deal of AI technology found in non-autonomous systems that are used as cognitive tools by humans. Under the extended mind thesis, the functional contributions of these tools become as essential to our cognition as our brains. But AI can take cognitive extension towards totally new capabil- ities, posing new philosophical, ethical and technical chal- lenges. To (...) analyse these challenges better, we define and place AI extenders in a continuum between fully-externalized systems, loosely coupled with humans, and fully-internalized processes, with operations ultimately performed by the brain, making the tool redundant. We dissect the landscape of cog- nitive capabilities that can foreseeably be extended by AI and examine their ethical implications. We suggest that cognitive extenders using AI be treated as distinct from other cognitive enhancers by all relevant stakeholders, including developers, policy makers, and human users. (shrink)

The hypergrowth and hyperconnectivity of networks of artificial intelligence (AI) systems and algorithms increasingly cause our interactions with the world, socially and environmentally, more technologically mediated. AI systems start interfering with our choices or making decisions on our behalf: what we see, what we buy, which contents or foods we consume, where we travel to, who we hire, etc. It is imperative to understand the dynamics of human-computer interaction in the age of progressively more competent AI. This essay presents five (...) premises to serve as starting points for building our understanding of the “interactions’ in human-computer interactions. (shrink)

Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on (...) the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly. (shrink)

The recent book by Landgrebe and Smith (2022) offers compelling arguments against the possibility of Artificial General Intelligence (AGI) as well as against the idea that machines have the abilities to master human language, human social interaction and morality. Their arguments leave open, however, a problem on the side of the imaginative power of humans to perceive more than there is and treat AIs as humans and social actors independent of their actual properties and abilities or lack thereof. The mathematical (...) and ontological arguments will not help against this strong human tendency to treat conversational AI (CAI) as if it was human. (shrink)