Saleh Afroogh

AI researchers and ethicists have long worried about the threat that automation poses to human dignity, autonomy, and to the sense of personal value that is tied to work. Typically, proposed solutions to this problem focus on ways in which we can reduce the number of job losses which result from automation, ways to retrain those that lose their jobs, or ways to mitigate the social consequences of those job losses. However, even in cases where workers keep their jobs, their agency within those roles might be severely downgraded. For instance, human employees might work alongside artificial intelligence (AI) but not be allowed to make decisions or not be allowed to make decisions without consulting with or coming to agreement with the AI. Here, we argue that this is a kind of epistemic harm (which could be an injustice if it is distributed on the basis of identity prejudice). It diminishes human agency (in constraining people’s ability to act independently), and it fails to recognize the workers’ epistemic agency as qualified experts. Workers, in this case, aren’t given the trust they are entitled to. This means that issues of human dignity remain even in cases where everyone keeps their job. Further, job retention focused solutions, such as designing an algorithm to work alongside the human employee, may only enable these harms. In response to this, we propose an alternative design solution, adversarial collaboration, which addresses the traditional retention problem of automation, but also addresses the larger underlying problem of epistemic harms and the distribution of trust between AI and humans in the workplace.

In this paper, we challenge the overreliance on conventional ethical frameworks commonly observed in current AI ethics literature. We begin by surveying the ethical concerns and frameworks that dominate this field. Following this, we categorize and critically review the existing objections to these traditional approaches in terms of conceptual challenges, professional and regulatory challenges, and challenges from practical implementation. Finally, we present three key arguments against conventional ethical frameworks, drawing upon the fact that they fail to preclude ethics washing, stifle innovation in ethical research, and the drastic changes brought about by AI technology in the ethical landscape.

This study maps the functions of artificial intelligence in disaster (mis)management. It begins with a classification of disasters in terms of their causal parameters, introducing hypothetical cases of independent or hybrid AI-caused disasters. We then overview the role of AI in disaster management and mismanagement, where the latter includes possible ethical repercussions of the use of AI in intelligent disaster management (IDM), as well as ways to prevent or mitigate these issues, which include pre-design a priori, in-design, and post-design methods as well as regulations. We then discuss the government’s role in preventing the ethical repercussions of AI use in IDM and identify and asses its deficits and challenges. This discussion is followed by an account of the advantages and disadvantages of pre-design or embedded ethics. Finally, we briefly consider the question of accountability and liability in AI-caused disasters.

IGGA (Industrial Guidelines/policy statements for Generative AIs) is a comprehensive dataset comprising 160 guidelines and policy statements pertaining to the use of generative AIs and large language models across 14 industry sectors. These guidelines were systematically selected and gathered from official company websites and reliable sources spanning six continents. The dataset, containing 295,692 words, is designed to support various natural language processing tasks, including language modeling, sentiment analysis, semantic analysis, model synthesis, classification, and topic labeling. Additionally, it serves as a benchmark for tasks such as ambiguity detection and requirements categorization. This resource aims to advance research in AI governance within industrial settings, fostering a deeper understanding of how AI technologies are integrated into industry practices. (2024-11-17)

AGGA (Academic Guidelines for Generative AIs) is a dataset of 80 academic guidelines for the usage of generative AIs and large language models in academia, selected systematically and collected from official university websites across six continents. Comprising 181,225 words, the dataset supports natural language processing tasks such as language modeling, sentiment and semantic analysis, model synthesis, classification, and topic labeling. It can also serve as a benchmark for ambiguity detection and requirements categorization. This resource aims to facilitate research on AI governance in educational contexts, promoting a deeper understanding of the integration of AI technologies in academia.

In this paper, I argue for a probabilistic theory of trust, and the plausibility of “trustworthy AI” in which we trust (as opposed to mere reliance). I show that the current trust theories cannot accommodate trust pertaining to AI, and I propose an alternative probabilistic theory, which accounts for the four major types of AI-related trust: an AI agent’s trust in another AI agent, a human agent’s trust in an AI agent, an AI agent’s trust in a human agent, and an AI agent’s trust in an object (including mental and complex objects). I draw a broadly neglected distinction between transitive and intransitive senses of trust, each of which calls for a distinctive semantical theory. Based on this distinction, I classify the current theories into the theories of trust and theories of trustworthiness, showing that the current theories fail to model some of the major types of AI-related trust; while the proposed conditional probabilistic theory of trust and theory of trustworthiness, unlike the current trust theories, is scalable, and they would also accommodate major types of trust in non-AI, including interpersonal trust, reciprocal trust, one-sided trust, as well as trust in objects—e.g., thoughts, theories, data, algorithms, systems, and institutions.

Empathic design, which is the outcome of embedding the empathic approach in community resilience, will meet all four critical features of any models which are supposed to satisfy both physical resilience and humanistic considerations. It holds that in addition to the technical knowledge, engineers have to care about the humanistic side of the engineering process as well. Empathic design refers to a design in which the designers, as well as the technical specifications, consider the humanistic aspects of the system in virtue of three types of empathy, namely, cognitive, affective, and conative. The empathic design brings about an inclusive and effective community resilience approach that is human-centric, individual and communal sensitive, justice-oriented, and value-based consistent.

In this paper, we argue that an inclusive and effective community resilience approach requires empathy as a missing component in the current engineering education and practice. An inclusive and effective community resilience approach needs to be human-centric, individual- and communal-sensitive, justice-oriented, and values-based consistent. In this paper, we argue that three kinds of empathy, namely cognitive, affective, and conative, play a central role in creating and sustaining an inclusive and effective approach to community resilience. Finally, we discuss empathetic education through learning theories and analytics skills to cultivate empathy in engineering education. Cultivating empathy in engineering education could help advance the impact and contribution of engineering to well-being.

In this paper, we propose a conceptual model to improve moral sensitivity in human resource development (HRD) to assist human resource (HR) practitioners in contending with moral challenges in HRD. The literature on the relationship between ethics and HRD suggests that the organizational and employee development discipline deals with ethical issues at three different levels: Individual, organizational and communal, and international levels. In section I, we elaborate on moral challenges facing HRD. In section II, we conceptualize moral sensitive HRD, proposing a conceptual model in virtue of some essential ethical theories and concepts that assist HRD in grappling with those problems. We will show how each theory and concept can help HRD to deal with relevant problems. In section III, we elaborate on practical approaches to implement moral sensitivity in HRD. We put forward some strategies that help HRD bring those theories and concepts to bear on the ethical problems facing this discipline. Finally, we discuss moral education through learning theories to cultivate moral concepts and ethical dimensions in HRD education.

Summary Background The principle of maximization, which roughly means that we should save more lives and more years of life, is usually taken for granted by the health community. This principle is even more forceful in crises like the COVID-19 pandemic, where we have scarce resources which can be allocated only to some patients. However, the standard consequentialist version of this principle can be challenging particularly when we have to reallocate a resource that has already been given to a patient. Methodology Engaging in thought experiments, conceptual analysis, providing counterexamples, and appealing to moral intuitions, we challenge the standard consequentialist version of the maximization principle and make a case for adopting an alternative deontological version. Discussion In certain cases, the standard consequentialist version of the maximization principle is shown to yield intuitively immoral results. The deontological version of this principle is preferable because it can retain the merits of the standard consequentialist version without falling prey to its problems. Conclusion Compared to the standard consequentialist version, the deontological version of the maximization principle can better guide the ethical decisions of the health community, even in cases where we face a scarcity of resources.

In this paper, I argue that Kantian ethics is not inclusive, and his formulation of CI fails. It excludes some intuitive moral actions. I show that Kant’s formulation of categorical imperative fails in some important category of moral actions, due to the fact that its first formula (i.e., the formula of universal law ) is contingent, and doesn't necessarily obtain in all categories of moral actions. Wood in 1999 shows that the formula of universal law is incomplete, however, I argue that it is not a necessary condition for moral action; and this entails the failure of CI. There are some exceptional moral maxims (and relevant actions), which are both permissible and moral, but they cannot be thought as universal one, without practical contradiction.

In this paper, I propose a pragmatic theory of meaning. In section 1, I clarify the distinction between meanNN and meanN proposed by Grice (1957), and I suggest a new distinction between linguistic and non-linguistic meanNN. In section 2, I shall explain Grice theory of meaning and criticize it. In section 3, I explain the classical theory of meaning and shall propose some consideration about it. Then, in section 4, I shall elaborate on the pragmatic theory of meaning, and I explore its difference with Grice theory of meaning as well as the classical theory of meaning. I also clarify the role of practical consequences in the pragmatic theory of meaning and will explain how it relates to Quine’s idea of the indeterminacy of translation. Section 5 concludes.

In Delusions of consciousness, Blackmore supports illusionism on consciousness, using a Humean approach toward "self." First, she tries to explain away the intuitive, realistic viewpoint on self-consciousness; she "explains why some the illusionary self-consciousness is so compelling" by claiming a "simple mistake in introspections" and tries to explain it away. Secondly, she concludes that the idea of illusionary self-consciousness shows the delusion of consciousness per se. In this paper, first, I shall show that her explanation against realism on consciousness (in support of the illusion of self-consciousness) is not decisive; it can be formulated such that works against illusionism. Secondly, I show that un-consciousness is a delusion and explain it away, not in a Humean approach, but in terms of the semantics of "forgetfulness." Thirdly, I shall show that Blackmore wrongly concludes delusion of consciousness, on the basis of illusionary self-consciousness, while the latter doesn't necessarily entail the former.

Ethical considerations play a key role in the proposed frameworks for COVID-19 vaccine allocation. Based on a set of ethical principles, these frameworks make recommendations on those who should be prioritized for vaccination. WHO Strategic Advisory Group of Experts of Immunization (SAGE)1, the National Academy of Medicine (NAM)2, and the Johns Hopkins Center for Health Security (JHCHS)3 have issued frameworks for allocation and prioritization of COVID-19 vaccination. None of these frameworks have made any recommendations as to the prioritization of those who have not yet been infected with SARS-CoV-2 (non-infected group) over those who have been infected previously (infected group). Does this imply that giving priority to the non-infected group is not justified from the ethical perspective of these frameworks? In the following, we review the ethical foundations of these frameworks and show that prioritization of the non-infected group is not only consistent with, but also implied by, these ethical foundations.

Consciousness is one of the most complex phenomena of the world. As philosophy of mind, together with psychology and cognitive sciences, developed, consciousness was seriously studied in the twentieth century. Many attempts have been made to explain the nature of this mysterious phenomenon and the way in which it has come into being; as a result, different theories have been offered about it. In this paper, a variety of naturalistic theories of consciousness have been considered, and their weakness has been shown.

What does it mean to say that an agent has a reason to do a certain action? Does it mean that she would desire to do the action, or that there is some external consideration, which she ought to follow? Or is there a third alternative? The debate between Humean affective (i.e., desire-based) and classical Kantian cognitive theories has seemingly ended up in a theoretical standoff, and so most of the contributors have recently focused on the conative attitude of motivation - either preceded by affective or cognitive attitudes. Accordingly, they contend that an agent has a reason to f only if, on some occasions, she would be motivated to f: call this Conative Reason Internalism. I argue, first, that even the most qualified version of this weak conative condition obtains only contingently. Secondly, that a cognitive contextual attitude, derived from the agent’s capacity of Reasons-Understanding, necessarily obtains. Therefore, necessarily, if an agent has a reason to f, it follows that, were she contextually rational, she would make evaluative sense out of the propositional content of f-ing or would understand why f-ing is considered as a right action in the relevant context: I call this De Dicto Cognitive Reason Contextualism.

Consciousness is one of the most complex phenomena of the world. As philosophy of mind, together with psychology and cognitive sciences, developed, consciousness was seriously studied in the twentieth century. Many attempts have been made to explain the nature of this mysterious phenomenon and the way in which it has come into being; as a result, different theories have been offered about it. In this paper, a variety of naturalistic theories of consciousness have been considered, and their weakness has been shown.

In Internal and External Reasons, Bernard Williams proposes a speculative argument for the idea that internal reasons are the only kind of normative reason, and that his counterfactual internal interpretation is the only truth condition for both kinds of reason-statements, H and S (H: “A has a reason to φ” and S: “There is a reason for A to φ”). He takes for granted, however, that internal and external reasons are the only possible kinds of normative reasons at work: his argument is therefore question-begging against a externalism—yet this is only the lesser problem faced by reason internalism. The main problems for Williams’s argument are his semantical reduction of S to H (which entails his question-begging argument) and his ad hoc interpretation of H in terms of his stipulative account of internal reasons. The most serious problem for both reason internalism and reason externalism, however, is represented by certain moral counterintuitive examples on both sides, which could be considered as the central problem in the debate. In this thesis, after showing Williams’s argument to be question-begging, I first explain his failure to unify two irreducible questions (on the nature of normative reasons and on the nature of rationality) as well as to reduce the truth conditions of S to H. Then, through conceptual analysis, I show that William’s internalist interpretation of H is false, and that the commonsensical interpretation of H is one that I call contextual interpretation in terms of contextual reasons. Internal reasons are just one usage of H, and contextual reasons are the real rival to external reasons in the debate on normative reason. Secondly, I argue that the two distinct notions of rationality— relative (instrumental) rationality and absolute rationality—require two distinct truth conditions, and the central problem deals only with the absolute one. I hope to show that reason contextualism, defined in terms of contextual reasons (i.e., neither internal nor external reasons) provides us with the best explication of these two distinct notions of rationality, and that the contextual interpretation accommodates all the counterexamples associated with the central problem.