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.

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.

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)

The global rollout of Covid-19 vaccination has amplified concerns regarding vaccination hesitancy, which presents a formidable challenge for public health authorities. While widespread vaccination is imperative for containing the virus, hesitancy arises from multiple factors, including apprehensions about potential long-term vaccine side-effects and misconceptions about vaccination. This has prompted discussions about the feasibility of mandating Covid-19 vaccination, especially for specific groups like healthcare professionals and the general public. This paper critically examines the tension between a paternalistic approach and individual autonomy, advocating for a balanced strategy that considers both public health imperatives and individual rights. Drawing insights from existing literature, the study emphasizes the need for a nuanced and pragmatic approach to address vaccine hesitancy effectively.

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.

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.

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.

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.

What is it for an agent to have a reason to do a certain action? Does this mean that she would desire to do the action under specified conditions, or that there is some external consideration, which she ought to follow? The former affective (i.e., desire-based) theory is ascribed to Humeans, whereas the latter cognitive theory is adopted by Kantians. The debate between the two views has seemingly ended up in a theoretical standoff, and most of the theorists of practical reason have recently turned their attention to the motivational attitude—preceded either by affective attitudes or by cognitive attitudes—as a necessary constituent of having a reason. They contend that an agent has a reason to f only if, on certain occasions, she would be motivated to f: I refer to it as Motivation Reason Internalism. In this paper, I argue that Motivation Reason Internalism is a mirage; it does not obtain necessarily. I classify major versions of Motivation Reason Internalism into three idealized counterfactual versions, arguing that they obtain only contingently, since they all face counterexamples. The Motivation Reason Internalist might attempt to dodge the counterexamples by appealing to Contextual Motivations Reason Internalism, but I show that this move also fails. I discuss a third cause for motivation, some unconscious cause, other than cognitive or affective attitudes, which can still be a motivation maker or breaker. These unconscious causes, such as an underlying biological inclination or psychological bias, functions as motivation maker/breaker but not a reason maker/breaker. This makes room for a variety of cases in which we have reason to perform an action in the absence of any kind (i.e., actual or counterfactual) of motivation.

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.

We conducted a systematic literature review on the ethical considerations of the use of contact tracing app technology, which was extensively implemented during the COVID-19 pandemic. The rapid and extensive use of this technology during the COVID-19 pandemic, while benefiting the public well-being by providing information about people’s mobility and movements to control the spread of the virus, raised several ethical concerns for the post-COVID-19 era. To investigate these concerns for the post-pandemic situation and provide direction for future events, we analyzed the current ethical frameworks, research, and case studies about the ethical usage of tracing app technology. The results suggest there are seven essential ethical considerations—privacy, security, acceptability, government surveillance, transparency, justice, and voluntariness—in the ethical use of contact tracing technology. In this paper, we explain and discuss these considerations and how they are needed for the ethical usage of this technology. The findings also highlight the importance of developing integrated guidelines and frameworks for implementation of such technology in the post- COVID-19 world.