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.