The advent of generative artificial intelligence and the widespread adoption of it in society engendered intensive debates about its ethical implications and risks. These risks often differ from those associated with traditional discriminative machine learning. To synthesize the recent discourse and map its normative concepts, we conducted a scoping review on the ethics of generative artificial intelligence, including especially large language models and text-to-image models. Our analysis provides a taxonomy of 378 normative issues in 19 topic areas and ranks them (...) according to their prevalence in the literature. The study offers a comprehensive overview for scholars, practitioners, or policymakers, condensing the ethical debates surrounding fairness, safety, harmful content, hallucinations, privacy, interaction risks, security, alignment, societal impacts, and others. We discuss the results, evaluate imbalances in the literature, and explore unsubstantiated risk scenarios. (shrink)

Scientific research is supposed to acquire or generate knowledge, but such a purpose would be severely undermined by instances of research misconduct (RM) and questionable research practices (QRP). RM and QRP are often framed in terms of moral transgressions by individuals (bad apples) whose aberrant acts could be made conducive by shortcomings in regulatory measures of organizations or institutions (bad barrels). This notion presupposes, to an extent, that the erring parties know exactly what they are doing is wrong and morally (...) culpable, but had nonetheless proceeded to commit wrongful acts. However, a confession of intent to deceived is often not readily admitted by perpetrators of RM. I posit that beyond the simplistic notion of conscious moral transgression, deficits in epistemic virtues and/or the prevalence of epistemic vices have important roles to play in initiating and driving RM/QRP. For the individual perpetrator, deficits in epistemic virtues could lead to or amplify errors in one’s desperate attempt to be accomplished or to excel, and pushes one across the ethical line or down the slippery slope of misconduct. Likewise, a lack of epistemic virtue within perpetrators’ institution or organization could make it conducive for deceitful acts and suppress indications and warning signs for the former. Furthermore, epistemic vices exhibited by reviewers, editors and journals could also promote RM/QRP. In this view, epistemic failings, rather than widespread moral deficiencies of individuals within the research ecosystem, may underlie the prevalence of RM/QRP. (shrink)

Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be (...) valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work. (shrink)

The new generative artificial intelligence (AI) tools, and especially the large language models (LLMs) of which ChatGPT is the most prominent example, have the potential to transform many aspects o...

Generative artificial intelligence (AI) has the potential to transform many aspects of scholarly publishing. Authors, peer reviewers, and editors might use AI in a variety of ways, and those uses might augment their existing work or might instead be intended to replace it. We are editors of bioethics and humanities journals who have been contemplating the implications of this ongoing transformation. We believe that generative AI may pose a threat to the goals that animate our work but could also be (...) valuable for achieving those goals. In the interests of fostering a wider conversation about how generative AI may be used, we have developed a preliminary set of recommendations for its use in scholarly publishing. We hope that the recommendations and rationales set out here will help the scholarly community navigate toward a deeper understanding of the strengths, limits, and challenges of AI for responsible scholarly work. (shrink)

Journals and publishers are increasingly using artificial intelligence (AI) to screen submissions for potential misconduct, including plagiarism and data or image manipulation. While using AI can enhance the integrity of published manuscripts, it can also increase the risk of false/unsubstantiated allegations. Ambiguities related to journals’ and publishers’ responsibilities concerning fairness and transparency also raise ethical concerns. In this Topic Piece, we offer the following guidance: (1) All cases of suspected misconduct identified by AI tools should be carefully reviewed by humans (...) to verify accuracy and ensure accountability; (2) Journals/publishers that use AI tools to detect misconduct should use only well-tested and reliable tools, remain vigilant concerning forms of misconduct that cannot be detected by these tools, and stay abreast of advancements in technology; (3) Journals/publishers should inform authors about irregularities identified by AI tools and give them a chance to respond before forwarding allegations to their institutions in accordance with Committee on Publication Ethics guidelines; (4) Journals/publishers that use AI tools to detect misconduct should screen all relevant submissions and not just random/purposefully selected submissions; and (5) Journals should inform authors about their definition of misconduct, their use of AI tools to detect misconduct, and their policies and procedures for responding to suspected cases of misconduct. (shrink)

The new generative artificial intelligence (AI) tools, and especially the large language models (LLMs) of which ChatGPT is the most prominent example, have the potential to transform many aspects o...

Artificial intelligence (AI), large language models (LLMs), such as Open AI’s ChatGPT, have a remarkable ability to process and generate human language but have also raised complex and novel ethica...