Artificial Intelligence

The article discusses concerns regarding the potential misuse of AI language models like ChatGPT in spreading misinformation and deepening societal divides. It highlights the ethical considerations surrounding the use of this technology while emphasizing the importance of responsible usage. There is a need for higher education faculty to educate students about the ethical implications and limitations of such technology.

The article discusses a legal case involving the use of AI-generated art on DeviantArt and Getty Images, highlighting the challenges and questions regarding copyright and ownership in the context of AI-generated content. It's essential for higher education faculty to follow this case as it raises important ethical and legal considerations related to the use of AI-generated art, which may impact how educators and institutions engage with and teach about AI-generated content.

The article discusses the advancements in generative AI models like DALL·E and MidJourney Stable Diffusion Models, highlighting their potential applications in various fields, including higher education. It underscores the importance of faculty staying informed about these innovations to harness their potential for creative and educational purposes in the academic setting.

Information capitalism, the system where information, a historically, largely free and ubiquitous product of basic communication, is commodified by private owners for profit, is entrenched in our society. Information brokers have consolidated and swallowed up huge amounts of data, in a system that leaves data purchase, consumption, and use largely unregulated and unchecked. This article focuses on librarian ethics in the era of information capitalism, focusing specifically on an especially insidious arena of data ownership: surveillance capitalism and big data policing. While librarians value privacy and intellectual freedom, librarians increasingly rely on products that sell personal data to law enforcement, including Immigration and Customs Enforcement (ICE). Librarians should consider how buying and using these products in their libraries comports with our privacy practices and ethical standards.

The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

Compares the contents of thirty-six prominent AI principles documents side-by-side. This effort uncovered a growing consensus around eight key thematic trends: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values

The post contains a selection of resources covering ethics in AI on a range of subjects including Race and AI, Gender and AI, Disability and AI, Online Resources on AI, and General Sources on AI.

The article delves into the emerging legal challenges associated with generative AI technology and emphasizes that the current legal cases represent just the tip of the iceberg. It underscores the importance for higher education faculty to stay informed about these legal developments, as they have implications for research, intellectual property, and responsible use of AI in academia.

How has recent AI Ethics literature addressed topics such as fairness and justice in the context of continued social and structural power asymmetries? We trace both the historical roots and current landmark work that have been shaping the field and categorize these works under three broad umbrellas: (i) those grounded in Western canonical philosophy, (ii) mathematical and statistical methods, and (iii) those emerging from critical data/algorithm/information studies. We also survey the field and explore emerging trends by examining the rapidly growing body of literature that falls under the broad umbrella of AI Ethics. To that end, we read and annotated peer-reviewed papers published over the past four years in two premier conferences: FAccT and AIES. We organize the literature based on an annotation scheme we developed according to three main dimensions: whether the paper deals with concrete applications, use-cases, and/or people's lived experience; to what extent it addresses harmed, threatened, or otherwise marginalized groups; and if so, whether it explicitly names such groups. We note that although the goals of the majority of FAccT and AIES papers were often commendable, their consideration of the negative impacts of AI on traditionally marginalized groups remained shallow. Taken together, our conceptual analysis and the data from annotated papers indicate that the field would benefit from an increased focus on ethical analysis grounded in concrete use-cases, people's experiences, and applications as well as from approaches that are sensitive to structural and historical power asymmetries.

The article profiles an artist who is at the forefront of AI-generated art but expresses dissatisfaction with how the technology is being used to create derivative works without proper credit or compensation. Higher education faculty should take note of this article as it sheds light on the ethical and creative challenges in the field of AI-generated art, offering valuable insights for discussions on intellectual property and artistic expression in the digital age.

Roopika Risam (2018). Chapter 3 from Bodies of Information: Intersectional Feminism and Digital Humanities, edited by Elizabeth Losh and Jacqueline Wernimont. "In considering the potentially important roles of machine learning and natural language processing in next-generation work, [Risam] points out that "artificial intelligence purports to represent universal 'human' intellectual processes but, in fact, is only representative of a fictive 'universal' model of human cognition that elides both women, peoples of the Global South, and those at the interstices of these categories."

Given a growing concern about the lack of ethical consideration in the Artificial Intelligence (AI) field, many have begun to question how dominant approaches to the disciplinary education of computer science (CS)---and its implications for AI---has led to the current "ethics crisis". However, we claim that the current AI ethics education space relies on a form of "exclusionary pedagogy," where ethics is distilled for computational approaches, but there is no deeper epistemological engagement with other ways of knowing that would benefit ethical thinking or an acknowledgement of the limitations of uni-vocal computational thinking. This results in indifference, devaluation, and a lack of mutual support between CS and humanistic social science (HSS), elevating the myth of technologists as "ethical unicorns" that can do it all, though their disciplinary tools are ultimately limited. Through an analysis of computer science education literature and a review of college-level course syllabi in AI ethics, we discuss the limitations of the epistemological assumptions and hierarchies of knowledge which dictate current attempts at including ethics education in CS training and explore evidence for the practical mechanisms through which this exclusion occurs. We then propose a shift towards a substantively collaborative, holistic, and ethically generative pedagogy in AI education.