As AI systems become more advanced and integrated into our lives, there has been a corresponding urgency to ensure they align with social values and norms, and that their benefits significantly outweigh any potential harms. In response to this imperative, legal and regulatory bodies globally are engaged in a concerted effort to develop comprehensive AI regulations. The increasing size, generality, opaqueness, and closed nature of present-day AI systems, however, pose significant challenges to effective regulation. Even when requirements can be articulated, it remains uncertain whether and how we can verify an AI system's compliance with these standards: A requirement that cannot be checked will not provide effective protection. If we believe that AI systems should be regulated, then AI systems must be designed to be regulatable.

The rapid growth in the usage and applications of Natural Language Processing (NLP) in various sociotechnical solutions has highlighted the need for a comprehensive understanding of bias and its impact on society. While research on bias in NLP has expanded, several challenges persist that require attention. These include the limited focus on sociodemographic biases beyond race and gender, the narrow scope of analysis predominantly centered on models, and the technocentric implementation approaches. This paper addresses these challenges and advocates for a more interdisciplinary approach to understanding bias in NLP. The work is structured into three facets, each exploring a specific aspect of bias in NLP.

Suresh Venkatasubramanian (University of Utah), Nadya Bliss (Arizona State University), Helen Nissenbaum (Cornell University), and Melanie Moses (University of New Mexico) Overview Long gone are the days when computing was the domain of technical experts. We live in a world where computing technology--especially artificial intelligence--permeates every aspect of our daily lives, playing a significant role in augmenting and even replacing human decision-making in a broad range of situations. AIenabled technologies can adjust to your child's level of understanding by processing a pattern of mistakes; AI systems can leverage combinations of sensor inputs to choose and carry out braking actions in your car; web browsers with AI capabilities can reason from past observations of your searches to recommend a new cuisine in a new location. Innovations in AI have focused primarily on the questions of "what" and "how"--algorithms for finding patterns in web searches, for instance--without adequate attention to the possible harms (such as privacy, bias, or manipulation) and without adequate consideration of the societal context in which these systems operate. As a result of this tight technical focus, and the rapid, worldwide explosion in its use, AI has come with a storm of unanticipated socio-technical problems, ranging from algorithms that act in racially or gender-biased ways, get caught in feedback loops that perpetuate inequalities, or enable unprecedented behavioral monitoring surveillance that challenges the fundamental values of free, democratic societies.

David Mindell has spent his career defying traditional distinctions between disciplines. His work has explored the ways humans interact with machines, drive innovation, and maintain societal well-being as technology transforms our economy. And, Mindell says, he couldn't have done it anywhere but MIT. He joined MIT's faculty 23 years ago after completing his PhD in the Program in Science, Technology, and Society, and he currently holds a dual appointment in engineering and humanities as the Frances and David Dibner Professor of the History of Engineering and Manufacturing in the School of Humanities, Arts, and Social Sciences and professor of aeronautics and astronautics. Mindell's experience combining fields of study has shaped his ideas about the relationship between humans and machines.

Being the most innovative university in Germany, FAU has been strong in the subject of Artificial Intelligence for the past five decades. As a full spectrum university FAU pursues an interdisciplinary approach on this topic. With the newly launched FAU AI MAP the different disciplines are brought even closer together and move AI to a new level – true to the university motto "Knowledge in Motion". Being the most innovative university in Germany, FAU has been strong in the subject of Artificial Intelligence for the past five decades. As a full spectrum university FAU pursues an interdisciplinary approach on this topic.

The history of science and technology is often delineated by paradigm shifts. A paradigm shift is a fundamental change in how we view the world and our relationship to it. The big paradigm shifts are sometimes even referred to as an "age" or a "revolution". The Space Age is a perfect example. The middle of the 20th Century saw not only an incredible increase in public awareness of space and space travel, but many of the industrial and technical advances that we now take for granted were byproducts of the Space Age.

JAXenter: The term'intelligence' is not easy to understand. What's the best way to explain it and how can we apply it to machines? Marisa Tschopp: Human intelligence has been a very controversial topic and has undergone dramatic changes in history since the beginnings in the early 19th century. Intelligence gained importance especially in the educational context as these "mental abilities" were the best predictors for success in school and aimed to place students into the right classes. There are various, very elaborated theories, that define human intelligence.