In recent years, the development of Large Language Models (LLMs) has made significant breakthroughs in the field of natural language processing and has gradually been applied to the field of humanities and social sciences research. LLMs have a wide range of application value in the field of humanities and social sciences because of its strong text understanding, generation and reasoning capabilities. In humanities and social sciences research, LLMs can analyze large-scale text data and make inferences. This article analyzes the large language model DeepSeek-R1 from seven aspects: low-resource language translation, educational question-answering, student writing improvement in higher education, logical reasoning, educational measurement and psychometrics, public health policy analysis, and art education.Then we compare the answers given by DeepSeek-R1 in the seven aspects with the answers given by o1-preview. DeepSeek-R1 performs well in the humanities and social sciences, answering most questions correctly and logically, and can give reasonable analysis processes and explanations. Compared with o1-preview, it can automatically generate reasoning processes and provide more detailed explanations, which is suitable for beginners or people who need to have a detailed understanding of this knowledge, while o1-preview is more suitable for quick reading. Through analysis, it is found that LLM has broad application potential in the field of humanities and social sciences, and shows great advantages in improving text analysis efficiency, language communication and other fields. LLM's powerful language understanding and generation capabilities enable it to deeply explore complex problems in the field of humanities and social sciences, and provide innovative tools for academic research and practical applications.

The increasing capacities of large language models (LLMs) present an unprecedented opportunity to scale up data analytics in the humanities and social sciences, augmenting and automating qualitative analytic tasks previously typically allocated to human labor. This contribution proposes a systematic mixed methods framework to harness qualitative analytic expertise, machine scalability, and rigorous quantification, with attention to transparency and replicability. 16 machine-assisted case studies are showcased as proof of concept. Tasks include linguistic and discourse analysis, lexical semantic change detection, interview analysis, historical event cause inference and text mining, detection of political stance, text and idea reuse, genre composition in literature and film; social network inference, automated lexicography, missing metadata augmentation, and multimodal visual cultural analytics. In contrast to the focus on English in the emerging LLM applicability literature, many examples here deal with scenarios involving smaller languages and historical texts prone to digitization distortions. In all but the most difficult tasks requiring expert knowledge, generative LLMs can demonstrably serve as viable research instruments. LLM (and human) annotations may contain errors and variation, but the agreement rate can and should be accounted for in subsequent statistical modeling; a bootstrapping approach is discussed. The replications among the case studies illustrate how tasks previously requiring potentially months of team effort and complex computational pipelines, can now be accomplished by an LLM-assisted scholar in a fraction of the time. Importantly, this approach is not intended to replace, but to augment researcher knowledge and skills. With these opportunities in sight, qualitative expertise and the ability to pose insightful questions have arguably never been more critical.

With the accelerating evolution of technology, artificial intelligence (AI) plays a growing role in decision-making processes. Humans are becoming increasingly dependent on algorithms to process information, recommend certain behaviors, and even take actions of their behalf. A research team has studied how humans react to the introduction of AI decision making. Specifically, they explored the question, 'is society ready for AI ethical decision making?' by studying human interaction with autonomous cars.

This article was published in the Spring 2021 issue of Litterae Populi. The full issue can be found here. The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as Japan's only research center specializing in hybrid research and education in the humanities, social sciences, neuroscience and AI. With the mission to create new knowledge on the nature of human existence, the Center conducts research and education taking advantage of the strengths and characteristics of Hokkaido University as a leading research university. In July 2019, the Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) was established as a university facility and it launched its activities. CHAIN's mission is to provide interdisciplinary research and education that integrate arts and sciences at the intersection of the humanities, social sciences, neuroscience and artificial intelligence (AI), i.e., to be a place where new knowledge is generated.

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.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.

The College of Humanities and Social Sciences (CHSS) at HBKU aims to deliver innovative programs that meet educational needs in the fields of humanities and social sciences for Qatar and the region. The College of Humanities and Social Sciences (CHSS) at Hamad Bin Khalifa University (HBKU) invites applications for Open Rank positions in the field of Translation Studies. The successful candidate will have long-standing experience in the field of Intercultural and Literary Translation, or Machine Translation, Artificial Intelligence and/or Terminology, a dynamic and innovative research agenda, as evidenced through an internationally recognized, strong record of peer-reviewed publications. The candidate will work closely with other programs in the college, in particular the PhD Program in Humanities and Social Sciences, and with national, regional and international partners and stakeholders. The candidate will be expected to teach graduate courses at MA and PhD level, applying a range of methodologies for teaching and assessment, contribute to all levels of curriculum development in the area(s) of specialty including the development of the interdisciplinary PhD in Humanities and Social Sciences.

Thousands of academics are gathering in Vancouver for the annual Congress of the Humanities and Social Sciences from June 1-7. They will present papers on everything from child marriage in Canada to why dodgeball is problematic. It's been the edict of parents, teachers and etiquette experts since time immemorial: Not every thought that pops into your head needs to come out of your mouth. Discretion helps hold our society together. We don't tell each other how we really feel. But now computers are learning to read our feelings from our faces.