SciEvent: Benchmarking Multi-domain Scientific Event Extraction

Scientific information extraction (SciIE) has primarily relied on entity-relation extraction in narrow domains, limiting its applicability to interdisciplinary research and struggling to capture the necessary context of scientific information, often resulting in fragmented or conflicting statements. In this paper, we introduce SciEvent, a novel multi-domain benchmark of scientific abstracts annotated via a unified event extraction (EE) schema designed to enable structured and context-aware understanding of scientific content. It includes 500 abstracts across five research domains, with manual annotations of event segments, triggers, and fine-grained arguments. We define SciIE as a multi-stage EE pipeline: (1) segmenting abstracts into core scientific activities--Background, Method, Result, and Conclusion; and (2) extracting the corresponding triggers and arguments. Experiments with fine-tuned EE models, large language models (LLMs), and human annotators reveal a performance gap, with current models struggling in domains such as sociology and humanities. SciEvent serves as a challenging benchmark and a step toward generalizable, multi-domain SciIE.

A Democratic Platform for Engaging with Disabled Community in Generative AI Development

Artificial Intelligence (AI) systems, especially generative AI technologies are becoming more relevant in our society. Tools like ChatGPT are being used by members of the disabled community e.g., Autistic people may use it to help compose emails. The growing impact and popularity of generative AI tools have prompted us to examine their relevance within the disabled community. The design and development phases often neglect this marginalized group, leading to inaccurate predictions and unfair discrimination directed towards them. This could result from bias in data sets, algorithms, and systems at various phases of creation and implementation. This workshop paper proposes a platform to involve the disabled community while building generative AI systems. With this platform, our aim is to gain insight into the factors that contribute to bias in the outputs generated by generative AI when used by the disabled community. Furthermore, we expect to comprehend which algorithmic factors are the main contributors to the output's incorrectness or irrelevancy. The proposed platform calls on both disabled and non-disabled people from various geographical and cultural backgrounds to collaborate asynchronously and remotely in a democratic approach to decision-making.

Exploring outlooks towards generative AI-based assistive technologies for people with Autism

The last few years have significantly increased global interest in generative artificial intelligence. Deepfakes, which are synthetically created videos, emerged as an application of generative artificial intelligence. Fake news and pornographic content have been the two most prevalent negative use cases of deepfakes in the digital ecosystem. Deepfakes have some advantageous applications that experts in the subject have thought of in the areas of filmmaking, teaching, etc. Research on the potential of deepfakes among people with disabilities is, however, scarce or nonexistent. This workshop paper explores the potential of deepfakes as an assistive technology. We examined Reddit conversations regarding Nvdia's new videoconferencing feature which allows participants to maintain eye contact during online meetings. Through manual web scraping and qualitative coding, we found 162 relevant comments discussing the relevance and appropriateness of the technology for people with Autism. The themes identified from the qualitative codes indicate a number of concerns for technology among the autistic community. We suggest that developing generative AI-based assistive solutions will have ramifications for human-computer interaction (HCI), and present open questions that should be investigated further in this space.