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Identifying Gendered Language
Shweta Soundararajan and Sarah Jane Delany
Gendered language refers to the use of words that indicate the gender of an individual. It can be explicit, where the gender is directly implied by the specific words used (e.g., mother, she, man), or it can be implicit, where societal roles and behaviors convey a person's gender. For example, expectations that women display communal traits (e.g., affectionate, caring, gentle) and men display agentic traits (e.g., assertive, competitive, decisive). The presence of gendered language in natural language processing (NLP) systems can reinforce gender stereotypes and bias. Our work introduces an approach to creating gendered language datasets using ChatGPT. These datasets are designed to support data-driven methods for identifying gender stereotypes and mitigating gender bias. The approach focuses on generating implicit gendered language that captures and reflects stereotypical characteristics or traits associated with a specific gender. This is achieved by constructing prompts for ChatGPT that incorporate gender-coded words sourced from gender-coded lexicons. The evaluation of the datasets generated demonstrates good examples of English-language gendered sentences that can be categorized as either contradictory to or consistent with gender stereotypes. Additionally, the generated data exhibits a strong gender bias.
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Detecting Patches on Road Pavement Images Acquired with 3D Laser Sensors using Object Detection and Deep Learning
Ibrahim Hassan Syed, Dympna O'Sullivan, Susan McKeever, David Power, Ray Mcgowan, and Kieran Feighan
Regular pavement inspections are key to good road maintenance and road defect corrections. Advanced pavement inspection systems such as LCMS (Laser Crack Measurement System) can automatically detect the presence of different defects using 3D lasers. However, such systems still require manual involvement to complete the detection of pavement defects. This work proposes an automatic patch detection system using an object detection technique. Results show that the object detection model can successfully detect patches inside LCMS images and suggest that the proposed approach could be integrated into the existing pavement inspection systems.
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Investigating the Use of Conversational Agents as Accountable Buddies to Support Health and Lifestyle Change
Ekaterina Uetova, Dympna O'Sullivan, Lucy Hederman, and Robert J. Ross
The poster focuses on the role of conversational agents in promoting health and well-being. Results of the literature review indicate that negative emotions can hinder individuals from taking necessary actions related to their health. The study concludes that understanding and addressing emotional barriers is essential to facilitating early access to health services and improving well-being. The poster outlines plans to investigate motivation strategies, develop a prototype conversational agent based on user study insights and chat log data, and incorporate emotion regulation to effectively manage users' emotional experiences.
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