Accepted Papers

The digital realm has witnessed the rise of various search modalities, among which the Image-Based Conversational Search System stands out. This research delves into the design, implementation, and evaluation of this specific system, juxtaposing it against its text-based and mixed counterparts. A diverse participant cohort ensures a broad evaluation spectrum. Advanced tools facilitate emotion analysis, capturing user sentiments during interactions, while structured feedback sessions offer qualitative insights. Results indicate that while the text-based system minimizes user confusion, the image-based system presents challenges in direct information interpretation. However, the mixed system achieves the highest engagement, suggesting an optimal blend of visual and textual information. Notably, the potential of these systems, especially the image-based modality, to assist individuals with intellectual disabilities is highlighted. The study concludes that the Image-Based Conversational Search System, though challenging in some aspects, holds promise, especially when integrated into a mixed system, offering both clarity and engagement.

Recommender systems are invaluable tools in filtering vast amounts of information online. However, there are ethical challenges related to their objectives and design that have the potential to make some users vulnerable. Within emergent AI policy and regulation, vulnerable users have been given safeguarding measures to protect them against manipulation or exploitation. Vulnerable users are primarily defined as children and adults with particular attributes. However, this definition focuses attention on the cause of vulnerability being the attributes of users rather than the design of recommender systems themselves when all users, regardless of personal characteristics, could potentially be considered vulnerable to the negative effects of ethical issues pertaining to recommender algorithms. This paper examines three threads of vulnerability within recommender systems: vulnerability derived from user attributes, the vulnerabilities of the recommender systems themselves and vulnerability caused by the nature of interactions between users and recommendation algorithms. This paper argues that while it is essential to offer more protection and assistance to users who are considered vulnerable by virtue of certain characteristics, it is also important to acknowledge the possibility of all users being rendered vulnerable by features of the recommendation algorithms themselves. This reconsideration of the concept of vulnerability serves to highlight the importance of researching the effects of recommender algorithms on user groups that are currently understudied.