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Asif Q. Gill

Publications and source records attributed to Asif Q. Gill.

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The Tool-to-Entity Threshold: Parasocial Dynamics of Personalised AI Agents in Shared Social Spaces

As AI agents acquire names, avatars, phone numbers, and persistent personalities, they increasingly inhabit the same messaging platforms and group conversations as the humans they serve, crossing from tools their users operate into social entities their users relate to. Yet no existing framework identifies the infrastructural markers that cause the shift: the anthropomorphism literature catalogues perceptual cues rendered inside the interaction surface, not the agent's placement in the user's social and computational graph. We propose the identity marker framework: six design variables - naming, visual identity, contact presence, personality derivation, social co-presence, and persistence - that collectively trigger a psychological reclassification, categorical in its consequences even where thetransition itself is gradual, and operating independently of model capability. We read the framework through parasocial interaction theory (Horton & Wohl, 1956) and the Computers Are Social Actors paradigm (Nass et al., 1994), and identify four novel dynamics that arise when such an agent joins existing group conversations: bidirectional information asymmetry, delegation legibility, social norm negotiation, and parasocial contagion. Our method is autoethnographic: the first author built and deployed a personalised agent into WhatsApp and Signal group chats over two months of live use, supplemented by twelve structured interviews with the group members who encountered it. We treat this as a preliminary qualitative evaluation of the framework, with controlled experimental validation set out as future work. The strongest design implication runs through all four dynamics: in shared social spaces, consent to an agent's presence is categorically distinct from consent to its processing of the messages exchanged there, and existing consent frameworks collapse the two.

cs.HC

Agile System Development Lifecycle for AI Systems: Decision Architecture

Agile system development life cycle (SDLC) focuses on typical functional and non-functional system requirements for developing traditional software systems. However, Artificial Intelligent (AI) systems are different in nature and have distinct attributes such as (1) autonomy, (2) adaptiveness, (3) content generation, (4) decision-making, (5) predictability and (6) recommendation. Agile SDLC needs to be enhanced to support the AI system development and ongoing post-deployment adaptation. The challenge is: how can agile SDLC be enhanced to support AI systems? The scope of this paper is limited to AI system enabled decision automation. Thus, this paper proposes the use of decision science to enhance the agile SDLC to support the AI system development. Decision science is the study of decision-making, which seems useful to identify, analyse and describe decisions and their architecture subject to automation via AI systems. Specifically, this paper discusses the decision architecture in detail within the overall context of agile SDLC for AI systems. The application of the proposed approach is demonstrated with the help of an example scenario of insurance claim processing. This initial work indicated the usability of a decision science to enhancing the agile SDLC for designing and implementing the AI systems for decision-automation. This work provides an initial foundation for further work in this new area of decision architecture and agile SDLC for AI systems.

cs.SE

AI-based Identity Fraud Detection: A Systematic Review

With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity fraud. Fraudsters may use these technologies to create highly sophisticated counterfeit personal identification documents, photos and videos. These advancements in the identity fraud landscape pose challenges for identity fraud detection and society at large. There is a pressing need to review and understand identity fraud detection methods, their limitations and potential solutions. This research aims to address this important need by using the well-known systematic literature review method. This paper reviewed a selected set of 43 papers across 4 major academic literature databases. In particular, the review results highlight the two types of identity fraud prevention and detection methods, in-depth and open challenges. The results were also consolidated into a taxonomy of AI-based identity fraud detection and prevention methods including key insights and trends. Overall, this paper provides a foundational knowledge base to researchers and practitioners for further research and development in this important area of digital identity fraud.

cs.AI