http://hdl.handle.net/10453/35217 2026-10-15T04:13:37Z http://hdl.handle.net/10453/197209 Title: Explaining Hallucination of Artificial Intelligence in Data-Driven Decision-Making Authors: Kabir, ZS Abstract: Artificial intelligence transforms a paradigm shift in delivering data-driven innovative knowledge, where hallucinated information can influence users' decision-making. It demands exploring the effects of hallucinations towards the trustworthiness and ethics of AI in decision-making. Thus, this chapter aims to explain the implications of hallucinations across data-driven decision-making. The chapter conceptualizes a framework for explaining a process flow of mitigating AI-driven hallucinations that engages critical flaws in promoting innovative and strategic decision making for professional practices. Theoretically, the framework explains the effects of hallucinations and how it impacts decision making, following multiple theories and models. We present a metatheoretical framework to study the hallucinations of LLMs-based AI, which will allow researchers and practitioners to capture a multidisciplinary theoretical lens to investigate the complexities of AI-driven hallucinations in the data-driven innovative decision-making process. 2026-11-20T00:00:00Z http://hdl.handle.net/10453/197208 Title: Innovative Decision Making Through Artificial Intelligence in a Connected Healthcare System Authors: Kabir, ZS Abstract: Artificial intelligence (AI) has transformed the healthcare industry by improving diagnosis, recordkeeping, and predictive analysis. To provide an in-depth picture of artificial intelligence-enabled healthcare technology, this chapter centers on the emergence of AI capabilities in medical applications. This chapter develops an AIpowered innovative decision-making support framework that explains sequential steps to get an optimum level of healthcare outcomes through data input, perception analysis, decision intelligence, cognitive reasoning, explainable decision, and feedback loop. By leveraging innovative decision making and advanced artificial intelligence techniques, the chapter seeks to generate actionable and valuable insights that can improve patient outcomes with the least possible resources like health professionals, accommodation, and medical instruments. The chapter reveals considerable importance for hospital systems transitioning from reactive care to proactive that engages in innovative data-driven decision-making in a connected healthcare system. 2026-11-20T00:00:00Z http://hdl.handle.net/10453/197206 Title: Co-designing culturally responsive simulation-based learning: AI, First Peoples’ knowledges, and the implications for social work field education Authors: Bennett, B; Loban, R; Beetson, S; Evans, K; Dalziel, MV; Jefferies, G; Harris, S Abstract: This chapter explores the role of artificial intelligence (AI) in the teaching and learning of cultural responsiveness to Australian social work students. The aim of the project is to provide dynamic learning environments that facilitate co-designing culturally responsive social work practices with First Peoples and rural and remote communities in Australia. This encompasses the co-creation of simulated learning experiences as part of social work field education placements. The following critical questions guided this chapter: How are cultural understandings constructed by and embedded in AI technologies? What practice implications might arise at the intersection of social work education founded in settler educational contexts? What First Nations knowledges should be included in AI-generated learning? Most importantly, given that humans have traditionally been the primary source of creating and directing teaching and learning experiences (human-in-loop) in simulations, can AI-generated learning appropriately represent First Nations knowledges? 2026-01-01T00:00:00Z http://hdl.handle.net/10453/197168 Title: Approximation Does Not Help in Quantum Unitary Time-Reversal Authors: Chen, K; Yu, N; Zhang, Z Abstract: Access to the time-reverse U-1 of an unknown quantum unitary process U is widely assumed in quantum learning, metrology, and many-body physics. The fundamental task of unitary time-reversal dictates implementing U-1 to within diamond-norm error ϵ using black-box queries to the d-dimensional unitary U. Although the query complexity of this task has been extensively studied, existing lower bounds either hold only for the exact case (i.e., ϵ=0) or are suboptimal in d. This raises a central question: does approximation help reduce the query complexity of unitary time-reversal? We settle this question in the negative by establishing a robust and tight lower bound ω((1-ϵ)d2) with explicit dependence on the error ϵ. This implies that unitary time-reversal retains optimal exponential hardness (in the number of qubits) even when constant error is allowed. Our bound applies to adaptive and coherent algorithms with unbounded ancillas and holds even when ϵ is an average-case distance error. 2026-06-09T00:00:00Z