What are some topics for a final year dissertation relating to UX and technology

Short explanation: As AI systems increasingly mediate user experiences, designers must address how algorithmic decisions, training data, and interface choices affect fairness, trust, and user autonomy. A dissertation on ethical UX for AI-driven interfaces would investigate methods to detect and mitigate bias in AI outputs (e.g., through dataset auditing, diverse testing, and algorithmic fairness techniques), and translate those technical safeguards into clear, usable interactions (e.g., explanations, uncertainty displays, and controls for correction). It would also explore transparency practices—what to disclose, when, and how—to support informed consent and accountability without overwhelming users. The work combines empirical user research, design patterns, and evaluation metrics to propose actionable guidelines for creating interfaces that promote equity, explainability, and user agency in AI-mediated contexts. Suggested areas to cover: definitions and types of bias; methods for bias detection and mitigation; explainable AI (XAI) techniques and UX patterns; usability studies on transparency and trust; legal and ethical frameworks (e.g., GDPR, AI ethics guidelines); evaluation metrics for fairness and transparency. Key references: - Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law. - Kagal, L., et al. (2020). Human-Centered Explainable AI: A Survey. (See research on XAI and UX). - Friedman, B., & Nissenbaum, H. (1996). Bias in computer systems. ACM Transactions on Information Systems.