Technology Dissertation Topics: AI, Ethics & Research

Research Angles on AI and Technology

AI in healthcare decision-making raises urgent questions. If algorithms make diagnosis recommendations or treatment decisions, how accurate are they? Do they perpetuate existing medical biases? How transparent are they to clinicians and patients? A dissertation might analyse whether NHS or private sector hospitals are implementing algorithmic decision support responsibly. What governance exists? What happens when algorithms make errors?

Algorithmic bias and fairness is increasingly studied. Algorithms trained on biased historical data reproduce that bias. Hiring algorithms, lending algorithms, criminal risk assessment algorithms have all been shown to discriminate. A dissertation might focus on one sector: How are bias and fairness defined in algorithmic auditing? What technical methods exist for detecting bias? What's the gap between technical possibility and actual audit practice?

AI regulation and governance is rapidly evolving. The EU AI Act, UK AI regulation approach, US frameworks differ . A dissertation might analyse whether regulatory approaches actually protect consumers and workers or whether they're captured by industry. What obligations do different frameworks impose? How are they enforced? What's missing?

Digital divide and access: Not everyone benefits from technology equally. Some people lack internet access. Some lack skills. Some use technology differently. A dissertation might examine disparities in tech access in the UK by region or socio-economic status. Or examine how older people or people with disabilities experience digital systems designed for young, able-bodied users.

Platform economics: How do technology companies structure their businesses? What's the model? How does it affect workers, consumers, content creators? A dissertation might analyse labour practices in the gig economy (Uber, Deliveroo, Instacart) or examine how social media platforms monetise user attention and data.

Data privacy and rights: What data do organisations collect? Who accesses it? What are people's rights? UK data protection law (GDPR and Data Protection Act 2018) establishes rights. A dissertation might examine whether organisations actually respect these rights or whether enforcement is weak.

Fintech disruption: How have technology companies disrupted financial services? What risks exist? What regulation is appropriate? A dissertation might examine cryptocurrency, digital banking, or algorithmic trading.

Smart cities: Governments and technology companies are investing in "smart city" infrastructure. Sensors, data analytics, automated systems. What benefits are claimed? What actually materialises? What are privacy risks? What are equity concerns? A dissertation might do case study analysis of a specific smart city initiative.

Surveillance capitalism: How extensively do technology companies and governments surveil populations? What are the implications for autonomy and democracy? Shoshana Zuboff's work is foundational. A dissertation might examine specific manifestations: facial recognition, mobile location tracking, social media data harvesting.

The way in which you present your findings will have a considerable impact on how your marker perceives the quality of your analysis, since a well-organised and clearly written results chapter makes it much easier for the reader to understand and evaluate your conclusions. For quantitative studies, it is conventional to present your findings in a structured sequence that moves from descriptive statistics through to the results of inferential tests, with clear tables and figures that summarise the key data in an accessible format. Qualitative researchers typically organise their findings around the themes or categories that emerged during analysis, using illustrative quotes from participants or examples from their data to support each thematic claim they make. Regardless of which approach you take, you should ensure that your results chapter presents your findings as objectively as possible, saving your interpretation and evaluation of those findings for the discussion chapter that follows.