• University of Huddersfield Queensgate Huddersfield HD1 3DH

    United Kingdom

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Dr Isa Inuwa-Dutse is lecturer in the Department of Computer Science at the University of Huddersfield, UK. He is a visiting lecturer at the University of Hertfordshire. Isa did his postdoc at the University of St Andrews on eXplainable Artificial Intelligence (XAI) project aimed at making ML systems more transparent and improving end-users' engagements through an interactive argumentative framework. Isa’s research lies at the intersection of Natural Language Processing (NLP) and Machine Learning (ML) aimed at developing useful tools with applications across various domains.  

Research Degree Supervision

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  1. Explainable AI
    • Operationalisation of Explainable AI-powered Systems –The utility of AI-driven systems transcends expert users, but non-expert users are heavily relying on them for many reasons. In the quest to democratise AI-powered systems(akin to the GDPR’s right of the user to know), this project will investigate how explainable artificial intelligence will be useful in improving non-expert users’ engagements with AI-powered systems, which are generally opaque, through an interactive explainable artificial framework. The end goal is to make decisions from such systems to be intuitive and discernable through the prism of rigorous theoretical and empirical analysis. The study will improve accessibility, trust and end-user's expertise to fully harness the power of the systems across various application domains.

  2. NLP and Low Resource Languages
    • Expansive NLP pipeline for pedagogical needs – With the increasing success of deep learning models and the huge availability of social media data, many exciting research problems are open for exploration. In the quest to make computers more amenable to natural language through useful computational tools, the proposed project will focus on developing an NLP pipeline with a wide range of applications in uncovering meanings in data, especially those expressed in human natural language (with emphasis on low resource languages). Among the objectives of the project is to support pedagogical activities in low resource languages.
  3. Social Network Analysis:
    • Misinformation in the era of pandemics –It is well-known how online social media platforms promote viral propagation of all sorts of information, making it suitable for unwanted and fake information to proliferate, especially during a critical period. A case in point is the prevailing COVID-19 pandemic, in which exposure to misleading information could lead to a catastrophic outcome. As virus propagate, so is fake news or misinformation about it; hence it is equally important to simultaneously curtail propagation of the virus and its associated negative infodemic. It would be crucial to leverage social media posts as signals to identify the proliferation of spurious content to develop a real-time monitoring system of health-related content to inform preemptive measures.
    • Flattening the curve: a posthoc analysis of COVID-19 aftermath –COVID-19, akin to illnesses such as diabetes, seems to require prolong precautionary measures to observe in managing it. Knowledge about the impact of the virus is evolving, and its long-term effect is yet to be fully established.Thus, it is pertinent to incorporate diverse information sources from social media to help in offering a useful and holistic prevention pathway that is better equipped to tackle resurgences and other related eventualities. 
    • Improving Civilised Online Social Interactions –In the realm of computational sociometry, a more civilised online social interactions can be achieved through the analysis of multiplexity of relationships, multiple pathways of forming online connection; structural hole, which hinders transparency in social networks and tends to promote unethical behaviour. A critical analysis of these aspects and dispensation of spurious content will promote acceptable online social interactions. 

Research Expertise and Interests

  • AI, Machine Learning, Data Mining, Natural Language Processing, Social Network Analysis


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