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Ariful Islam Anik
PhD Student
anikmai [at] myumanitoba.ca
External Website ↗
Advisor: Andrea Bunt
Research Areas: Human-Computer Interaction, Human-Centered AI, Intelligent User Interfaces
About
I am a graduate student doing my PhD in Computer Science at the University of Manitoba. I completed my MSc in the same department in Fall 2020. My research interests are in the areas of human-computer interaction and human-centered AI. I am interested to work with explainable AI systems, designing and personalizing explanations, and study the factors that impact people’s perception of and interaction with AI explanations.
I was born in Bangladesh and I moved to Canada for my MSc in Fall 2018. I completed my BSc in Computer Science and Engineering in 2016 at the Islamic University of Technology, Gazipur, Dhaka, Bangladesh. I also worked in the same institution as an Assistant Programmer till Fall 2018.
I love to listen to a lot of music, and I am an avid soccer fan!
Projects
Publications
Diversity Challenges in Recruiting for Human-Centered Explainable AI Studies
A. I. Anik and A. Bunt, "Diversity Challenges in Recruiting for Human-Centered Explainable AI Studies," 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Liverpool, United Kingdom, 2024, pp. 429-431, doi: 10.1109/VL/HCC60511.2024.00067.
Supporting User Critiques of AI Systems via Training Dataset Explanations
A. I. Anik and A. Bunt, "Supporting User Critiques of AI Systems via Training Dataset Explanations: Investigating Critique Properties and the Impact of Presentation Style," 2024 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC), Liverpool, United Kingdom, 2024, pp. 134-147, doi: 10.1109/VL/HCC60511.2024.00024.
Understanding and Mitigating the Negative Consequences of Training Dataset Explanations
Ariful Islam Anik and Andrea Bunt. 2023. Understanding and Mitigating the Negative Consequences of Training Dataset Explanations. In Proceedings of the ACM CHI 2023 Workshop on Human-centered Explainable AI (HCXAI), April 23–28, 2023, Hamburg, Germany.
Data-Centric Explanations: Explaining Training Data of ML Systems to Promote Transparency
Ariful Islam Anik and Andrea Bunt. 2021. Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency. In CHI Conference on Human Factors in Computing Systems (CHI’21), May 08–13, 2021, Yokohama, Japan. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3411764.3445736
Investigating Explanations that Target Training Data
Ariful Islam Anik and Andrea Bunt. 2021. Investigating Explanations that Target Training Data. In Proceedings of the ACM IUI workshop on Transparency and Explanations in Smart Systems (TExSS). Texas, USA, 7 pages.
Data-Centric Explanations: Explaining Training Data of ML Systems to Promote Transparency
Ariful Islam Anik. Data-Centric Explanations: Explaining Training Data of Machine Learning Systems to Promote Transparency. MSc Thesis (2020). University of Manitoba, Canada.