Explaining the data or explaining a model? Shapley values that uncover non-linear dependencies. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (CoRR, 2020)

Shapley Value Confidence Intervals for Attributing Variance Explained. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (Frontiers Appl. Math. Stat., 2020)

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (IEEE Access, 2021)

The social dilemma in AI development and why we have to solve it. - Inga Strümke; Marija Slavkovik 001; Vince I. Madai (CoRR, 2021)

Inferring feature importance with uncertainties in high-dimensional data. - Pål Vegard Johnsen; Inga Strümke; Signe Riemer-Sørensen; Andrew Thomas DeWan; Mette Langaas (CoRR, 2021)

Artificial Intelligence in Dry Eye Disease. - Andrea M. Storås; Inga Strümke; Michael A. Riegler; Jakob Grauslund; Hugo Lewi Hammer; Anis Yazidi; Pål Halvorsen; Kjell G. Gundersen; Tor Paaske Utheim; Catherine Jackson (CoRR, 2021)

Model independent feature attributions: Shapley values that uncover non-linear dependencies. - Daniel Vidali Fryer; Inga Strümke; Hien D. Nguyen 001 (PeerJ Comput. Sci., 2021)

Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning. - Sindre Benjamin Remman; Inga Strümke; Anastasios M. Lekkas (ACC, 2022)

Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning. - Andrea M. Storås; Anders Åsberg; Pål Halvorsen; Michael A. Riegler; Inga Strümke (CBMS, 2022)

Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework. - Cise Midoglu; Andrea M. Storås; Saeed Shafiee Sabet; Malek Hammou; Steven Alexander Hicks; Inga Strümke; Michael Alexander Riegler; Carsten Griwodz; Pål Halvorsen (ISM, 2022)

Huldra: a framework for collecting crowdsourced feedback on multimedia assets. - Malek Hammou; Cise Midoglu; Steven Alexander Hicks; Andrea M. Storås; Saeed Shafiee Sabet; Inga Strümke; Michael A. Riegler; Pål Halvorsen (MMSys, 2022)

Explainability methods for machine learning systems for multimodal medical datasets: research proposal. - Andrea M. Storås; Inga Strümke; Michael A. Riegler; Pål Halvorsen (MMSys, 2022)

The social dilemma in artificial intelligence development and why we have to solve it. - Inga Strümke; Marija Slavkovik 001; Vince Istvan Madai (AI Ethics, 2022)

Socioeconomic disparities and COVID-19: the causal connections. - Tannista Banerjee; Ayan Paul; Vishak Srikanth; Inga Strümke (CoRR, 2022)

Explaining a Deep Reinforcement Learning Docking Agent Using Linear Model Trees with User Adapted Visualization. - Vilde B. Gjærum; Inga Strümke; Ole Andreas Alsos; Anastasios M. Lekkas (CoRR, 2022)

Interpretable machine learning in Physics. - Christophe Grojean; Ayan Paul; Zhuoni Qian; Inga Strümke (CoRR, 2022)

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations. - Steven Hicks; Andrea M. Storås; Michael Riegler 001; Cise Midoglu; Malek Hammou; Thomas de Lange; Sravanthi Parasa; Pål Halvorsen; Inga Strümke (CoRR, 2022)

Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts. - Patrik Hammersborg; Inga Strümke (CoRR, 2022)

Real-Time Counterfactual Explanations For Robotic Systems With Multiple Continuous Outputs. - Vilde B. Gjærum; Inga Strümke; Anastasios M. Lekkas; Tim Miller 001 (CoRR, 2022)

Identifying Important Proteins in Meibomian Gland Dysfunction with Explainable Artificial Intelligence. - Andrea M. Storås; Morten Magnø; Fredrik A. Fineide; Bernd Thiede; Xiangjun Chen; Inga Strümke; Pål Halvorsen; Tor P. Utheim; Michael A. Riegler (CBMS, 2023)

Against Algorithmic Exploitation of Human Vulnerabilities. - Inga Strümke; Marija Slavkovik 001; Clemens Stachl (CoRR, 2023)

Concept backpropagation: An Explainable AI approach for visualising learned concepts in neural network models. - Patrik Hammersborg; Inga Strümke (CoRR, 2023)

Information based explanation methods for deep learning agents - with applications on large open-source chess models. - Patrik Hammersborg; Inga Strümke (CoRR, 2023)

Lecture Notes in Probabilistic Diffusion Models. - Inga Strümke; Helge Langseth (CoRR, 2023)

Model tree methods for explaining deep reinforcement learning agents in real-time robotic applications. - Vilde B. Gjærum; Inga Strümke; Jakob Løver; Timothy Miller 001; Anastasios M. Lekkas (Neurocomputing, 2023)

Inferring feature importance with uncertainties with application to large genotype data. - Pål Vegard Johnsen; Inga Strümke; Mette Langaas; Andrew Thomas DeWan; Signe Riemer-Sørensen (PLoS Comput. Biol., 2023)

AutoGCN-Toward Generic Human Activity Recognition With Neural Architecture Search. - Felix Tempel; Espen Alexander Fürst Ihlen; Inga Strümke (IEEE Access, 2024)

AutoGCN - Towards Generic Human Activity Recognition with Neural Architecture Search. - Felix Tempel; Inga Strümke; Espen Alexander Fürst Ihlen (CoRR, 2024)

Lightweight Neural Architecture Search for Cerebral Palsy Detection. - Felix Tempel; Espen A. F. Ihlen; Inga Strümke (CoRR, 2024)

A transformer-based deep reinforcement learning approach to spatial navigation in a partially observable Morris Water Maze. - Marte Eggen; Inga Strümke (CoRR, 2024)

Interplay between Federated Learning and Explainable Artificial Intelligence: a Scoping Review. - Luis M. Lopez-Ramos; Florian Leiser; Aditya Rastogi; Steven Hicks; Inga Strümke; Vince I. Madai; Tobias Budig; Ali Sunyaev; Adam Hilbert (CoRR, 2024)

Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition. - Felix Tempel; Daniel Groos; Espen Alexander Fürst Ihlen; Lars Adde; Inga Strümke (CoRR, 2024)

From Movements to Metrics: Evaluating Explainable AI Methods in Skeleton-Based Human Activity Recognition. - Kimji N. Pellano; Inga Strümke; Espen A. F. Ihlen (Sensors, 2024)

Clash of Requirements: Users First vs. Model First. - Tor Sporsem; Rasmus Ulfsnes; Morten Hatling; Inga Strümke (SIGSOFT FSE Companion, 2025)

Evaluating Explainable AI Methods in Deep Learning Models for Early Detection of Cerebral Palsy. - Kimji N. Pellano; Inga Strümke; Daniel Groos; Lars Adde; Espen A. F. Ihlen (IEEE Access, 2025)

Explaining Human Activity Recognition with SHAP: Validating insights with perturbation and quantitative measures. - Felix Tempel; Espen Alexander Fürst Ihlen; Lars Adde; Inga Strümke (Comput. Biol. Medicine, 2025)

Clinicians don't know what explanations they need: A case study on eliciting AI software explainability requirements. - Tor Sporsem; Stine Rasdal Finserås; Inga Strümke (CoRR, 2025)

Towards Biomarker Discovery for Early Cerebral Palsy Detection: Evaluating Explanations Through Kinematic Perturbations. - Kimji N. Pellano; Inga Strümke; Daniel Groos; Lars Adde; Pål Haugen; Espen Alexander Fürst Ihlen (CoRR, 2025)

Integrating attention into explanation frameworks for language and vision transformers. - Marte Eggen; Jacob Lysnæs-Larsen; Inga Strümke (CoRR, 2025)