@article{arzberger2026label,

title = {Label from Somewhere: Reflexive Annotating for Situated AI Alignment. },

author = {A. Arzberger and C. Offerman and U. Gadiraju and A. Bozzon and J. Yang},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {arXiv preprint arXiv:2601.17937},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{arzberger2026coconstructing,

title = {Co-Constructing Alignment: A Participatory Approach to Situate AI Values},

author = {A. Arzberger and E. Liscio and M. L. Lupetti and I. M. D. R. Troya and J. Yang},

year  = {2026},

date = {2026-01-01},

urldate = {2026-01-01},

journal = {arXiv preprint arXiv:2601.15895},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{tocchetti2025ai,

title = {A.I. Robustness: A Human-Centered Perspective on Technological Challenges and Opportunities},

author = {A. Tocchetti and L. Corti and A. Balayn and M. Yurrita and P. Lippmann and M. Brambilla and J. Yang},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {ACM Computing Surveys (CSUR)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bonatti2025towards,

title = {Towards Computer-Using Personal Agents},

author = {P. A. Bonatti and J. Domingue and A. L. Gentile and A. Harth and O. Hartig and A. Hogan and K. Hose and E. Jimenez-Ruiz and D. L. McGuinness and C. Sun and R. Verborgh},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {arXiv preprint arXiv:2503.15515},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{noben2025rule,

title = {Rule-augmented constraint learning for semantic error detection in MIMIC-III knowledge graph},

author = {Ö. E. Noben and Ö. D. Kılıç and T. Rienstra and M. Dumontier and R. Celebi},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {International Journal of Medical Informatics},

volume = {210},

pages = {106297},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sun2025generating,

title = {Generating unseen diseases patient data using ontology enhanced generative adversarial networks},

author = {C. Sun and M. Dumontier},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {npj Digital Medicine},

volume = {8},

number = {1},

pages = {4},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sun_revisiting_2024,

title = {Revisiting Bundle Recommendation for Intent-aware Product Bundling},

author = {Z. Sun and K. Feng and J. Yang and H. Fang and X. Qu and Y. S. Ong and W. Liu},

url = {https://dl.acm.org/doi/10.1145/3652865},

doi = {10.1145/3652865},

year  = {2024},

date = {2024-06-01},

urldate = {2024-06-01},

journal = {ACM Trans. Recomm. Syst.},

volume = {2},

number = {3},

pages = {24:1–24:34},

abstract = {Product bundling represents a prevalent marketing strategy in both offline stores and e-commerce systems. Despite its widespread use, previous studies on bundle recommendation face two significant limitations. Firstly, they rely on noisy datasets, where bundles are defined by heuristics, e.g., products co-purchased in the same session. Secondly, they target specific tasks by holding unrealistic assumptions, e.g., the availability of bundles for recommendation directly. This paper proposes to take a step back and considers the process of bundle recommendation from a holistic user experience perspective. We first construct high-quality bundle datasets with rich metadata, particularly bundle intents, through a carefully designed crowd-sourcing task. We then define a series of tasks that together, support all key steps in a typical bundle recommendation process, from bundle detection, completion and ranking, to explanation and auto-naming, whereby 19 research questions are raised correspondingly to guide the analysis. Finally, we conduct extensive experiments and analyses with representative recommendation models and large language models (LLMs), demonstrating the challenges and opportunities, especially with the emergence of LLMs. To summarize, our study contributes by introducing novel data sources, paving the way for new research avenues, and offering insights to guide product bundling in real e-commerce platforms.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{yang_fedtrans_2024,

title = {FedTrans: Client-Transparent Utility Estimation for Robust Federated Learning},

author = {M. Yang and R. Zhu and Q. Wang and J. Yang},

url = {https://proceedings.iclr.cc/paper_files/paper/2024/hash/bb309cc1fbdb88ea755bd7cee4b310ec-Abstract-Conference.html},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

journal = {International Conference on Representation Learning},

volume = {2024},

pages = {42668–42692},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{salimzadeh_when_2024,

title = {When in Doubt! Understanding the Role of Task Characteristics on Peer Decision-Making with AI Assistance: 32nd ACM Conference on User Modeling, Adaptation and Personalization},

author = {S. Salimzadeh and U. Gadiraju},

url = {http://www.scopus.com/inward/record.url?scp=85197883809&partnerID=8YFLogxK},

doi = {10.1145/3627043.3659567},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {UMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {89–101},

series = {UMAP 2024 - Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

abstract = {With the integration of AI systems into our daily lives, human-AI collaboration has become increasingly prevalent. Prior work in this realm has primarily explored the effectiveness and performance of individual human and AI systems in collaborative tasks. While much of decision-making occurs within human peers and groups in the real world, there is a limited understanding of how they collaborate with AI systems. One of the key predictors of human-AI collaboration is the characteristics of the task at hand. Understanding the influence of task characteristics on human-AI collaboration is crucial for enhancing team performance and developing effective strategies for collaboration. Addressing a research and empirical gap, we seek to explore how the features of a task impact decision-making within human-AI group settings. In a 2 × 2 between-subjects study (N = 256) we examine the effects of task complexity and uncertainty on group performance and behaviour. The participants were grouped into pairs and assigned to one of four experimental conditions characterized by varying degrees of complexity and uncertainty. We found that high task complexity and high task uncertainty can negatively impact the performance of human-AI groups, leading to decreased group accuracy and increased disagreement with the AI system. We found that higher task complexity led to a higher efficiency in decision-making, while a higher task uncertainty had a negative impact on efficiency. Our findings highlight the importance of considering task characteristics when designing human-AI collaborative systems, as well as the future design of empirical studies exploring human-AI collaboration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{he_opening_2024,

title = {Opening the Analogical Portal to Explainability: Can Analogies Help Laypeople in AI-assisted Decision Making?},

author = {G. He and A. Balayn and S. Buijsman and J. Yang and U. Gadiraju},

url = {http://www.scopus.com/inward/record.url?scp=85204874277&partnerID=8YFLogxK},

doi = {10.1613/jair.1.15118},

issn = {1076-9757},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Artificial Intelligence Research},

volume = {81},

pages = {117–162},

abstract = {Concepts are an important construct in semantics, based on which humans understand the world with various levels of abstraction. With the recent advances in explainable artificial intelligence (XAI), concept-level explanations are receiving an increasing amount of attention from the broad research community. However, laypeople may find such explanations difficult to digest due to the potential knowledge gap and the concomitant cognitive load. Inspired by prior work that has explored analogies and sensemaking, we argue that augmenting concept-level explanations with analogical inference information from commonsense knowledge can be a potential solution to tackle this issue. To investigate the validity of our proposition, we first designed an effective analogy-based explanation generation method and collected 600 analogy-based explanations from 100 crowd workers. Next, we proposed a set of structured dimensions for the qualitative assessment of such explanations, and conducted an empirical evaluation of the generated analogies with experts. Our findings revealed significant positive correlations between the qualitative dimensions of analogies and the perceived helpfulness of analogy-based explanations, suggesting the effectiveness of the dimensions. To understand the practical utility and the effectiveness of analogybased explanations in assisting human decision-making, we conducted a follow-up empirical study (N = 280) on a skin cancer detection task with non-expert humans and an imperfect AI system. Thus, we designed a between-subjects study spanning five different experimental conditions with varying types of explanations. The results of our study confirmed that a knowledge gap can prevent participants from understanding concept-level explanations. Consequently, when only the target domain of our designed analogy-based explanation was provided (in a specific experimental condition), participants demonstrated relatively more appropriate reliance on the AI system. In contrast to our expectations, we found that analogies were not effective in fostering appropriate reliance. We carried out a qualitative analysis of the open-ended responses from participants in the study regarding their perceived usefulness of explanations and analogies. Our findings suggest that human intuition and the perceived plausibility of analogies may have played a role in affecting user reliance on the AI system. We also found that the understanding of commonsense explanations varied with the varying experience of the recipient user, which points out the need for further work on personalization when leveraging commonsense explanations. In summary, although we did not find quantitative support for our hypotheses around the benefits of using analogies, we found considerable qualitative evidence suggesting the potential of high-quality analogies in aiding non-expert users in their decision making with AI-assistance. These insights can inform the design of future methods for the generation and use of effective analogy-based explanations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{loesch2024automated,

title = {Automated identification of healthier food substitutions through a combination of graph neural networks and nutri-scores},

author = {J. Loesch and I. Lier and A. Boer and J. Scholtes and M. Dumontier and R. Celebi},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Journal of Food Composition and Analysis},

volume = {125},

pages = {105829},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{sun2023generating,

title = {Generating synthetic personal health data using conditional generative adversarial networks combining with differential privacy},

author = {C. Sun and J. Soest and M. Dumontier},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Journal of Biomedical Informatics},

volume = {143},

pages = {104404},

keywords = {},

pubstate = {published},

tppubtype = {article}

}