Going beyond xai: A systematic survey for explanation-guided learning

Y Gao, S Gu, J Jiang, SR Hong, D Yu, L Zhao - ACM Computing Surveys, 2024 - dl.acm.org
As the societal impact of Deep Neural Networks (DNNs) grows, the goals for advancing
DNNs become more complex and diverse, ranging from improving a conventional model …

Modern views of machine learning for precision psychiatry

ZS Chen, IR Galatzer-Levy, B Bigio, C Nasca, Y Zhang - Patterns, 2022 - cell.com
In light of the National Institute of Mental Health (NIMH)'s Research Domain Criteria (RDoC),
the advent of functional neuroimaging, novel technologies and methods provide new …

Leveraging explanations in interactive machine learning: An overview

S Teso, Ö Alkan, W Stammer, E Daly - Frontiers in Artificial …, 2023 - frontiersin.org
Explanations have gained an increasing level of interest in the AI and Machine Learning
(ML) communities in order to improve model transparency and allow users to form a mental …

Building trust in interactive machine learning via user contributed interpretable rules

L Guo, EM Daly, O Alkan, M Mattetti, O Cornec… - Proceedings of the 27th …, 2022 - dl.acm.org
Machine learning technologies are increasingly being applied in many different domains in
the real world. As autonomous machines and black-box algorithms begin making decisions …

AIMEE: An Exploratory Study of How Rules Support AI Developers to Explain and Edit Models

D Piorkowski, I Vejsbjerg, O Cornec, EM Daly… - Proceedings of the …, 2023 - dl.acm.org
In real-world applications when deploying Machine Learning (ML) models, initial model
development includes close analysis of the model results and behavior by a data scientist …

FROTE: feedback rule-driven oversampling for editing models

O Alkan, D Wei, M Mattetti, R Nair… - … of Machine Learning …, 2022 - proceedings.mlsys.org
Abstract Machine learning (ML) models may involve decision boundaries that change over
time due to updates to rules and regulations, such as in loan approvals or claims …

End User Authoring of Personalized Content Classifiers: Comparing Example Labeling, Rule Writing, and LLM Prompting

L Wang, K Yurechko, P Dani, QZ Chen… - arXiv preprint arXiv …, 2024 - arxiv.org
Existing tools for laypeople to create personal classifiers often assume a motivated user
working uninterrupted in a single, lengthy session. However, users tend to engage with …

Interactive model refinement in relational domains with inductive logic programming

O Deane, O Ray - Companion Proceedings of the 28th International …, 2023 - dl.acm.org
This paper presents an interactive system for exploring and editing logic-based machine
learning models specialised for the relational reasoning problem domain. Prior work has …

Co-creating a globally interpretable model with human input

R Nair - arXiv preprint arXiv:2306.13381, 2023 - arxiv.org
We consider an aggregated human-AI collaboration aimed at generating a joint
interpretable model. The model takes the form of Boolean decision rules, where human …

Understanding the Role of Interactivity and Explanation in Adaptive Experiences

L Guo - 2023 - search.proquest.com
Adaptive experiences have been an active area of research in the past few decades,
accompanied by advances in technology such as machine learning and artificial …