6 research outputs found
T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems
Machine learning (ML) based systems have been suffering a lack of
interpretability. To address this problem, counterfactual explanations (CEs)
have been proposed. CEs are unique as they provide workable suggestions to
users, in addition to explaining why a certain outcome was predicted. However,
the application of CEs has been hindered by two main challenges, namely general
user preferences and variable ML systems. User preferences, in particular, tend
to be general rather than specific feature values. Additionally, CEs need to be
customized to suit the variability of ML models, while also maintaining
robustness even when these validation models change. To overcome these
challenges, we propose several possible general user preferences that have been
validated by user research and map them to the properties of CEs. We also
introduce a new method called \uline{T}ree-based \uline{C}onditions
\uline{O}ptional \uline{L}inks (T-COL), which has two optional structures and
several groups of conditions for generating CEs that can be adapted to general
user preferences. Meanwhile, a group of conditions lead T-COL to generate more
robust CEs that have higher validity when the ML model is replaced. We compared
the properties of CEs generated by T-COL experimentally under different user
preferences and demonstrated that T-COL is better suited for accommodating user
preferences and variable ML systems compared to baseline methods including
Large Language Models
Benchmarking and survey of explanation methods for black box models
The rise of sophisticated black-box machine learning models in Artificial Intelligence systems has prompted the need for explanation methods that reveal how these models work in an understandable way to users and decision makers. Unsurprisingly, the state-of-the-art exhibits currently a plethora of explainers providing many different types of explanations. With the aim of providing a compass for researchers and practitioners, this paper proposes a categorization of explanation methods from the perspective of the type of explanation they return, also considering the different input data formats. The paper accounts for the most representative explainers to date, also discussing similarities and discrepancies of returned explanations through their visual appearance. A companion website to the paper is provided as a continuous update to new explainers as they appear. Moreover, a subset of the most robust and widely adopted explainers, are benchmarked with respect to a repertoire of quantitative metrics
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation
In recent years, Graph Neural Networks have reported outstanding performance
in tasks like community detection, molecule classification and link prediction.
However, the black-box nature of these models prevents their application in
domains like health and finance, where understanding the models' decisions is
essential. Counterfactual Explanations (CE) provide these understandings
through examples. Moreover, the literature on CE is flourishing with novel
explanation methods which are tailored to graph learning.
In this survey, we analyse the existing Graph Counterfactual Explanation
methods, by providing the reader with an organisation of the literature
according to a uniform formal notation for definitions, datasets, and metrics,
thus, simplifying potential comparisons w.r.t to the method advantages and
disadvantages. We discussed seven methods and sixteen synthetic and real
datasets providing details on the possible generation strategies. We highlight
the most common evaluation strategies and formalise nine of the metrics used in
the literature. We first introduce the evaluation framework GRETEL and how it
is possible to extend and use it while providing a further dimension of
comparison encompassing reproducibility aspects. Finally, we provide a
discussion on how counterfactual explanation interplays with privacy and
fairness, before delving into open challenges and future works.Comment: arXiv admin note: text overlap with arXiv:2107.04086 by other author
On the computation of counterfactual explanations -- A survey
Artelt A, Hammer B. On the computation of counterfactual explanations -- A survey. 2019