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Reinforced Path Reasoning for Counterfactual Explainable Recommendation
[article]
2022
arXiv
pre-print
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In this work, we propose a novel Counterfactual Explainable Recommendation (CERec) to generate item attribute-based counterfactual explanations meanwhile to boost recommendation performance. ...
Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. ...
Explainable Recommendation Explainable recommendation is proven to improve user satisfaction [23] and system transparency [24] . ...
arXiv:2207.06674v1
fatcat:cypcpzhxfrfbpaadfevc64un7u
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
[article]
2021
arXiv
pre-print
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Each strategy explains its ranking decisions via different mechanisms: attention weights, adversarial perturbations, and counterfactual perturbations. ...
In this paper, we investigate the dilemma between recommendation and explainability, and show that by utilizing the contextual features (e.g., item reviews from users), we can design a series of explainable ...
recommendation (CER) [36] : The latest counterfactual explainable recommendation model. ...
arXiv:2110.14844v1
fatcat:e3s7nbxivzhknidbckjek2hx2e
On the Relationship between Counterfactual Explainer and Recommender: A Framework and Preliminary Observations
[article]
2022
arXiv
pre-print
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We analyze the relationship between the performance of the recommender and the quality of the explainer. ...
With this framework, we are able to investigate the relationship between the explainers and recommenders. ...
Therefore, the recommendation 𝑟𝑒𝑐 * can be counterfactually explained by 𝑟𝑒𝑐 * and I * 𝑢 . ...
arXiv:2207.04317v1
fatcat:25q42lwugfadxo42uinzmeihn4
Counterfactual Explainable Recommendation
[article]
2021
pre-print
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In this paper, we propose Counterfactual Explainable Recommendation (CountER), which takes the insights of counterfactual reasoning from causal inference for explainable recommendation. ...
recommendation decision on the counterfactual item is reversed. ...
Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors. ...
doi:10.1145/3459637.3482420
arXiv:2108.10539v1
fatcat:bdohmlnkdzacvmyecxbzqkvs4e
FIND:Explainable Framework for Meta-learning
[article]
2022
arXiv
pre-print
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This paper proposes FIND, an interpretable meta-learning framework that not only can explain the recommendation results of meta-learning algorithm selection, but also provide a more complete and accurate ...
Since the traditional meta-learning technique lacks explainability, as well as shortcomings in terms of transparency and fairness, achieving explainability for meta-learning is crucial. ...
On the other hand, recommendation explainability is the explainability of the leaning algorithm recommended by meta-learning. ...
arXiv:2205.10362v2
fatcat:2mo4kyd3onap5av4qyugz4f2ru
Interpretable Credit Application Predictions With Counterfactual Explanations
[article]
2018
arXiv
pre-print
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We predict credit applications with off-the-shelf, interchangeable black-box classifiers and we explain single predictions with counterfactual explanations. ...
Our contribution is two-fold: i) we propose positive counterfactuals, i.e. we adapt counterfactual explanations to also explain accepted loan applications, and ii) we propose two weighting strategies to ...
Recommending to change these types of features would be of little use. ...
arXiv:1811.05245v2
fatcat:rnvgsdfdlvb4reetsqwifdjhli
On Counterfactual Explanations under Predictive Multiplicity
[article]
2020
arXiv
pre-print
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In summary, our theoretical and empiricaln results challenge the commonly held view that counterfactual recommendations should be sparse in general. ...
In this work, we derive a general upper bound for the costs of counterfactual explanations under predictive multiplicity. ...
: Explaining the predictions of
any classifier. In SIGKDD. ACM, 2016. ...
arXiv:2006.13132v1
fatcat:nkqjenm4lrdlnegecwlmuay5pi
A General Model for Fair and Explainable Recommendation in the Loan Domain (Short paper)
2021
ACM Conference on Recommender Systems
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To this end, in this paper, we propose a model for generating natural language and counterfactual explanations for a loan recommender system with the aim of providing fairer and more transparent suggestions ...
Recommender systems have been widely used in the Financial Services domain and can play a crucial role in personal loan comparison platforms. ...
Figure 1 represents our proposed workflow for generating an explanation and a counterfactual explanation in order to recommend also corrective actions to the user. ...
dblp:conf/recsys/CornacchiaNR21
fatcat:lbgytk2dazd23lc5ttupreb4oi
Counterfactual Explanations for Machine Learning: A Review
[article]
2020
arXiv
pre-print
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We also identify gaps and discuss promising research directions in the space of counterfactual explainability. ...
Modern approaches to counterfactual explainability in machine learning draw connections to the established legal doctrine in many countries, making them appealing to fielded systems in high-impact areas ...
Therefore the recommended counterfactuals should be diverse, giving applicants the choice to choose the easiest one. ...
arXiv:2010.10596v1
fatcat:bhw56sorfbfrhm7y473wlizt24
Causal Collaborative Filtering
[article]
2021
arXiv
pre-print
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Finally, we further propose a general counterfactual constrained learning framework for estimating the user-item preferences. ...
Recommender systems are important and valuable tools for many personalized services. ...
More details will be explained in Section 5. ...
arXiv:2102.01868v4
fatcat:ly4jyi4v6na25m3hgwn264d5wu
"GAN I hire you?" – A System for Personalized Virtual Job Interview Training
[article]
2022
arXiv
pre-print
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The new feedback extension employs an eXplainable AI (XAI) method based on counterfactual reasoning for generating verbal feedback about observed social behavior. ...
There, we make use of counterfactual explanations, explaining to a user that a modified version of her/his social behavior would have led to a better behavior rating. ...
arXiv:2206.03869v1
fatcat:qprb3vrscnelnf4mbbtxxgoubu
Explanation Ontology in Action: A Clinical Use-Case
[article]
2020
arXiv
pre-print
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Such a representation is increasingly necessary as explainability has become an important problem in Artificial Intelligence with the emergence of complex methods and an uptake in high-precision and user-facing ...
Additionally, there have been some promising machine learning (ML) model efforts in the explainability space [1, 6] that could be used to generate system recommendations to populate specific explanation ...
With the alternate set of inputs and the corresponding recommendation in place, the counterfactual explanation components can now be populated as slots based on the sufficiency condition for this explanation ...
arXiv:2010.01478v1
fatcat:a7mqolaoejehja3i67fltvpnmm
Counterfactual Explanations as Interventions in Latent Space
[article]
2021
arXiv
pre-print
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from the data, and at the same time to provide feasible recommendations to reach the proposed profile. ...
Explainable Artificial Intelligence (XAI) is a set of techniques that allows the understanding of both technical and non-technical aspects of Artificial Intelligence (AI) systems. ...
generator of explanations; providing, besides counterfactual explanations, causal-aware recommendations for algorithmic recourse. ...
arXiv:2106.07754v2
fatcat:vgab6kv2qjhrrgeeiwzq47dulq
"If it didn't happen, why would I change my decision?": How Judges Respond to Counterfactual Explanations for the Public Safety Assessment
[article]
2022
arXiv
pre-print
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We ran think-aloud trials with eight sitting US state court judges, providing them with recommendations from the PRAI as well as CFEs. ...
At first, judges misinterpreted the counterfactuals as real -- rather than hypothetical -- changes to defendants. ...
Indeed, the judges felt more comfortable asking questions, and when asked to explain the counterfactuals to us, nearly all of them did so well. ...
arXiv:2205.05424v2
fatcat:awajsm5eqrh2pogzttr27k3vpm
Model agnostic generation of counterfactual explanations for molecules
2022
Chemical Science
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Generating model agnostic molecular counterfactual explanations to explain model predictions. ...
are recommended (3) the basic alphabet with only B, C, N, O, S, F, Cl, Br, I atoms is recommended. ...
These counterfactuals can be used to explain what functional groups are most important for solubility of the base molecule. ...
doi:10.1039/d1sc05259d
pmid:35432902
pmcid:PMC8966631
fatcat:6enpcykw3zfajan3xmqwfwbjf4
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