mlsanityData to 7 Oct 2026

LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information

arXiv 2104.06057 ↗First posted 13 Apr 2021cs.LG cs.AIcited by 3

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Abstract (arXiv)

Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect. AI-powered systems are monitoring and deciding about sensitive economic and societal issues. The future is towards automation, and it must not be prevented. However, this is a conflicting viewpoint for a lot of people, due to the fear of uncontrollable AI systems. This concern could be reasonable if it was originating from considerations associated with social issues, like gender-biased, or obscure decision-making systems. Explainable AI (XAI) is recently treated as a huge step towards reliable systems, enhancing the trust of people to AI. Interpretable machine learning (IML), a subfield of XAI, is also an urgent topic of research. This paper presents a small but significant contribution to the IML community, focusing on a local-based, neural-specific interpretation process applied to textual and time-series data. The proposed methodology introduces new approaches to the presentation of feature importance based interpretations, as well as the production of counterfactual words on textual datasets. Eventually, an improved evaluation metric is introduced for the assessment of interpretation techniques, which supports an extensive set of qualitative and quantitative experiments.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 32 of its 84 references that are in the map. First posted on arXiv 13 Apr 2021.

Builds on

32 of 84 references.

References · 32

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20 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2022An Attention Matrix for Every Decision: Faithfulness-based Arbitration Among Multiple Attention-Based Interpretations of Transformers in Text Classificationcited by 6
  2. 2024Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligencecited by 1
  3. 2024Interpretable classification of wiki-review streamscited by 1