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On Modifying a Neural Network's Perception

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Manuel de Sousa Ribeiro, João Leite

Artificial Intelligence · Submitted 5 Mar 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In this work, we propose a method which allows one to modify what an artificial neural network is perceiving regarding specific human-defined concepts, enabling the generation of hypothetical scenarios that could help understand and even debug the neural network model.

how it is stated · asserted 0.73 read the record →

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arXiv ID2303.02655 (version 1)
Pith IDSZA4B3UK, Pith's permanent record of this paper and its review
CitationJo\~ao Leite, Manuel de Sousa Ribeiro. "On Modifying a Neural Network's Perception." arXiv:2303.02655 (2023). https://pith.science/paper/SZA4B3UK
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,683 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.13 · human participants 0.06 · controlled comparison 0.09 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.10 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; .

Artificial neural networks have proven to be extremely useful models that have allowed for multiple recent breakthroughs in the field of Artificial Intelligence and many others. However, they are typically regarded as black boxes, given how difficult it is for humans to interpret how these models reach their results. In this work, we propose a method which allows one to modify what an artificial neural network is perceiving regarding specific human-defined concepts, enabling the generation of hypothetical scenarios that could help understand and even debug the neural network model. Through empirical evaluation, in a synthetic dataset and in the ImageNet dataset, we test the proposed method on different models, assessing whether the performed manipulations are well interpreted by the models, and analyzing how they react to them.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Artificial neural networks have proven to be extremely useful models that have allowed for multiple recent breakthroughs in the field of Artificial Intelligence and many others. background 1.00 0.05 0.02 asserted 1.00
s1 However, they are typically regarded as black boxes, given how difficult it is for humans to interpret how these models reach their results. background 0.74 0.05 0.02 asserted 0.98
s2 In this work, we propose a method which allows one to modify what an artificial neural network is perceiving regarding specific human-defined concepts, enabling the generation of hypothetical scenarios that could help understand and even debug the neural network model. contribution 0.98 0.79 0.29 asserted 0.73
s3 Through empirical evaluation, in a synthetic dataset and in the ImageNet dataset, we test the proposed method on different models, assessing whether the performed manipulations are well interpreted by the models, and analyzing how they react to them. method 0.92 0.96 0.11 asserted 0.94

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.93.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Artificial neural networks have proven to be extremely useful models that have allowed for multiple recent breakthroughs in the field of Artificial Intelligence and many others. However, they are typically regarded as black boxes, given how difficult it is for humans to interpret how these models reach their results. In this work, we propose a method which allows one to modify what an artificial neural network is perceiving regarding specific human-defined concepts, enabling the generation of hypothetical scenarios that could help understand and even debug the neural network model. Through empirical evaluation, in a synthetic dataset and in the ImageNet dataset, we test the proposed method on different models, assessing whether the performed manipulations are well interpreted by the models, and analyzing how they react to them.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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