Leveraging Knowledge Graphs for Empirical Concept Retrieval in Explainable AI

Core Concepts

Knowledge graphs can be leveraged to generate data-driven concepts for concept-based explainable AI, providing robust and well-aligned explanations.

Abstract

The paper introduces an interactive workflow for defining concepts based on knowledge graphs, such as WordNet, Wikidata, and ConceptNet. This allows for comprehensive and personalized concept definition, overcoming the challenge of establishing relevant concept datasets.
The key highlights and insights are:

Knowledge graphs can be used to generate data-driven concepts for concept-based explainable AI methods like Concept Activation Vectors (CAVs) and Concept Activation Regions (CARs). This provides a way to define relevant concepts beyond the limitations of existing datasets.

Concepts derived from knowledge graphs lead to robust and accurate CAVs and CARs, with performance comparable or even better than using labeled datasets like Pascal VOC. The explanations are also found to be stable to variations in the negative set.

The internal representations of the models are well-aligned with the structure of the human-curated knowledge graphs, indicating a strong correspondence between machine and human conceptual representations. This supports the relevance of knowledge graph-based concepts for explainable AI.

The interactive nature of the concept definition process allows for personalization and ensures the concepts reflect the user's intentions, addressing a key challenge in concept-based explainability.

Overall, the paper demonstrates the great potential of leveraging knowledge graphs to define empirical concepts and generate robust and well-aligned explanations in the context of explainable AI.

Stats

Wikimedia Commons provides more images per concept class compared to the labeled Pascal VOC dataset.
The text dataset from Wikipedia contains over 500,000 sentences for the "sport" concept and its sub-concepts.
The "fruit" concept and its sub-concepts have over 2,000 sentences in the text dataset.
The "motor vehicle" concept and its sub-concepts have over 57,000 sentences in the text dataset.

Quotes

"Knowledge graphs can help to define and delineate concepts, and, as we will show, help to retrieve data for alignment of concepts and machine representations."
"Importantly, we also find good alignment between the models' representations of concepts and the structure of knowledge graphs, i.e., human representations. This supports our conclusion that knowledge graph-based concepts are relevant for XAI."

Key Insights Distilled From

Knowledge graphs for empirical concept retrieval

by Lenk... at arxiv.org 04-11-2024

https://arxiv.org/pdf/2404.07008.pdf

Deeper Inquiries

How can the interactive concept definition process be further improved to better capture the user's evolving understanding and needs?

The interactive concept definition process can be enhanced by incorporating feedback loops that allow users to provide continuous input and refine the defined concepts over time. This could involve mechanisms for users to rate the relevance and accuracy of the retrieved examples, suggest additional sub-concepts, or provide their own examples to further personalize the concept dataset. By enabling users to actively participate in the refinement of concepts, the system can better adapt to their evolving understanding and needs.
Additionally, implementing natural language processing capabilities could facilitate more intuitive interactions with the system. Users could describe concepts in their own words, and the system could leverage semantic analysis to map these descriptions to relevant concepts in the knowledge graph. This would make the process more user-friendly and align more closely with how individuals naturally think about and express concepts.

What are the potential limitations or biases introduced by the knowledge graphs used, and how can they be mitigated?

One potential limitation of using knowledge graphs is the inherent biases present in the data they are constructed from. Knowledge graphs are often curated by individuals or generated from existing sources, which can introduce biases related to cultural perspectives, language usage, or domain-specific knowledge. These biases can impact the quality and diversity of the concepts retrieved from the knowledge graph.
To mitigate these biases, it is essential to implement robust validation and filtering mechanisms during the concept retrieval process. This could involve incorporating diversity metrics to ensure a broad representation of concepts, conducting regular audits to identify and address biases, and leveraging multiple knowledge graphs to cross-validate concept definitions. Additionally, integrating fairness and transparency considerations into the concept retrieval algorithms can help mitigate biases and promote more inclusive and accurate concept definitions.

How can the insights from this work be extended to other domains beyond vision and text, such as audio or multimodal data?

The insights from this work can be extended to other domains beyond vision and text by adapting the concept-based explainability framework to accommodate the unique characteristics of audio or multimodal data. For audio data, concepts could be defined based on acoustic features, speech patterns, or semantic content extracted from audio signals. Knowledge graphs specific to audio domains could be leveraged to define and retrieve relevant concepts for explainability.
In the case of multimodal data, where information is derived from multiple modalities such as text, images, and audio, a fusion approach could be employed to integrate concept definitions from different modalities. This would involve creating a unified concept dataset that captures the relationships and interactions between concepts across modalities. By extending the concept-based explainability framework to audio and multimodal data, a more comprehensive and interpretable understanding of complex models can be achieved across diverse data types.