explainability

Neuro-symbolic artificial intelligence

Justification not yet generated — showing supporting facts

• The paper 'Neuro-Symbolic AI: Explainability, Challenges, and Future Trends' identifies three significant challenges in neuro-symbolic AI: unified representations, explainability and transparency, and sufficient cooperation between neural networks and symbolic learning.
• Neuro-symbolic AI is a hybrid approach that combines the learning capabilities of neural networks with the reasoning and explainability of symbolic systems.
• The authors of 'Neuro-Symbolic AI: Explainability, Challenges, and Future Trends' argue that explainability in neuro-symbolic AI must be considered during the design phase rather than as an afterthought, as current models still do not meet the requirements for application in critical fields.
• Explainability requirements for Neuro-Symbolic AI consist of two components: process transparency and result transparency.
• The paper 'Neuro-Symbolic AI: Explainability, Challenges, and Future Trends' proposes a classification system for explainability in neuro-symbolic AI that evaluates both model design and behavior across 191 studies published in 2013.
• The CREST framework, introduced in the paper 'Building trustworthy NeuroSymbolic AI Systems: Consistency...', demonstrates how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods.
• The representation space in neuro-symbolic AI determines the technical foundation, the feasibility of achieving explainability and transparency, and the overall impact on ethics and society.
• Explainability in Neuro-Symbolic AI requires a relatively stable concept to be convincing.
• The authors of the paper 'Building Trustworthy NeuroSymbolic AI Systems' argue that NeuroSymbolic AI is better suited for creating trusted AI systems than statistical or symbolic AI methods used in isolation, because trust requires consistency, reliability, explainability, and safety.
• Zhang, X. and Sheng, V.S. authored a 2024 arXiv preprint (arXiv:2411.04383) that examines explainability, challenges, and future trends in neuro-symbolic AI.
• The authors of 'Neuro-Symbolic AI: Explainability, Challenges, and Future Trends' classify neuro-symbolic AI explainability into five categories: implicit intermediate representations and implicit prediction, partially explicit intermediate representations and partially explicit prediction, explicit intermediate representations or explicit prediction, explicit intermediate representation and explicit prediction, and unified representation and explicit prediction.

artificial neural networks

Justification not yet generated — showing supporting facts

• The paper 'Neuro-Symbolic AI: Explainability, Challenges, and Future Trends' identifies three significant challenges in neuro-symbolic AI: unified representations, explainability and transparency, and sufficient cooperation between neural networks and symbolic learning.
• Neuro-symbolic AI is a hybrid approach that combines the learning capabilities of neural networks with the reasoning and explainability of symbolic systems.
• A primary driver for the integration of neural and symbolic AI is the quest for explainability, as neural networks are often criticized as 'black boxes' with internal decision processes that are difficult to interpret and debug, whereas symbolic representations allow for explicit explanations and traceable decision paths.
• Utilizing a unified representation for neural networks and symbolic logic can improve explainability by creating semantic overlap between the two systems.
• The lack of explainability is a primary factor limiting the deployment of neural networks in critical domains.
• Explainability is a limiting factor for the application of neural networks in many vital fields.

artificial intelligence

Artificial intelligence is intrinsically linked to explainability as a core technical and ethical challenge, as evidenced by academic research [1], [2] and industry discourse [3], [4]. Furthermore, explainability is recognized as a critical priority for the future development and safety of AI systems [5], [6]. — 6 supporting facts

knowledge graphs

Knowledge graphs are inherently linked to explainability because they facilitate multi-hop reasoning [1] and provide transparent reasoning chains that map out relationship paths [2]. — 2 supporting facts

Large Language Models

Large Language Models are linked to explainability because their inherent probabilistic nature presents significant challenges for achieving transparency in knowledge graph reasoning, as noted in [1]. — 1 supporting fact

Knowledge Graph

Knowledge Graphs are identified as a core component of systems that require explainability to ensure trustworthiness and cognitive alignment, as established in [1]. — 1 supporting fact