XXAI: Towards eXplicitly eXplainable Artificial Intelligence
- URL: http://arxiv.org/abs/2401.03093v4
- Date: Sun, 19 May 2024 14:02:45 GMT
- Title: XXAI: Towards eXplicitly eXplainable Artificial Intelligence
- Authors: V. L. Kalmykov, L. V. Kalmykov,
- Abstract summary: There are concerns about the reliability and safety of artificial intelligence based on sub-symbolic neural networks.
symbolic AI has the nature of a white box and is able to ensure the reliability and safety of its decisions.
We propose eXplicitly eXplainable AI (XXAI) - a fully transparent white-box AI based on deterministic logical cellular automata.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: There are concerns about the reliability and safety of artificial intelligence (AI) based on sub-symbolic neural networks because its decisions cannot be explained explicitly. This is the black box problem of modern AI. At the same time, symbolic AI has the nature of a white box and is able to ensure the reliability and safety of its decisions. However, several problems prevent the widespread use of symbolic AI: the opacity of mathematical models and natural language terms, the lack of a unified ontology, and the combinatorial explosion of search capabilities. To solve the black-box problem of AI, we propose eXplicitly eXplainable AI (XXAI) - a fully transparent white-box AI based on deterministic logical cellular automata whose rules are derived from the first principles of the general theory of the relevant domain. In this case, the general theory of the domain plays the role of a knowledge base for deriving the inferences of the cellular automata. A cellular automaton implements parallel multi-level logical inference at all levels of organization - from local interactions of the element base to the system as a whole. Our verification of several ecological hypotheses sets a precedent for the successful implementation of the proposed solution. XXAI is able to automatically verify the reliability, security and ethics of sub-symbolic neural network solutions in both the final and training phases. In this article, we present precedents for the successful implementation of XXAI, the theoretical and methodological foundations for its further development, and discuss prospects for the future.
Related papers
- The Switch, the Ladder, and the Matrix: Models for Classifying AI Systems [0.0]
There still exists a gap between principles and practices in AI ethics.
One major obstacle organisations face when attempting to operationalise AI Ethics is the lack of a well-defined material scope.
arXiv Detail & Related papers (2024-07-07T12:16:01Z) - Artificial General Intelligence (AGI)-Native Wireless Systems: A Journey Beyond 6G [58.440115433585824]
Building future wireless systems that support services like digital twins (DTs) is challenging to achieve through advances to conventional technologies like meta-surfaces.
While artificial intelligence (AI)-native networks promise to overcome some limitations of wireless technologies, developments still rely on AI tools like neural networks.
This paper revisits the concept of AI-native wireless systems, equipping them with the common sense necessary to transform them into artificial general intelligence (AGI)-native systems.
arXiv Detail & Related papers (2024-04-29T04:51:05Z) - Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review [12.38351931894004]
We present the first systematic literature review of explainable methods for safe and trustworthy autonomous driving.
We identify five key contributions of XAI for safe and trustworthy AI in AD, which are interpretable design, interpretable surrogate models, interpretable monitoring, auxiliary explanations, and interpretable validation.
We propose a modular framework called SafeX to integrate these contributions, enabling explanation delivery to users while simultaneously ensuring the safety of AI models.
arXiv Detail & Related papers (2024-02-08T09:08:44Z) - Enabling High-Level Machine Reasoning with Cognitive Neuro-Symbolic
Systems [67.01132165581667]
We propose to enable high-level reasoning in AI systems by integrating cognitive architectures with external neuro-symbolic components.
We illustrate a hybrid framework centered on ACT-R and we discuss the role of generative models in recent and future applications.
arXiv Detail & Related papers (2023-11-13T21:20:17Z) - Trust, Accountability, and Autonomy in Knowledge Graph-based AI for
Self-determination [1.4305544869388402]
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making.
The integration of KGs with neuronal learning is currently a topic of active research.
This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination.
arXiv Detail & Related papers (2023-10-30T12:51:52Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - A User-Centred Framework for Explainable Artificial Intelligence in
Human-Robot Interaction [70.11080854486953]
We propose a user-centred framework for XAI that focuses on its social-interactive aspect.
The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
arXiv Detail & Related papers (2021-09-27T09:56:23Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Exploring the Nuances of Designing (with/for) Artificial Intelligence [0.0]
We explore the construct of infrastructure as a means to simultaneously address algorithmic and societal issues when designing AI.
Neither algorithmic solutions, nor purely humanistic ones will be enough to fully undesirable outcomes in the narrow state of AI.
arXiv Detail & Related papers (2020-10-22T20:34:35Z) - Opportunities and Challenges in Explainable Artificial Intelligence
(XAI): A Survey [2.7086321720578623]
Black-box nature of deep neural networks challenges its use in mission critical applications.
XAI promotes a set of tools, techniques, and algorithms that can generate high-quality interpretable, intuitive, human-understandable explanations of AI decisions.
arXiv Detail & Related papers (2020-06-16T02:58:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.