# Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

arXiv: https://arxiv.org/abs/2310.19503 · HTML: https://mlsanity.com/paper/2310.19503/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 119 references that are in the map.
- First posted on arXiv 30 Oct 2023.

## Builds on (most cited first, 30 of 31)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Towards Reasoning in Large Language Models: A Survey](https://mlsanity.com/paper/2212.10403/) (2022)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Towards Expert-Level Medical Question Answering with Large Language Models](https://mlsanity.com/paper/2305.09617/) (2023)
- [Causal Reasoning and Large Language Models: Opening a New Frontier for Causality](https://mlsanity.com/paper/2305.00050/) (2023)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Reasoning with Language Model Prompting: A Survey](https://mlsanity.com/paper/2212.09597/) (2022)
- [Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?](https://mlsanity.com/paper/2308.10168/) (2023)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Auditing large language models: a three-layered approach](https://mlsanity.com/paper/2302.08500/) (2023)
- [Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective](https://mlsanity.com/paper/2112.06409/) (2021)
- [Thinking Fast and Slow in AI](https://mlsanity.com/paper/2010.06002/) (2020)
- [Clingo = ASP + Control: Preliminary Report](https://mlsanity.com/paper/1405.3694/) (2014)
- [Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning](https://mlsanity.com/paper/2202.05338/) (2022)
- [Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use cases](https://mlsanity.com/paper/2102.11965/) (2021)
- [Realistic Re-evaluation of Knowledge Graph Completion Methods: An Experimental Study](https://mlsanity.com/paper/2003.08001/) (2020)
- [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021)
- [Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles](https://mlsanity.com/paper/2110.10457/) (2021)
- [Explanation Ontology: A Model of Explanations for User-Centered AI](https://mlsanity.com/paper/2010.01479/) (2020)
- [DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection](https://mlsanity.com/paper/2107.10648/) (2021)
- [Generations of Knowledge Graphs: The Crazy Ideas and the Business Impact](https://mlsanity.com/paper/2308.14217/) (2023)
- [Personal Knowledge Graphs: Use Cases in e-learning Platforms](https://mlsanity.com/paper/2203.08507/) (2022)
- [From Technology to Society: An Overview of Blockchain-based DAO](https://mlsanity.com/paper/2011.14940/) (2020)
- [Self-sovereign Identity - Opportunities and Challenges for the Digital Revolution](https://mlsanity.com/paper/1712.01767/) (2017)
- [The SPECIAL-K Personal Data Processing Transparency and Compliance Platform](https://mlsanity.com/paper/2001.09461/) (2020)
- [Intelligent Software Web Agents: A Gap Analysis](https://mlsanity.com/paper/2102.06607/) (2021)
- [BlockIoT: Blockchain-based Health Data Integration using IoT Devices](https://mlsanity.com/paper/2110.10123/) (2021)

## Built on by (most cited first, 2 of 2)

- [Federated Personal Knowledge Graph Completion with Lightweight Large Language Models for Personalized Recommendations](https://mlsanity.com/paper/2603.13264/) (2026)
- [Matching Game Preferences Through Dialogical Large Language Models: A Perspective](https://mlsanity.com/paper/2507.20000/) (2025)

## Abstract (arXiv)

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives.