# Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety

arXiv: https://arxiv.org/abs/2206.13349 · HTML: https://mlsanity.com/paper/2206.13349/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 26 references that are in the map.
- First posted on arXiv 9 Jun 2022.

## Builds on (most cited first, 7 of 7)

- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [Process Knowledge-infused Learning for Suicidality Assessment on Social Media](https://mlsanity.com/paper/2204.12560/) (2022)

## Built on by (most cited first, 18 of 18)

- [Neurosymbolic AI -- Why, What, and How](https://mlsanity.com/paper/2305.00813/) (2023)
- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [Process Knowledge-infused Learning for Clinician-friendly Explanations](https://mlsanity.com/paper/2306.09824/) (2023)
- [Neuro-Symbolic AI for Military Applications](https://mlsanity.com/paper/2408.09224/) (2024)
- [KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts](https://mlsanity.com/paper/2210.04307/) (2022)
- [Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual Assistance for Telehealth: The Mental Health Case](https://mlsanity.com/paper/2304.00025/) (2023)
- [Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes](https://mlsanity.com/paper/2306.01805/) (2023)
- [Knowledge-Infused Self Attention Transformers](https://mlsanity.com/paper/2306.13501/) (2023)
- [IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations](https://mlsanity.com/paper/2306.13865/) (2023)
- [The Case for Developing a Foundation Model for Planning-like Tasks from Scratch](https://mlsanity.com/paper/2404.04540/) (2024)
- [Neurosymbolic AI for Enhancing Instructability in Generative AI](https://mlsanity.com/paper/2407.18722/) (2024)
- [Neurosymbolic Value-Inspired AI (Why, What, and How)](https://mlsanity.com/paper/2312.09928/) (2023)
- [Neurosymbolic Inference On Foundation Models For Remote Sensing Text-to-image Retrieval With Complex Queries](https://mlsanity.com/paper/2512.14102/) (2025)
- [Neurosymbolic Retrievers for Retrieval-augmented Generation](https://mlsanity.com/paper/2601.04568/) (2026)
- [RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding](https://mlsanity.com/paper/2312.09932/) (2023)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [NeuroLit Navigator: A Neurosymbolic Approach to Scholarly Article Searches for Systematic Reviews](https://mlsanity.com/paper/2503.00278/) (2025)
- [Bag of Tricks or Bag of Myths? Reducing Modeling Complexity with Task Knowledge in Explainable Suicide Risk Assessment](https://mlsanity.com/paper/2609.07766/) (2026)

## Abstract (arXiv)

AI systems have been widely adopted across various domains in the real world. However, in high-value, sensitive, or safety-critical applications such as self-management for personalized health or food recommendation with a specific purpose (e.g., allergy-aware recipe recommendations), their adoption is unlikely. Firstly, the AI system needs to follow guidelines or well-defined processes set by experts; the data alone will not be adequate. For example, to diagnose the severity of depression, mental healthcare providers use Patient Health Questionnaire (PHQ-9). So if an AI system were to be used for diagnosis, the medical guideline implied by the PHQ-9 needs to be used. Likewise, a nutritionist's knowledge and steps would need to be used for an AI system that guides a diabetic patient in developing a food plan. Second, the BlackBox nature typical of many current AI systems will not work; the user of an AI system will need to be able to give user-understandable explanations, explanations constructed using concepts that humans can understand and are familiar with. This is the key to eliciting confidence and trust in the AI system. For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.