# Enriching Artificial Intelligence Explanations with Knowledge Fragments

arXiv: https://arxiv.org/abs/2204.05579 · HTML: https://mlsanity.com/paper/2204.05579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 101 references that are in the map.
- First posted on arXiv 12 Apr 2022.

## Builds on (most cited first, 28 of 28)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [European Union regulations on algorithmic decision-making and a "right to explanation"](https://mlsanity.com/paper/1606.08813/) (2016)
- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [Local Rule-Based Explanations of Black Box Decision Systems](https://mlsanity.com/paper/1805.10820/) (2018)
- [Towards Explainable Artificial Intelligence](https://mlsanity.com/paper/1909.12072/) (2019)
- [If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques](https://mlsanity.com/paper/2103.01035/) (2021)
- [Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI)](https://mlsanity.com/paper/2005.13997/) (2020)
- [On quantitative aspects of model interpretability](https://mlsanity.com/paper/2007.07584/) (2020)
- [DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems](https://mlsanity.com/paper/1906.10263/) (2019)
- [Interpretable & Explorable Approximations of Black Box Models](https://mlsanity.com/paper/1707.01154/) (2017)
- [Human-in-the-Loop Interpretability Prior](https://mlsanity.com/paper/1805.11571/) (2018)
- [Contrastive Explanations with Local Foil Trees](https://mlsanity.com/paper/1806.07470/) (2018)
- [To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods](https://mlsanity.com/paper/2106.00461/) (2021)
- [Enriching Visual with Verbal Explanations for Relational Concepts -- Combining LIME with Aleph](https://mlsanity.com/paper/1910.01837/) (2019)
- [Crowdsourcing Question-Answer Meaning Representations](https://mlsanity.com/paper/1711.05885/) (2017)
- [Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy](https://mlsanity.com/paper/2008.12735/) (2020)
- [An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability](https://mlsanity.com/paper/2109.05327/) (2021)
- [Roadmap of Designing Cognitive Metrics for Explainable Artificial Intelligence (XAI)](https://mlsanity.com/paper/2108.01737/) (2021)
- [Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective](https://mlsanity.com/paper/2001.09464/) (2020)
- [Representation Learning and Pairwise Ranking for Implicit Feedback in Recommendation Systems](https://mlsanity.com/paper/1705.00105/) (2017)
- [Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications](https://mlsanity.com/paper/2203.10794/) (2022)
- [Open the Black Box Data-Driven Explanation of Black Box Decision Systems](https://mlsanity.com/paper/1806.09936/) (2018)
- [Actionable Cognitive Twins for Decision Making in Manufacturing](https://mlsanity.com/paper/2103.12854/) (2021)
- [XAI-KG: knowledge graph to support XAI and decision-making in manufacturing](https://mlsanity.com/paper/2105.01929/) (2021)
- [Reframing demand forecasting: a two-fold approach for lumpy and intermittent demand](https://mlsanity.com/paper/2103.13812/) (2021)
- [Active Learning for Automated Visual Inspection of Manufactured Products](https://mlsanity.com/paper/2109.02469/) (2021)

## Built on by (most cited first, 1 of 1)

- [Explainable automatic industrial carbon footprint estimation from bank transaction classification using natural language processing](https://mlsanity.com/paper/2405.14505/) (2024)

## Abstract (arXiv)

Artificial Intelligence models are increasingly used in manufacturing to inform decision-making. Responsible decision-making requires accurate forecasts and an understanding of the models' behavior. Furthermore, the insights into models' rationale can be enriched with domain knowledge. This research builds explanations considering feature rankings for a particular forecast, enriching them with media news entries, datasets' metadata, and entries from the Google Knowledge Graph. We compare two approaches (embeddings-based and semantic-based) on a real-world use case regarding demand forecasting.