Human-Centric Justification of Machine Learning Predictions
Open accessAbstract
Human decision makers in many domains can make use of predictions made by machine learning models in their decision making process, but the usability of these predictions is limited if the human is unable to justify his or her trust in the prediction. We propose a novel approach to producing justifications that is geared towards users without machine learning expertise, focusing on domain knowledge and on human reasoning, and utilizing natural language generation. Through a task-based experiment, we show that our approach significantly helps humans to correctly decide whether or not predictions are accurate, and significantly increases their satisfaction with the justification.
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A series of works [13, 15, 16, 55, 61, 69] refer to justifications as ways of ensuring that a decision is good (in contrast to understanding a decision), which is in line with the definition proposed in this paper.
“However, a series of works [13, 15, 16, 55, 61, 69], refer to justifications as ways of ensuring that a decision is good (in contrast to understanding a decision), which is in line with the definition proposed in this paper.”
From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [15]
A series of works [13, 15, 16, 55, 61, 69] refer to justifications as ways of ensuring that a decision is good (in contrast to understanding a decision), which is in line with the definition proposed in this paper.
“However, a series of works [13, 15, 16, 55, 61, 69], refer to justifications as ways of ensuring that a decision is good (in contrast to understanding a decision), which is in line with the definition proposed in this paper.”
From Beyond explainability: justifiability and contestability of algorithmic decision systems (2022) · cites this paper as [16]
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Chapter 8: Explainable AI and Taxation: A Real-Life Application
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Combining Deductive and Statistical Explanations in the FRANK Query Answering System
Bundy, A., & Nuamah, K. (2021) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/ickg52313.2021.00010 · Cited by 1 · see citing works
Interpreting Complex Regression ModelsPreprint
Avigdor-Elgrabli, N., Libov, A., Viderman, M., & Wolff, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.09225 · Cited by 1 · see citing works
Human-grounded Evaluations of Explanation Methods for Text ClassificationPreprint
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ICAIL Doctoral Consortium, Montreal 2019
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Towards a Framework for Challenging ML-Based Decisions
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CACTUS: Detecting and Resolving Conflicts in Objective FunctionsPreprint
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Towards a framework for challenging ML-based decisions
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Large Language Models as Financial Analysts: Sector-Aware Reasoning for Investment Decisions
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Analyzing and explaining data-driven Artificial Intelligence Models by argumentation
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- Authors
- Biran, O., & McKeown, K.
- Affiliations
- Columbia University (US)
- ORCID
- McKeown, Kathleen
- Venue
- Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
- Published
- 2017-08
- Pages
- 1461-1467
- OpenAlex
- W2741678970
- Cited by
- 84
- References
- 33
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 6 · 2023: 13 · 2022: 8 · 2021: 22 · 2020: 16 · 2019: 9 · 2018: 4
- FWCI
- 3.62
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Joint Conference on Artificial Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Keywords
- explainable machine learning, natural language generation, non-expert users, domain knowledge, human reasoning, decision-making, trust in AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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