Explaining machine learning models with interactive natural language conversations using TalkToModel
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Abstract
Practitioners increasingly use machine learning (ML) models, yet models have become more complex and harder to understand. To understand complex models, researchers have proposed techniques to explain model predictions. However, practitioners struggle to use explainability methods because they do not know which explanation to choose and how to interpret the explanation. Here we address the challenge of using explainability methods by proposing TalkToModel: an interactive dialogue system that explains ML models through natural language conversations. TalkToModel consists of three components: an adaptive dialogue engine that interprets natural language and generates meaningful responses; an execution component that constructs the explanations used in the conversation; and a conversational interface. In real-world evaluations, 73% of healthcare workers agreed they would use TalkToModel over existing systems for understanding a disease prediction model, and 85% of ML professionals agreed TalkToModel was easier to use, demonstrating that TalkToModel is highly effective for model explainability.
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The few studies that move beyond purely local explanations point to promising but underexplored directions.
“The few studies that move beyond purely local explanations [11, 74,80,81,104,116,125,129] point to promising but underexplored directions.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
Of the 69 reviewed studies, only three included any user-facing evaluation of explanation presentation.
“Of the 69 reviewed studies, only three [75,76,104] included any user-facing evaluation of explanation presentation.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
A minority of studies engaged clinicians to evaluate explanations, while most relied entirely on automated metrics or proxies.
“A minority of studies engaged clinicians to evaluate explanations [67,83,87,102,104], while most relied entirely on automated metrics or proxies.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
Only one study conducted a controlled user study comparing explanation formats.
“Only one [104] conducted a controlled user study comparing explanation formats.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [104]
Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513 · see citing works
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766 · see citing works
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · see citing works
Self-citationSentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N., & Gurevych, I. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1410 · Cited by 12,778 · see citing works
Decoupled Weight Decay Regularization
Loshchilov, I., & Hutter, F. (2017) · arXiv · doi:10.48550/arxiv.1711.05101 · Cited by 12,522 · see citing works
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · see citing works
From local explanations to global understanding with explainable AI for trees
Lundberg, S. M., Erion, G., Chen, H., DeGrave, A., Prutkin, J. M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., & Lee, S.-I. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-019-0138-9 · Cited by 10,551 · see citing works
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works
Self-citationDeep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370 · see citing works
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works
Prolific.ac—A subject pool for online experiments
Palan, S., & Schitter, C. (2018) · Journal of Behavioral and Experimental Finance · doi:10.1016/j.jbef.2017.12.004 · Cited by 3,450 · see citing works
Psychological Aspects of Natural Language Use: Our Words, Our Selves
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003) · Annual Review of Psychology · doi:10.1146/annurev.psych.54.101601.145041 · Cited by 2,624 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · see citing works
Anchors: High-Precision Model-Agnostic Explanations
Ribeiro, M. T., Singh, S., & Guestrin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11491 · Cited by 2,162 · cited 1× · see citing works
“These techniques either fit faithful models in the local region around a prediction or inspect internal model details, like gradients, to explain predictions [56, 66, 61, 67, 58, 29, 65, 68].”
MethodSelf-citationSmoothGrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works
Hakkarainen, J., Kuhlmann, G., Koene, E. F. M., Santaren, D., Meier, S., Krol, M., van Stratum, B., Ialongo, I., Chevallier, F., Tamminen, J., et al. (2016) · Pakistan Journal of Medical & Cardiological Review · doi:10.5281//zenodo · Cited by 1,296 · see citing works
Explaining machine learning classifiers through diverse counterfactual explanations
Mothilal, R. K., Sharma, A., & Tan, C. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372850 · Cited by 1,233 · see citing works
Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1425 · Cited by 1,034 · see citing works
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
Liao, Q. V., Gruen, D., & Miller, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376590 · Cited by 925 · see citing works
Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2022.emnlp-main.759 · Cited by 755 · cited 1× · see citing works
“This procedure includes examples of the input and target from the training prepended to the test instance [10, 45, 80].”
MethodRethinking the Role of Demonstrations: What Makes In-Context Learning Work?
Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv · doi:10.48550/arxiv.2202.12837 · Cited by 752 · see citing works
Model-Agnostic Interpretability of Machine Learning
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05386 · Cited by 699 · see citing works
Self-citationInterpretable Decision Sets: A Joint Framework for Description and Prediction
Lakkaraju, H., Bach, S. H., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939874 · Cited by 687 · see citing works
Self-citationA Survey on Dialogue Systems: Recent Advances and New Frontiers
Chen, H., Liu, X., Yin, D., & Tang, J. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3166054.3166058 · Cited by 579 · see citing works
Accurate intelligible models with pairwise interactions
Lou, Y., Caruana, R., Gehrke, J., & Hooker, G. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2487579 · Cited by 564 · see citing works
Kaur, H., Nori, H., Jenkins, S., Caruana, R., Wallach, H., & Wortman Vaughan, J. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376219 · Cited by 564 · see citing works
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv · doi:10.48550/arxiv.2102.09690 · Cited by 513 · see citing works
Self-citationThe What-If Tool: Interactive Probing of Machine Learning Models
Wexler, J., Pushkarna, M., Bolukbasi, T., WATTENBERG, M. P., Viégas, F. B., & Wilson, J. (2019) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2019.2934619 · Cited by 512 · see citing works
Providing a unified account of definite noun phrases in discourse
Grosz, B. J., Joshi, A. K., & Weinstein, S. (1983) · Annual Meeting of the Association for Computational Linguistics (ACL) · doi:10.3115/981311.981320 · Cited by 451 · see citing works
Supersparse linear integer models for optimized medical scoring systems
Ustun, B., & Rudin, C. D. (2015) · Machine Learning · doi:10.1007/s10994-015-5528-6 · Cited by 316 · see citing works
On the Robustness of Interpretability Methods
Alvarez-Melis, D., & Jaakkola, T. S. (2018) · arXiv · doi:10.48550/arxiv.1806.08049 · Cited by 310 · see citing works
On the (In)fidelity and Sensitivity for ExplanationsPreprint
Yeh, C., Hsieh, C.-Y., Suggala, A. S., Inouye, D. I., & Ravikumar, P. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.09392 · Cited by 291 · see citing works
The Disagreement Problem in Explainable Machine Learning: A Practitioner's PerspectivePreprint
Krishna, S., Han, T., Gu, A., Jabbari, S., Wu, Z. S., & Lakkaraju, H. (2023) · Research Square · doi:10.21203/rs.3.rs-2963888/v1 · Cited by 279 · see citing works
Self-citationFaithful and Customizable Explanations of Black Box Models
Lakkaraju, H., Kamar, E. S., Caruana, R., & Leskovec, J. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314229 · Cited by 261 · see citing works
Self-citationEnd-to-End Task-Completion Neural Dialogue SystemsPreprint
Li, X., Chen, Y.-N., Li, L. C., Gao, J., & Çelikyılmaz, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01008 · Cited by 259 · see citing works
The challenge of crafting intelligible intelligence
Weld, D. S., & Bansal, G. (2019) · Communications of the ACM · doi:10.1145/3282486 · Cited by 253 · cited 1× · see citing works
“Yet, recent work suggests practitioners often have difficulty using explainability techniques [35, 31, 79].”
BackgroundNeural Additive Models: Interpretable Machine Learning with Neural NetsPreprint
Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B. J., Caruana, R., & Hinton, G. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13912 · Cited by 246 · see citing works
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220 · see citing works
Toward establishing trust in adaptive agents
Glass, A., McGuinness, D. L., & Wolverton, M. J. (2008) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/1378773.1378804 · Cited by 217 · see citing works
Recent advances and challenges in task-oriented dialog systems
Zhang, Z., Takanobu, R., Zhu, Q., Huang, M., & Zhu, X. (2020) · Science China Technological Sciences · doi:10.1007/s11431-020-1692-3 · Cited by 201 · see citing works
Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset
Meng, C., Trinh, L., Xu, N., Enouen, J., & Liu, Y. (2022) · Scientific Reports · doi:10.1038/s41598-022-11012-2 · Cited by 199 · see citing works
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Shao, Y., Gouws, S., Britz, D., Goldie, A., Strope, B. P., & Kurzweil, R. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1235 · Cited by 179 · see citing works
A Survey of Natural Language Generation
Dong, C., Li, Y., Gong, H., Chen, M., Li, J., Shen, Y., & Yang, M. (2022) · ACM Computing Surveys · doi:10.1145/3554727 · Cited by 169 · see citing works
Dialog State Tracking: A Neural Reading Comprehension Approach
Gao, S., Sethi, A. R., Agarwal, S., Chung, T., & Hakkani-Tür, D. Z. (2019) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/w19-5932 · Cited by 166 · see citing works
Model Agnostic Supervised Local Explanations
Plumb, G., Molitor, D., & Talwalkar, A. (2018) · arXiv · doi:10.48550/arxiv.1807.02910 · Cited by 149 · see citing works
Constrained Language Models Yield Few-Shot Semantic Parsers
Shin, R., Lin, C., Thomson, S., Chen, C., Roy, S., Platanios, E. A., Pauls, A., Klein, D., Eisner, J., & Van Durme, B. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.608 · Cited by 139 · cited 2× · see citing works
“To ensure the generations are grammatical, we constrain the decodings to be in the grammar [63].”
BackgroundMethodTenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.15 · Cited by 138 · cited 1× · see citing works
“…have proposed several point-and-click dashboard techniques to help overcome these issues, such as the Language Interpretability Tool (LiT) [73], which is designed to understand natural language processing (NLP) models and the "What-If" Tool [2]—a tool aimed at performing counterfactual…”
Backgroundvip: Variable Importance Plots
Greenwell, B., & Boehmke, B. (2018) · doi:10.32614/cran.package.vip · Cited by 134 · see citing works
L-Shapley and C-Shapley: Efficient Model Interpretation for Structured DataPreprint
Chen, J., Song, L., Wainwright, M. J., & Jordan, M. I. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.02610 · Cited by 108 · see citing works
(2017) · doi:10.1145/3097983.3098047 · Cited by 104 · see citing works
Using Context Information for Dialog Act Classification in DNN Framework
Liu, Y., Han, K., Tan, Z., & Lei, Y. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1231 · Cited by 103 · see citing works
Rethinking Stability for Attribution-based ExplanationsPreprint
Agarwal, C., Johnson, N., Pawelczyk, M., Krishna, S., Saxena, E., Zitnik, M., & Lakkaraju, H. (2022) · arXiv.org · doi:10.48550/arxiv.2203.06877 · Cited by 88 · see citing works
Self-citationReliable Post hoc Explanations: Modeling Uncertainty in ExplainabilityPreprint
Slack, D., Hilgard, S., Singh, S., & Lakkaraju, H. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05030 · Cited by 85 · see citing works
Self-citationNeural Approaches to Conversational AI
Gao, J., Galley, M., & Li, L. (2018) · Proceedings of ACL 2018, Tutorial Abstracts · doi:10.18653/v1/p18-5002 · Cited by 73 · see citing works
Sokol, K., & Flach, P. A. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/865 · Cited by 65 · see citing works
Predicting User Intents and Satisfaction with Dialogue-based Conversational Recommendations
Cai, W., & Chen, L. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3394856 · Cited by 56 · see citing works
RADAR: A Personal Assistant that Learns to Reduce Email Overload
Freed, M. C., Carbonell, J. G., Gordon, G. J., Hayes, J., Myers, B. A., Siewiorek, D. P., Smith, S. F., Steinfeld, A. M., & Tomasic, A. (2018) · doi:10.1184/r1/6625490 · Cited by 53 · see citing works
Improving Compositional Generalization in Semantic Parsing
Oren, I., Herzig, J., Gupta, N., Gardner, M., & Berant, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.225 · Cited by 49 · see citing works
Santhanam, S., & Shaikh, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.00500 · Cited by 43 · see citing works
Generating patient-specific interactive natural language explanations.
Carenini, G., Mittal, V. O., & Moore, J. D. (1994) · PubMed Central · Cited by 38 · see citing works
Axiomatic Interpretability for Multiclass Additive Models
Zhang, X., Tan, S., Koch, P., Lou, Y., Chajewska, U., & Caruana, R. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330898 · Cited by 37 · see citing works
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention
Yin, P., Fang, H., Neubig, G., Pauls, A., Platanios, E. A., Su, Y., Thomson, S., & Andreas, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.225 · Cited by 35 · see citing works
Hase, P., Xie, H., & Bansal, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00786 · Cited by 29 · see citing works
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society
Matthews, J., Babaeianjelodar, M., Lorenz, S., Matthews, A., Njie, M., Adams, N., Krane, D. E., Goldwaite, J., & Hughes, C. (2019) · doi:10.1145/3306618 · Cited by 29 · see citing works
Ten Challenges in Highly-Interactive Dialog Systems
Ward, N., & DeVault, D. (2015) · Cited by 20 · see citing works
Towards the Unification and Robustness of Perturbation and Gradient Based ExplanationsPreprint
Agarwal, S., Jabbari, S., Agarwal, C., Upadhyay, S., Wu, Z. S., & Lakkaraju, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10618 · Cited by 20 · see citing works
Self-citationHINT: Integration Testing for AI-based features with Humans in the Loop
Chen, Q. Z., Schnabel, T., Nushi, B., & Amershi, S. (2022) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3490099.3511141 · Cited by 18 · see citing works
NODE-GAM: Neural Generalized Additive Model for Interpretable Deep LearningPreprint
Chang, C., Caruana, R., & Goldenberg, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.01613 · Cited by 17 · see citing works
Evaluating Semantic Parsing against a Simple Web-based Question Answering Model
Talmor, A., Geva, M., & Berant, J. (2017) · Joint Conference on Lexical and Computational Semantics · doi:10.18653/v1/s17-1020 · Cited by 14 · see citing works
Tenney, I., Wexler, J., Bastings, J., Bolukbasi, T., Coenen, A., Gehrmann, S., Jiang, E., Pushkarna, M., Radebaugh, C., Reif, E., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.05122 · Cited by 13 · see citing works
CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues
Tseng, B.-H., Bhargava, S., Lu, J., Moniz, J. R. A., Piraviperumal, D., Li, L., & Yu, H. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.265 · Cited by 12 · see citing works
oegedijk/explainerdashboard: V0.4.2.1: workaround for dropdown search bug in dash
Dijk, O., oegesam, Bell, R., Lily, Simon-Free, Serna, B., Ferdman, E., rajgupt, yanhong-zhao-ef, Gädke, A., et al. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6407091 · Cited by 10 · see citing works
A Learning Theoretic Perspective on Local ExplainabilityPreprint
Li, J., Nagarajan, V., Plumb, G., & Talwalkar, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01205 · Cited by 5 · see citing works
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive Evaluation
Gupta, S., Singh, S., & Gardner, M. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.365 · Cited by 4 · see citing works
Self-citationDeveloping Dialogue Managers from Limited Amounts of Data
Rieser, V., & Lemon, O. (2012) · Springer eBooks · doi:10.1007/978-1-4614-4803-7_2 · Cited by 4 · see citing works
TalkToModel: Explaining Machine Learning Models with Interactive Natural Language Conversations
Slack, D., Krishna, S., Lakkaraju, H., & Singh, S. (2022) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.7502205 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
1 of the 80 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00834-7 · Cited by 1,409 · see citing works
“…could be, for example, to use generative AI for translating post hoc explanations derived from approaches like SHAP or LIME into more intuitive textual descriptions or generate user-friendly descriptions of models that are intrinsically inter-pretable (Slack et al. 2023; Zilker et al. 2023).”
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 302 · see citing works
Adversarial attacks and defenses in explainable artificial intelligence: A survey
Baniecki, H., & Biecek, P. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102303 · Cited by 204 · see citing works
Explainable Generative AI (GenXAI): a survey, conceptualization, and research agenda
Schneider, J. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10916-x · Cited by 184 · see citing works
Digital twins as global learning health and disease models for preventive and personalized medicine
Li, X., Loscalzo, J., Mahmud, A. K. M. F., Aly, D. M., Rzhetsky, A., Žitnik, M., & Benson, M. (2025) · Genome Medicine · doi:10.1186/s13073-025-01435-7 · Cited by 124 · see citing works
Generative AIPreprint
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4443189 · Cited by 65 · see citing works
Medical large language models are susceptible to targeted misinformation attacks
Han, T., Nebelung, S., Khader, F., Wang, T., Müller-Franzes, G., Kuhl, C., Försch, S., Kleesiek, J., Haarburger, C., Bressem, K. K., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01282-7 · Cited by 62 · see citing works
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI Assistant
He, G., Aishwarya, N., & Gadiraju, U. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712133 · Cited by 53 · see citing works
Cairone, S., Hasan, S. W., Choo, K., Li, C., Zorpas, A. A., Ksibi, M., Zarra, T., Belgiorno, V., & Naddeo, V. (2024) · Euro-Mediterranean Journal for Environmental Integration · doi:10.1007/s41207-024-00659-0 · Cited by 45 · see citing works
Ma, S., Chen, Q., Wang, X., Zheng, C., Peng, Z., Yin, M., & Ma, X. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713423 · Cited by 48 · see citing works
Ahmed, N., Saha, A. K., Noman, M. A. A., Jim, J. R., Mridha, M. F., & Kabir, M. M. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100112 · Cited by 44 · see citing works
FaultExplainer: Leveraging large language models for interpretable fault detection and diagnosis
Khan, A., Nahar, R., Chen, H., Constante‐Flores, G. E., & Li, C. (2025) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2025.109152 · Cited by 45 · see citing works
Kabir, S., Hossain, M. S., & Andersson, K. (2025) · Algorithms · doi:10.3390/a18090556 · Cited by 44 · see citing works
EXMOS: Explanatory Model Steering through Multifaceted Explanations and Data Configurations
Bhattacharya, A., Stumpf, S., Gosak, L., Štiglic, G., & Verbert, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642106 · Cited by 38 · see citing works
Artificial intelligence-enhanced patient evaluation: bridging art and science
Oikonomou, E. K., & Khera, R. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae415 · Cited by 34 · see citing works
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Martens, D., Hinns, J., Dams, C., Vergouwen, M., & Evgeniou, T. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114402 · Cited by 43 · see citing works
Panigrahi, B., Razavi, S., Doig, L., Cordell, B., Gupta, H. V., & Liber, K. (2025) · Water Resources Research · doi:10.1029/2024wr037398 · Cited by 24 · see citing works
Buçinca, Z., Swaroop, S., Paluch, A. E., Doshi‐Velez, F., & Gajos, K. Z. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713229 · Cited by 33 · see citing works
Why we do need explainable AI for healthcare
Cinà, G., Röber, T. E., Goedhart, R., & Birbil, Ş. İ. (2025) · Diagnostic and Prognostic Research · doi:10.1186/s41512-025-00209-4 · Cited by 39 · see citing works
Zhang, K., Wang, D., Lin, F., Xie, J., & Zhou, W. (2026) · iScience · doi:10.1016/j.isci.2026.115026 · Cited by 39 · see citing works
Zilker, S., Weinzierl, S., Kraus, M., Zschech, P., & Matzner, M. (2024) · Health Care Management Science · doi:10.1007/s10729-024-09673-8 · Cited by 29 · see citing works
Ma, P., Chen, L., Yu, C., Zhu, Q., Ding, Y., Wu, Z., Li, H., Tian, C., & Fan, X. (2025) · Remote Sensing of Environment · doi:10.1016/j.rse.2025.114800 · Cited by 27 · see citing works
Diagnosing infeasible optimization problems using large language models
Chen, H., Constante‐Flores, G. E., & Li, C. (2024) · INFOR Information Systems and Operational Research · doi:10.1080/03155986.2024.2385189 · Cited by 26 · see citing works
Human-AI interaction in safety-critical network infrastructures
Mussi, M., Metelli, A. M., Restelli, M., Losapio, G., Bessa, R. J., Boos, D., Borst, C., Leto, G., Castagna, A., Chavarriaga, R., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113400 · Cited by 25 · see citing works
Yu, Q., Shen, X., Yi, L., Liang, M., Li, G., Guan, Z., Wu, X., Castel, H., Hu, B., Yin, P., et al. (2024) · ACS Sensors · doi:10.1021/acssensors.4c00149 · Cited by 23 · see citing works
He, G., Demartini, G., & Gadiraju, U. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713218 · Cited by 23 · see citing works
Building trustworthy NeuroSymbolic AI Systems: Consistency, reliability, explainability, and safety
Gaur, M., & Sheth, A. (2024) · AI Magazine · doi:10.1002/aaai.12149 · Cited by 27 · see citing works
Measuring the Interpretability and Explainability of Model Decisions of Five Large Language ModelsPreprint
Fujiwara, K., Sasaki, M., Nakamura, A., & Watanabe, N. (2024) · doi:10.31219/osf.io/d4ntw · Cited by 19 · see citing works
From Text to Trust: Empowering AI-assisted Decision Making with Adaptive LLM-powered Analysis
Li, Z., Zhu, H., Lu, Z., Xiao, Z., & Yin, M. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713133 · Cited by 18 · see citing works
Kundu, R. K., & Hoque, K. A. (2025) · IEEE Access · doi:10.1109/access.2025.3566958 · Cited by 16 · see citing works
Explingo: Explaining AI Predictions using Large Language Models
Zytek, A., Pidò, S., Alnegheimish, S., Berti‐Équille, L., & Veeramachaneni, K. K. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825114 · Cited by 16 · see citing works
StressLLM: Large Language Models for Stress Prediction via Wearable Sensor Data
Thapa, B., Rivas, M., Griffith, H., & Rathore, H. (2025) · International Conference on Consumer Electronics · doi:10.1109/icce63647.2025.10929774 · Cited by 14 · see citing works
Generating and leveraging explanations of AI/ML models in materials and manufacturing research
Braham, E. J., Ruddock, J. M., & Hardin, J. O. (2025) · Patterns · doi:10.1016/j.patter.2025.101340 · Cited by 14 · see citing works
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Nguyen, V. B., Schlötterer, J., & Seifert, C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44070-0_4 · Cited by 14 · see citing works
Inherently Interpretable Machine Learning: A Contrasting Paradigm to Post-hoc Explainable AI
Zschech, P., Weinzierl, S., & Kraus, M. (2025) · Business & Information Systems Engineering · doi:10.1007/s12599-025-00964-0 · Cited by 13 · see citing works
InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations
Feldhus, N., Wang, Q., Anikina, T., Chopra, S., Oguz, C., & Möller, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.359 · Cited by 13 · see citing works
Kreutz, H., & Jahankhani, H. (2024) · Advanced sciences and technologies for security applications · doi:10.1007/978-3-031-52272-7_1 · Cited by 10 · see citing works
Towards Directive Explanations: Crafting Explainable AI Systems for Actionable Human-AI Interactions
Bhattacharya, A. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3638177 · Cited by 10 · see citing works
Redefining Mathematics Education in the Age of Artificial Intelligence
Zreık, M. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-2873-6.ch002 · Cited by 13 · see citing works
Visual-Conversational Interface for Evidence-Based Explanation of Diabetes Risk Prediction
Samimi, R., Bhattacharya, A., Gosak, L., Štiglic, G., & Verbert, K. (2025) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3719160.3736616 · Cited by 10 · see citing works
Pharmacometrics in the Age of Large Language Models: A Vision of the Future
Tosca, E. M., Aiello, L., Carlo, A. D., & Magni, P. (2025) · Pharmaceutics · doi:10.3390/pharmaceutics17101274 · Cited by 10 · see citing works
Bello, M., Bello, R., García, M.-M., Nowé, A., Sevillano-García, I., & Herrera, F. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10668-1 · Cited by 14 · see citing works
VanNostrand, P. M., Hofmann, D. M., Ma, L., & Rundensteiner, E. A. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3658997 · Cited by 8 · see citing works
Murray, M. C. (2025) · Issues in Informing Science and Information Technology · doi:10.28945/5516 · Cited by 10 · see citing works
Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation
Castelnovo, A., Depalmas, R., Mercorio, F., Mombelli, N., Potertì, D., Serino, A., Seveso, A., Sorrentino, S., & Viola, L. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-63787-2_11 · Cited by 9 · see citing works
Zhang, T., Yang, X. J., & Li, B. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2364986 · Cited by 8 · see citing works
Gyevnár, B., Droop, S., Quillien, T., Cohen, S. B., Bramley, N. R., Lucas, C. G., & Albrecht, S. V. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713509 · Cited by 8 · see citing works
Bhattacharya, A., Stumpf, S., Croon, R. D., & Verbert, K. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713497 · Cited by 6 · see citing works
Bhattacharya, A., Vanherwegen, T., & Verbert, K. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3699682.3728321 · Cited by 6 · see citing works
Intuitive tests to validate machine learning models against physics and domain knowledge
Naser, M. Z. (2025) · Digital engineering. · doi:10.1016/j.dte.2025.100057 · Cited by 6 · see citing works
Conversational Explanations: Discussing Explainable AI with Non-AI Experts
Zhang, T., Zhang, M., Low, W. Y., Yang, X. J., & Li, B. (2025) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3708359.3712143 · Cited by 7 · see citing works
Takefuji, Y. (2025) · Cities · doi:10.1016/j.cities.2025.106207 · Cited by 4 · see citing works
Can large language models help solve the cost problem for the right to explanation?
Munch, L. A., & Bjerring, J. C. (2024) · Journal of Medical Ethics · doi:10.1136/jme-2023-109737 · Cited by 4 · see citing works
Liu, H., & Li, L. (2024) · Materials Today Communications · doi:10.1016/j.mtcomm.2024.109809 · Cited by 4 · see citing works
QoEXplainer: Mediating Explainable Quality of Experience Models with Large Language Models
Wehner, N., Feldhus, N., Seufert, M., Möller, S., & Hoßfeld, T. (2024) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex61742.2024.10598297 · Cited by 4 · see citing works
Spitzer, P., Celis, S., Martin, D., Kühl, N., & Satzger, G. (2024) · Mensch und Computer (MuC) · doi:10.1145/3670653.3677488 · Cited by 4 · see citing works
RIXA - Explaining Artificial Intelligence in Natural Language
Becker, M., Vishwesh, V., Birnstill, P., Schwall, F., Wu, S., & Beyerer, J. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00118 · Cited by 4 · see citing works
Panigrahi, I., Kim, S. S. Y., Liaqat, A., Jinturkar, R., Russakovsky, O., Fong, R., & Abtahi, P. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719730 · Cited by 3 · see citing works
Empowering scientific discovery with explainable small domain-specific and large language models
Yu, H., Wang, Y., Cheng, T., Yan, Y., Dawson, K. A., Li, S. F. Y., Zheng, Y., & Jin, Y. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11365-w · Cited by 3 · see citing works
OptiChat: Bridging Optimization Models and Practitioners with Large Language Models
Chen, H., Constante‐Flores, G. E., Mantri, K. S. I., Kompalli, S. M., Ahluwalia, A. S., & Li, C. (2025) · INFORMS Journal on Data Science · doi:10.1287/ijds.2025.0074 · Cited by 4 · see citing works
Understanding the Effects of AI-Assisted Critical Thinking on Human-AI Decision Making
Tian, H. Y., Amin, H., & Yin, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790785 · Cited by 5 · see citing works
A meta-heuristic approach to estimate and explain classifier uncertainty
Houston, A. D., & Cosma, G. (2025) · Applied Intelligence · doi:10.1007/s10489-024-06127-0 · Cited by 3 · see citing works
Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
Hakizimana, G., & Ledezma Espino, A. (2025) · Algorithms · doi:10.3390/a18060306 · Cited by 2 · see citing works
TELL-ME: Toward Personalized Explanations of Large Language Models
Jeck, J., Leiser, F., Hüsges, A., & Sunyaev, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719982 · Cited by 2 · see citing works
Patient-Centred Explainability in IVF Outcome Prediction
Sivaprasad, A., Reiter, E., McLernon, D. J., Tintarev, N., Bhattacharya, S., & Oren, N. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-00656-1_7 · Cited by 2 · see citing works
Molaei, S., Robert, L., & Banović, N. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757437 · Cited by 2 · see citing works
PLEX: Perturbation-Free Local Explanations for LLM-Based Text Classification
Rahulamathavan, Y., Farooq, M., & Silva, D. (2025) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2025.3633593 · Cited by 3 · see citing works
Abreu, M. C., Duarte, R. D. B., Neves, B. D., Silva, N. A., Santos, R., & Campos, J. (2026) · ACM Transactions on Computing for Healthcare · doi:10.1145/3788685 · Cited by 3 · see citing works
Kim, H., Chen, H., Li, C., & Lee, J. M. (2026) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2026.109672 · Cited by 2 · see citing works
Wang, Y., Zhao, J. L., Hong, J., Askin, R. G., & Maciejewski, R. (2024) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2024.3468352 · Cited by 2 · see citing works
Christina, S., Sowjanya, S., Lakshmhyma, C., Prathiba, L., & Basha, M. S. A. (2025) · International Conference on Intelligent Systems and Computational Networks (ICISCN) · doi:10.1109/iciscn64258.2025.10934398 · Cited by 2 · see citing works
Mindlin, D., & Cimiano, P. (2025) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc64641.2025.00015 · Cited by 2 · see citing works
Evolutionary Computation for Explainable Deep Learning
Zhou, R., & Hu, T. (2025) · Natural computing series · doi:10.1007/978-981-96-2540-6_4 · Cited by 1 · see citing works
XRL-LLM: Explainable Reinforcement Learning Framework for Voltage Control
Jadhav, S., Sevak, B., & Bui, V. (2026) · Energies · doi:10.3390/en19071789 · Cited by 1 · see citing works
Herrera-Poyatos, D., Peláez-González, C., Zuheros, C., Tejedor, V., Montes, R., & Herrera, F. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104389 · Cited by 1 · see citing works
Grimm, V., Herder, E., Rubart, J., & Röcker, C. (2026) · ACM Web Science Conference (WebSci) · doi:10.1145/3795513.3810452 · Cited by 1 · see citing works
Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs
Vázquez, G., Evangelidis, A., Shahbeigi, S., Calinescu, R., & Gerasimou, S. (2026) · SEAMS@ICSE · doi:10.1145/3788550.3794879 · Cited by 4 · see citing works
Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
The concept of a set in the theory and practice of designing
Михайлович, Б. Н. (2023) · Ontology of Designing · doi:10.18287/2223-9537-2023-13-3-306-332
On Robustness of the Explanatory Power of Machine Learning ModelsPreprint
Panigrahi, B., Razavi, S., Doig, L. E., Cordell, B., Gupta, H. V., & Liber, K. (2024) · doi:10.22541/essoar.171052510.02996233/v1
Is Copyright Law the New Turing Test?
Blumenthal, R. L. (2023) · ACM SIGCAS Computers and Society · doi:10.1145/3656033.3656036
Why Do You Think This Person Is Introverted? – Towards Conversational Argumentative Explainability
Berman, A. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240222
Yang, N.-E., Han, E.-B., & Choi, J. (2024) · Journal of Digital Contents Society · doi:10.9728/dcs.2024.25.8.2261
Explainable Feature Ranking Using Interactive Dashboards
Amorim, D., Pato, M. P. M., & Datia, N. (2024) · International Conference on Information Visualisation (IV) · doi:10.1109/iv64223.2024.00050
Research Agenda for Sociotechnical Approaches to AI SafetyPreprint
Curtis, S., Iyer, R., Kirk‐Giannini, C. D., Krakovna, V., Krueger, D., Lambert, N., Marnette, B., McKenzie, C., Michael, J., Miyazono, E., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5097286
Assessing Conversational Capabilities of Explanatory AI Interfaces
Berman, A., & Larsson, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-93412-4_1
Interactive Explanations through Dialogue Systems
Feustel, I. (2024) · Young Researchers' Roundtable on Spoken Dialogue Systems (YRRSDS) · doi:10.18653/v1/2024.yrrsds-1.29
Conversational XAI and Explanation Dialogues
Feldhus, N. (2024) · Young Researchers' Roundtable on Spoken Dialogue Systems (YRRSDS) · doi:10.18653/v1/2024.yrrsds-1.1
Bringing Machine Learning Models Beyond the Experimental Stage with Explainable AI
Mikkelsen, N. W., Pedersen, L. S., Hussain, M., Foged, V., & Kindler, E. (2025) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1109/cain66642.2025.00022
Sardari, S., Fernandes, F., Araya-Martinez, J. M., Zak, J. A., & Roitberg, A. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163755
MAINLE: A Multi-Agent, Interactive, Natural Language Local Explainer of Classification Tasks
Serafim, P. B. S., Filho, R. F. F., Freitas, S., Gezi̇ci, G., Giannotti, F., Raimondi, F., & dos Santos, A. H. R. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06078-5_9
AI Agents: From Concept to Code to Commerce
Ishida, T., Murakami, Y., Lin, D., & Lhaksmana, K. M. (2025) · doi:10.1109/icoict66265.2025.11193130 · Cited by 1 · see citing works
Robot Delivery of Actionable Counterfactual Neural Network Explanations: Results in Group Perception
Narcomey, A., Claure, H., & Vázquez, M. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217869
Zhang, X., Chen, H., & Wu, L. (2025) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3746252.3760829
Suvorova, A. V. (2025) · Высшая школа: научные исследования · doi:10.15622/rcai.2025.028
Dual Attention Alignment for Clause-Grounded Instruction-based Legal Question Answering
Sowmiya, K., Umar Farook, R., Giridharan, B., Shalini, A., & Shalini, M. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11254806
Mindlin, D., Booshehri, M., & Cimiano, P. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3765768
Zhang, Y., Zhao, W., & Hu, C. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108390
Beyond Technical Transparency: Explainability as a Safeguard Against Manipulative AI*
Petrocchi, E., Tiribelli, S., Buzcu, B., Pacioni, E., & Calvaresi, D. (2025) · IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) · doi:10.1109/metroxraine66377.2025.11340252
Leveraging Large Language Models for Hybrid Workplace Recommendation
Kim, Y., & Hsu, C.-C. (2025) · International Conference on Foundation and Large Language Models (FLLM) · doi:10.1109/fllm67465.2025.11390885
- Authors
- Slack, D., Krishna, S., Lakkaraju, H., & Singh, S.
- Affiliations
- University of California, Irvine (US)
- Harvard University (US)
- Corresponding author
- Slack, Dylan
- Venue
- Nature Machine Intelligence
- Published
- 2023-07-27
- Volume
- 5
- Issue
- 8
- Pages
- 873-883
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W4385302687
- Cited by
- 125
- References
- 80
- Citations by year
- 2026: 31 · 2025: 53 · 2024: 33 · 2023: 8
- FWCI
- 11.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Machine Intelligence (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Funders
- NSF | Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems (IIS-2040989, IIS-2046873, IIS-2008956, IIS-2008461)
- I was supported by a fellowship from the hasso plattner institute during the bulk of completing this work.
- Google, JP Morgan, Amazon, Harvard Data Science Initiative, D^3 institute at Harvard
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Software › Software Testing and Debugging Techniques
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- machine learning explainability, natural language dialogue systems, interactive explanations, conversational interfaces, model understanding, disease prediction model, healthcare workers
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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