Explainable Artificial Intelligence (XAI): Interpretable Models for Ethical Decision-Making

Abstract

This paper reflects on the role of explainable artificial intelligence (XAI) towards constructing interpretable models that achieve ethically-sound decisions in areas such as health, finance, and public sector decision-making. Interpretable models, such as logistic regression or shallow decision trees, are transparent, accountable, and trustworthy - contrasting with a black-box opaque algorithm. We created interpretable classifiers using an Adult-like synthetic dataset with socio-economic decision-making. We examined the classifiers-key predictions with XAI tools. The results showed that interpretable models correctly sieved between accuracy versus interpretability, while identifying decision drivers, and potential biases. More, fairness metrics indicated evidence of systematic disparities, emphasizing the need to combine XAI with ethical auditing frameworks.

Introduction

Artificial Intelligence (AI) is transforming decision-making across critical sectors like healthcare, finance, and public services. Its strength lies in processing large datasets and detecting patterns that outperform human capabilities. However, as complex models like deep neural networks (DNNs) become more common, concerns about transparency, fairness, and accountability arise.

These opaque “black box” models lack interpretability, especially troubling in high-stakes decisions where human welfare is involved. This has led to the emergence of Explainable AI (XAI), which aims to make AI decision-making transparent, interpretable, and understandable—without significantly compromising predictive performance.

2. The Role of XAI

XAI helps:

• Build trust by exposing model logic

• Enable auditing and regulatory compliance

• Improve ethical accountability

• Avoid amplifying historical bias present in training data

Tools like SHAP offer local and global interpretability even for complex models. However, interpretability alone isn't sufficient. It must be paired with fairness metrics to prevent AI systems from perpetuating inequality.

3. Literature Review

Several key studies form the foundation of this research:

• Barocas et al. (2019): Fairness is both technical and social; emphasized bias mitigation at data/model level.

• Carvalho et al. (2019): Surveyed interpretability methods; interpretability is context-dependent.

• Díaz et al. (2024): Argued XAI is essential for ethical organizational decisions.

• Doshi-Velez & Kim (2017): Called for formal definitions of interpretability and systematic evaluation.

• Gerlings et al. (2020): Emphasized explainability as a necessity in high-stakes AI.

• Gilpin et al. (2018): Classified explanation types (transparent vs. post-hoc) and matched them to user needs.

4. Research Methodology

A synthetic dataset modeled after the UCI Adult Income dataset was used to:

• Predict whether income > $50K

• Evaluate accuracy and fairness

• Employ interpretable models and XAI tools

A. Dataset

Included features like age, education, work hours, occupation, sex, and race, enabling both performance and fairness assessments.

B. Models

• Logistic Regression (LR): Linear, globally interpretable.

• Decision Tree (DT, depth=4): Rule-based, human-readable.

C. XAI Tools

• SHAP: For local and global feature attribution.

• Feature weights, importances, confusion matrices, ROC curves.

• Fairness Metric: Selection rate by sex, identifying disparities in income prediction.

D. Evaluation Metrics

• Performance: Accuracy, precision, recall, F1, ROC-AUC.

• Fairness: Comparison of predicted outcomes across demographic groups (e.g., sex).

5. Results

A. Model Performance

• Logistic Regression outperformed Decision Tree across all metrics, especially in classification reliability and discrimination ability (ROC-AUC).

B. ROC Curve Analysis

• ROC curves show Logistic Regression has a smoother and higher curve, indicating better class separability and more stable performance.

C. Confusion Matrix

• Most predictions were correct, but some false positives were observed—important when assessing potential biases, particularly against marginalized groups.

Conclusion

This study demonstrated how important Explainable Artificial Intelligence (XAI) is in supporting ethical decision-making in machine learning systems by illustrating the trade-off of accuracy and transparency with interpretable models such as Logistic Regression and shallow Decision Trees. The models not only provided a high level of classification performance, but they also provided human interpretable information about how socio-economic variables impacted our income prediction. The XAI tools we used (i.e., the model coefficients and SHAP-based explanations) further enhanced our interpretability by describing global patterns and person-specific contributions to the model predictions. In addition, fairness metrics revealed systematic bias within the algorithm, specifically with gender bias, demonstrating that transparency alone may not mean ethical AI. We argued that understanding the interpretability of a model\'s decision is only part of the equation, and it is necessary that we remain vigilant about integrating interpretability with fairness auditing to discover and remediate hidden biases. Lastly, we suggest future work to improve our auditing framework employ bias mitigation methods including reweighting, adversarial debiasing, or adjusting thresholds, to minimize inequitable outcomes of models. All-in-all, this research confirms that XAI is essential for fostering accurate AI systems that are also accountable, trustworthy, and aligned with human values, social justice, and required legislation - especially in critical application areas, where you are not only concerned with the performance of the models, but the ethical ramifications of the model\'s decision.

References

[1] Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. Cambridge, MA: fairmlbook.org. [2] Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8(8), 832. [3] Díaz, G. M., Hernández, J. J. G., & Salvador, J. L. G. (2024). Explainable artificial intelligence (XAI) and ethical decision-making in business. In Smart Ethics in the Digital World: Proceedings of the ETHICOMP 2024. 21st International Conference on the Ethical and Social Impacts of ICT (pp. 19-22). Universidad de La Rioja. [4] Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608. [5] Gerlings, J., Shollo, A., & Constantiou, I. (2020). Reviewing the need for explainable artificial intelligence (xAI). arXiv preprint arXiv:2012.01007. [6] Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning. 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), 80–89. [7] Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., & Pedreschi, D. (2018). A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5), 93:1–93:42. [8] Lipton, Z. C. (2018). The Mythos of Model Interpretability. Communications of the ACM, 61(10), 36–43. [9] Lundberg, S. M., & Lee, S. I. (2017). A Unified Approach to Interpreting Model Predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774. [10] Mittelstadt, B., Russell, C., & Wachter, S. (2019). Explaining Explanations in AI. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT)*, 279–288. [11] Molnar, C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Christoph Molnar. [12] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why Should I Trust You?” Explaining the Predictions of Any Classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 1135–1144. [13] Tjoa, E., & Guan, C. (2020). A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. [14] Wachter, S., Mittelstadt, B., & Russell, C. (2018). Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887. [15] Yadav, B. R. (2024). The ethics of understanding: Exploring moral implications of explainable AI. International Journal of Science and Research (IJSR), 13(6), 1-7.

Copyright

Copyright © 2025 Dr. Diwakar Ramanuj Tripathi, Apeksha Shankar Urkude, Dr. Vrushali Pramod Parkhi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.