Explainable AI interfaces in cloud‑deployed portfolio optimization systems

  • Authors

    • Dr. Rajesh Kumar Sharma Professor, University of Delhi, India. Author
    • Dr. Mohammed Asif Khan Professor, Jamia Millia Islamia, India. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2020PI3B7M

    Published 02-04-2020

  • Explainable AI (XAI), Portfolio Optimization, Cloud Computing, Financial Technology (FinTech), Interpretable Machine Learning, Human-AI Interaction, Model Transparency, Algorithmic Decision-Making, Risk Management, Robo-Advisors

    Issue

    Vol. 3 No. 1 (2020)

    Section

    Articles

    How to Cite

    [1]
    R. K. Sharma and M. A. Khan, “Explainable AI interfaces in cloud‑deployed portfolio optimization systems”, IJAIDT, vol. 3, no. 1, pp. 01–15, Feb. 2020, doi: 10.67228/30713315/IJAIDT-2020PI3B7M.

  • Abstract

    As artificial intelligence (AI) increasingly drives decision-making in finance, ensuring transparency and trust becomes essential, particularly in high-stakes applications like portfolio optimization. This paper explores the integration of Explainable AI (XAI) interfaces within cloud-deployed portfolio optimization systems, aiming to bridge the gap between advanced AI models and financial professionals. We outline a cloud-native architecture that embeds explainability into each stage of the portfolio optimization lifecycle, from data ingestion to user interaction. Various XAI methods, including feature attribution and model-agnostic explanations, are examined in the context of financial decision-making. We present design considerations for building user-facing interfaces that make model decisions interpretable and actionable. A case study illustrates how such interfaces can enhance user trust and improve portfolio strategy validation. Finally, we discuss technical, regulatory, and usability challenges, and propose future research directions for deploying responsible AI in cloud-based financial systems.

  • References

    [1] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why Should I Trust You?": Explaining the Predictions of Any Classifier. KDD.

    [2] Lundberg, S. M., & Lee, S.-I. (2017). A Unified Approach to Interpreting Model Predictions. NeurIPS.

    [3] Doshi-Velez, F., & Kim, B. (2017). Towards a Rigorous Science of Interpretable Machine Learning. arXiv preprint arXiv:1702.08608.

    [4] Markowitz, H. (1952). Portfolio Selection. The Journal of Finance.

    [5] Black, F., & Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal.

    [6] Bertsimas, D., Dunn, J., & Pawlowski, C. (2020). Machine Learning for Portfolio Optimization. The Journal of Financial Data Science.

    [7] Holzinger, A., Biemann, C., Pattichis, C. S., & Kell, D. B. (2017). What do we need to build explainable AI systems for the medical domain? arXiv preprint arXiv:1712.09923.

    [8] Lipton, Z. C. (2018). The Mythos of Model Interpretability. Communications of the ACM.

    [9] Gilpin, L. H., Bau, D., Yuan, B. Z., et al. (2018). Explaining Explanations: An Overview of Interpretability of Machine Learning. IEEE DSAA.

    [10] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

    [11] Gadepalli, K., Zeng, X., & Kagal, L. (2020). Explainable AI in Financial Services. IBM Research White Paper.

    [12] Veale, M., & Edwards, L. (2018). GDPR: The Right to Explanation, Explained. Computer Law Review International.

    [13] McMahan, B., Moore, E., Ramage, D., Hampson, S., & y Arcas, B. A. (2017). Communication-efficient Learning of Deep Networks from Decentralized Data. AISTATS.

    [14] Chakraborty, S., Tomsett, R., Raghavan, S., et al. (2017). Interpretability of Deep Learning Models: A Survey of Results. arXiv preprint arXiv:1706.07269.

    [15] Amershi, S., Weld, D., Vorvoreanu, M., et al. (2019). Guidelines for Human-AI Interaction. CHI Conference on Human Factors in Computing Systems.

  • Downloads