Vol. 6 No. 1 (2026): January−June 2026
Articles

Toward Trustworthy and Robust Artificial Intelligence in High-Stakes Domains: A Cross-Domain Survey of Methods, Applications, and Open Challenges

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  • Adrian K. Mercer
Adrian K. Mercer
Department of Computer Science, University of Massachusetts Lowell, Lowell, MA 01854, USA

Published 2026-04-13

Keywords

  • trustworthy AI,
  • explainable machine learning,
  • federated learning,
  • differential privacy,
  • graph neural networks,
  • large language models
    • ...More
    Less

How to Cite

Mercer, A. K. (2026). Toward Trustworthy and Robust Artificial Intelligence in High-Stakes Domains: A Cross-Domain Survey of Methods, Applications, and Open Challenges. Spectrum of Research, 6(1). Retrieved from https://spectrumofresearch.com/index.php/sr/article/view/48

Copyright (c) 2026 Spectrum of Research

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Artificial intelligence (AI) has moved rapidly from controlled benchmarks into operational settings where decisions carry financial, clinical, legal, and safety consequences. In these high-stakes domains, raw predictive accuracy is no longer a sufficient measure of a system's value: deployment additionally demands explainability, fairness, privacy preservation, and robustness under distribution shift and adversarial pressure. This paper presents a cross-domain survey that synthesizes 196 recent studies spanning financial risk and compliance, healthcare and biomedical analytics, cybersecurity, sustainability and energy systems, autonomous perception, and a broad family of human-centered applications. Rather than organizing the literature by application alone, we propose a two-axis taxonomy that separates six recurring methodological pillars — graph-based representation learning, privacy-preserving and federated learning, explainability and fairness modeling, multimodal data fusion, large language models (LLMs) and agentic systems, and anomaly and risk detection — from the application domains in which they are instantiated, and we treat trustworthiness requirements as a cross-cutting layer that binds the two. Through a structured reading of the corpus, we observe a pronounced shift over 2023-2026 from performance-centered work toward trustworthiness-aware design, a convergence of techniques across nominally unrelated domains, and a maturing emphasis on agentic robustness, calibration, and regulatory alignment. We distill a set of design principles that recur across domains, and we close with a discussion of open challenges, including privacy-utility and fairness-accuracy trade-offs, hallucination and safety in LLM-driven pipelines, memory integrity in multi-agent systems, calibration under distribution shift, and the persistent gap between methodological progress and trustworthy clinical or regulatory deployment. The survey is intended as a map for researchers and practitioners who must reason simultaneously about model capability and deployment trust.

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