Explainable Machine Learning Models for High-Stakes Decision-Making: Bridging Transparency and Performance

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@article{1707003,
      author = {Martin Lious},
      title = {Explainable Machine Learning Models for High-Stakes Decision-Making: Bridging Transparency and Performance},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {357-373},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707003.pdf},
      abstract = {Nowadays, machine learning (ML) has shown significant success in making high-tensile decisions for health care, economic, legal, and public safety domains. These domains require not only accurate prediction models but also understandable predictions because these models should be transparent, non-discriminatory, and auditable. However, a significant challenge arises from the trade-off between model complexity and interpretability: They have found that highly accurate methods, including deep neural networks, may not be interpretable, but weakly interpretable models are less precise, for instance, performing worse than deep neural networks. The current article discusses principles and methodologies used in XML and the approaches appropriate for using these models in critical decision-making contexts. It discusses methods of model interpretation of intrinsic and post-hoc types, particular types of interpretable models, and specific explanation methods like SHAP and LIME. The discussion above reveals some pertinent problems, including using accurate general models, incorporating bias-free and fair models, and integrating the algorithms in real-time business decisions. This paper also discusses the ethics of XML, societal concerns relating to the use of XML and calls for trust and accountability as well as compliance with the set regulations. As summarised, the paper discusses the further prospects for research in the subject area, with causal explainability, the use of interactive tools, and the creation of appropriate ethical standards for using explainable AI systems. By creating the bridge between transparency and performance, XML points to approaches to develop trustful, fair, and efficient ML solutions for critical applications.},
      keywords = {Explainable Machine Learning, High-Stakes Decision-Making, Transparency in AI, Interpretable Models, AI Explainability, Model Transparency, Ethical AI, Trustworthy AI Systems, Performance in Machine Learning, Responsible AI Deployment, Decision-Making Systems, AI in Critical Domains, Machine Learning Accountability, AI Fairness and Ethics, Interpretability vs. Performance.},
      month = {December},
  }