A Comprehensive Review on Financial Explainable AI

• URL: http://arxiv.org/abs/2309.11960v1
• Date: Thu, 21 Sep 2023 10:30:49 GMT
• Title: A Comprehensive Review on Financial Explainable AI
• Authors: Wei Jie Yeo, Wihan van der Heever, Rui Mao, Erik Cambria, Ranjan Satapathy, Gianmarco Mengaldo
• Abstract summary: We provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics. We review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important.
• Score: 29.229196780505532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process huge amounts of data and learn complex patterns. However, due to their lack of explainability, there are significant concerns regarding their use in critical sectors, such as finance and healthcare, where decision-making transparency is of paramount importance. In this paper, we provide a comparative survey of methods that aim to improve the explainability of deep learning models within the context of finance. We categorize the collection of explainable AI methods according to their corresponding characteristics, and we review the concerns and challenges of adopting explainable AI methods, together with future directions we deemed appropriate and important.

Related papers

• Explainability in AI Based Applications: A Framework for Comparing Different Techniques [2.5874041837241304]
  In business applications, the challenge lies in selecting an appropriate explainability method that balances comprehensibility with accuracy. This paper proposes a novel method for the assessment of the agreement of different explainability techniques. By providing a practical framework for understanding the agreement of diverse explainability techniques, our research aims to facilitate the broader integration of interpretable AI systems in business applications.
  arXiv  Detail & Related papers  (2024-10-28T09:45:34Z)
• A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods [0.0]
  Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks. These models often lack transparency and interpretability, making them challenging to use in sensitive domains like finance. This paper explores good practices for deploying explainability in AI-based systems for finance.
  arXiv  Detail & Related papers  (2023-11-13T17:56:45Z)
• A Study of Situational Reasoning for Traffic Understanding [63.45021731775964]
  We devise three novel text-based tasks for situational reasoning in the traffic domain. We adopt four knowledge-enhanced methods that have shown generalization capability across language reasoning tasks in prior work. We provide in-depth analyses of model performance on data partitions and examine model predictions categorically.
  arXiv  Detail & Related papers  (2023-06-05T01:01:12Z)
• Explainable Data-Driven Optimization: From Context to Decision and Back Again [76.84947521482631]
  Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters. We introduce a counterfactual explanation methodology tailored to explain solutions to data-driven problems. We demonstrate our approach by explaining key problems in operations management such as inventory management and routing.
  arXiv  Detail & Related papers  (2023-01-24T15:25:16Z)
• Explainable Deep Reinforcement Learning: State of the Art and Challenges [1.005130974691351]
  Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains. This article provides a review of state of the art methods for explainable deep reinforcement learning methods.
  arXiv  Detail & Related papers  (2023-01-24T11:41:25Z)
• Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement [75.00655434905417]
  Explainable Artificial Intelligence (XAI) is an emerging research field bringing transparency to highly complex machine learning (ML) models. This paper offers a comprehensive overview over techniques that apply XAI practically for improving various properties of ML models. We show empirically through experiments on toy and realistic settings how explanations can help improve properties such as model generalization ability or reasoning.
  arXiv  Detail & Related papers  (2022-03-15T15:44:28Z)
• Harnessing value from data science in business: ensuring explainability and fairness of solutions [0.0]
  The paper introduces concepts of fairness and explainability (XAI) in artificial intelligence, oriented to solve a sophisticated business problems. For fairness, the authors discuss the bias-inducing specifics, as well as relevant mitigation methods, concluding with a set of recipes for introducing fairness in data-driven organizations.
  arXiv  Detail & Related papers  (2021-08-10T11:59:38Z)
• Rational Shapley Values [0.0]
  Most popular tools for post-hoc explainable artificial intelligence (XAI) are either insensitive to context or difficult to summarize. I introduce emphrational Shapley values, a novel XAI method that synthesizes and extends these seemingly incompatible approaches. I leverage tools from decision theory and causal modeling to formalize and implement a pragmatic approach that resolves a number of known challenges in XAI.
  arXiv  Detail & Related papers  (2021-06-18T15:45:21Z)
• Counterfactual Explanations as Interventions in Latent Space [62.997667081978825]
  Counterfactual explanations aim to provide to end users a set of features that need to be changed in order to achieve a desired outcome. Current approaches rarely take into account the feasibility of actions needed to achieve the proposed explanations. We present Counterfactual Explanations as Interventions in Latent Space (CEILS), a methodology to generate counterfactual explanations.
  arXiv  Detail & Related papers  (2021-06-14T20:48:48Z)
• Individual Explanations in Machine Learning Models: A Survey for Practitioners [69.02688684221265]
  The use of sophisticated statistical models that influence decisions in domains of high societal relevance is on the rise. Many governments, institutions, and companies are reluctant to their adoption as their output is often difficult to explain in human-interpretable ways. Recently, the academic literature has proposed a substantial amount of methods for providing interpretable explanations to machine learning models.
  arXiv  Detail & Related papers  (2021-04-09T01:46:34Z)
• Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty [66.17147341354577]
  We argue for considering a complementary form of transparency by estimating and communicating the uncertainty associated with model predictions. We describe how uncertainty can be used to mitigate model unfairness, augment decision-making, and build trustworthy systems. This work constitutes an interdisciplinary review drawn from literature spanning machine learning, visualization/HCI, design, decision-making, and fairness.
  arXiv  Detail & Related papers  (2020-11-15T17:26:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.