Explainable Artificial Intelligence Approaches: A Survey

• URL: http://arxiv.org/abs/2101.09429v1
• Date: Sat, 23 Jan 2021 06:15:34 GMT
• Title: Explainable Artificial Intelligence Approaches: A Survey
• Authors: Sheikh Rabiul Islam, William Eberle, Sheikh Khaled Ghafoor, Mohiuddin Ahmed
• Abstract summary: Lack of explainability of a decision from an Artificial Intelligence based "black box" system/model is a key stumbling block for adopting AI in high stakes applications. We demonstrate popular Explainable Artificial Intelligence (XAI) methods with a mutual case study/task. We analyze for competitive advantages from multiple perspectives. We recommend paths towards responsible or human-centered AI using XAI as a medium.
• Score: 0.22940141855172028
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The lack of explainability of a decision from an Artificial Intelligence (AI) based "black box" system/model, despite its superiority in many real-world applications, is a key stumbling block for adopting AI in many high stakes applications of different domain or industry. While many popular Explainable Artificial Intelligence (XAI) methods or approaches are available to facilitate a human-friendly explanation of the decision, each has its own merits and demerits, with a plethora of open challenges. We demonstrate popular XAI methods with a mutual case study/task (i.e., credit default prediction), analyze for competitive advantages from multiple perspectives (e.g., local, global), provide meaningful insight on quantifying explainability, and recommend paths towards responsible or human-centered AI using XAI as a medium. Practitioners can use this work as a catalog to understand, compare, and correlate competitive advantages of popular XAI methods. In addition, this survey elicits future research directions towards responsible or human-centric AI systems, which is crucial to adopt AI in high stakes applications.

Related papers

• Imagining and building wise machines: The centrality of AI metacognition [78.76893632793497]
  We argue that shortcomings stem from one overarching failure: AI systems lack wisdom. While AI research has focused on task-level strategies, metacognition is underdeveloped in AI systems. We propose that integrating metacognitive capabilities into AI systems is crucial for enhancing their robustness, explainability, cooperation, and safety.
  arXiv  Detail & Related papers  (2024-11-04T18:10:10Z)
• Raising the Stakes: Performance Pressure Improves AI-Assisted Decision Making [57.53469908423318]
  We show the effects of performance pressure on AI advice reliance when laypeople complete a common AI-assisted task. We find that when the stakes are high, people use AI advice more appropriately than when stakes are lower, regardless of the presence of an AI explanation.
  arXiv  Detail & Related papers  (2024-10-21T22:39:52Z)
• Study on the Helpfulness of Explainable Artificial Intelligence [0.0]
  Legal, business, and ethical requirements motivate using effective XAI. We propose to evaluate XAI methods via the user's ability to successfully perform a proxy task. In other words, we address the helpfulness of XAI for human decision-making.
  arXiv  Detail & Related papers  (2024-10-14T14:03:52Z)
• Combining AI Control Systems and Human Decision Support via Robustness and Criticality [53.10194953873209]
  We extend a methodology for adversarial explanations (AE) to state-of-the-art reinforcement learning frameworks. We show that the learned AI control system demonstrates robustness against adversarial tampering. In a training / learning framework, this technology can improve both the AI's decisions and explanations through human interaction.
  arXiv  Detail & Related papers  (2024-07-03T15:38:57Z)
• Investigating the Role of Explainability and AI Literacy in User Compliance [2.8623940003518156]
  We find that users' compliance increases with the introduction of XAI but is also affected by AI literacy. We also find that the relationships between AI literacy XAI and users' compliance are mediated by the users' mental model of AI.
  arXiv  Detail & Related papers  (2024-06-18T14:28:12Z)
• Applications of Explainable artificial intelligence in Earth system science [12.454478986296152]
  This review aims to provide a foundational understanding of explainable AI (XAI) XAI offers a set of powerful tools that make the models more transparent. We identify four significant challenges that XAI faces within the Earth system science (ESS) A visionary outlook for ESS envisions a harmonious blend where process-based models govern the known, AI models explore the unknown, and XAI bridges the gap by providing explanations.
  arXiv  Detail & Related papers  (2024-06-12T15:05:29Z)
• Connecting Algorithmic Research and Usage Contexts: A Perspective of Contextualized Evaluation for Explainable AI [65.44737844681256]
  A lack of consensus on how to evaluate explainable AI (XAI) hinders the advancement of the field. We argue that one way to close the gap is to develop evaluation methods that account for different user requirements.
  arXiv  Detail & Related papers  (2022-06-22T05:17:33Z)
• A User-Centred Framework for Explainable Artificial Intelligence in Human-Robot Interaction [70.11080854486953]
  We propose a user-centred framework for XAI that focuses on its social-interactive aspect. The framework aims to provide a structure for interactive XAI solutions thought for non-expert users.
  arXiv  Detail & Related papers  (2021-09-27T09:56:23Z)
• Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
  In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society. A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests. This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
  arXiv  Detail & Related papers  (2021-06-25T22:31:55Z)
• The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions [4.169915659794568]
  People need more awareness of how AI works and its outcomes to build a relationship with that system. In decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system.
  arXiv  Detail & Related papers  (2021-02-10T14:28:34Z)

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.