AI2030 Responsible AI Tools Marketplace

Key Takeaways

Explanations alone don't guarantee trustworthiness: While explainable ML is promoted as essential for trustworthy AI systems, the paper highlights that explanations are just one component—the field has not adequately addressed how explanations actually translate into genuine accountability and user trust in real-world applications.
Gap between academic research and industry needs: Many explanation techniques developed in academia don't address the practical challenges faced by industry practitioners, creating a significant barrier to widespread adoption of explainable ML in real business settings.
Stakeholder needs are often overlooked: The paper emphasizes that different stakeholders (regulators, end-users, data scientists, etc.) have different explanation needs, yet most explanation techniques are not designed with these diverse requirements in mind, limiting their practical utility.
Lack of standardized evaluation methods: The paper identifies that there are no widely agreed-upon standards for evaluating whether explanations are actually effective or useful, making it difficult for organizations to assess which explanation techniques work best for their specific contexts.
Explainable ML adoption remains limited despite hype: Despite significant academic interest and development of various explanation techniques, the paper notes that explainable ML has not achieved widespread adoption in industry, suggesting that current approaches may not adequately solve real-world problems.

Abstract

As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost importance. Explanations sit at the core of these desirable attributes of a ML system. The emerging field is frequently called ``Explainable AI (XAI)'' or ``Explainable ML.'' The goal of explainable ML is to intuitively explain the predictions of a ML system, while adhering to the needs to various stakeholders. Many explanation techniques were developed with contributions from both academia and industry. However, there are several existing challenges that have not garnered enough interest and serve as roadblocks to widespread adoption of explainable ML. In this short paper, we enumerate challenges in explainable ML from an industry perspective. We hope these challenges will serve as promising future research directions, and would contribute to democratizing explainable ML.

Presented at JOURNE workshop at MLSYS 2021 (https://sites.google.com/view/workshop-journe/home)

Key Takeaways

Explanations alone don't guarantee trustworthiness: While explainable ML is promoted as essential for trustworthy AI systems, the paper highlights that explanations are just one component—the field has not adequately addressed how explanations actually translate into genuine accountability and user trust in real-world applications.
Gap between academic research and industry needs: Many explanation techniques developed in academia don't address the practical challenges faced by industry practitioners, creating a significant barrier to widespread adoption of explainable ML in real business settings.
Stakeholder needs are often overlooked: The paper emphasizes that different stakeholders (regulators, end-users, data scientists, etc.) have different explanation needs, yet most explanation techniques are not designed with these diverse requirements in mind, limiting their practical utility.
Lack of standardized evaluation methods: The paper identifies that there are no widely agreed-upon standards for evaluating whether explanations are actually effective or useful, making it difficult for organizations to assess which explanation techniques work best for their specific contexts.
Explainable ML adoption remains limited despite hype: Despite significant academic interest and development of various explanation techniques, the paper notes that explainable ML has not achieved widespread adoption in industry, suggesting that current approaches may not adequately solve real-world problems.

Abstract

Presented at JOURNE workshop at MLSYS 2021 (https://sites.google.com/view/workshop-journe/home)

Pitfalls of Explainable ML: An Industry Perspective

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

Pitfalls of Explainable ML: An Industry Perspective

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links