AI2030 Responsible AI Tools Marketplace

Key Takeaways

One-size-fits-all explanations don't work: The paper identifies a critical gap between the many available explainability methods and actual stakeholder needs, suggesting that organizations cannot simply pick any explanation technique—they must match the method to their specific use case and audience.
Different stakeholders need different explanations: The authors emphasize that explainability requirements vary depending on who needs the explanation (regulators, customers, affected individuals, etc.), yet most technical literature focuses on the algorithms themselves rather than understanding these diverse stakeholder needs.
A systematic methodology is needed: Rather than leaving explainability selection to chance, the paper advocates for a structured methodology that helps data scientists bridge the gap between what stakeholders actually need to understand and which explanation methods will best serve those needs.
Regulatory and public pressure is driving change: The paper notes that organizations are increasingly required to provide explainability due to regulatory initiatives and growing public awareness, making it urgent to develop practical guidance on how to implement explainability responsibly.
Characterizing both methods and requirements is essential: The authors contribute frameworks for documenting both the properties of different XAI methods and the specific requirements of users, suggesting that responsible AI implementation requires understanding both sides of this equation before making a choice.

Abstract

Explainability is becoming an important requirement for organizations that make use of automated decision-making due to regulatory initiatives and a shift in public awareness. Various and significantly different algorithmic methods to provide this explainability have been introduced in the field, but the existing literature in the machine learning community has paid little attention to the stakeholder whose needs are rather studied in the human-computer interface community. Therefore, organizations that want or need to provide this explainability are confronted with the selection of an appropriate method for their use case. In this paper, we argue there is a need for a methodology to bridge the gap between stakeholder needs and explanation methods. We present our ongoing work on creating this methodology to help data scientists in the process of providing explainability to stakeholders. In particular, our contributions include documents used to characterize XAI methods and user requirements (shown in Appendix), which our methodology builds upon.

Key Takeaways

One-size-fits-all explanations don't work: The paper identifies a critical gap between the many available explainability methods and actual stakeholder needs, suggesting that organizations cannot simply pick any explanation technique—they must match the method to their specific use case and audience.
Different stakeholders need different explanations: The authors emphasize that explainability requirements vary depending on who needs the explanation (regulators, customers, affected individuals, etc.), yet most technical literature focuses on the algorithms themselves rather than understanding these diverse stakeholder needs.
A systematic methodology is needed: Rather than leaving explainability selection to chance, the paper advocates for a structured methodology that helps data scientists bridge the gap between what stakeholders actually need to understand and which explanation methods will best serve those needs.
Regulatory and public pressure is driving change: The paper notes that organizations are increasingly required to provide explainability due to regulatory initiatives and growing public awareness, making it urgent to develop practical guidance on how to implement explainability responsibly.
Characterizing both methods and requirements is essential: The authors contribute frameworks for documenting both the properties of different XAI methods and the specific requirements of users, suggesting that responsible AI implementation requires understanding both sides of this equation before making a choice.

Abstract

How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links

How to choose an Explainability Method? Towards a Methodical Implementation of XAI in Practice

Key Takeaways

Abstract

Paper Info

Responsible AI Topics

Links