Social Explainable AI: Communications of Nii Shonan Meetings
暫譯: 社會可解釋的人工智慧：Nii Shonan 會議通訊
Rohlfing, Katharina, Lim, Brian, Thommes, Kirsten
相關主題
商品描述
This open access book introduces social aspects relevant to research and developments of explainable AI (XAI). The new surge of XAI responds to the societal challenge that many algorithmic approaches (such as machine learning or autonomous intelligent systems) are rapidly increasing in complexity, making justified use of their recommendations difficult for the users. A large body of approaches now exists with many ideas of how algorithms should be explainable or even be able to explain their output. However, few of them consider the users' perspective, and even less address the social aspects of using XAI. To fill the gap, the book offers a conceptualization of explainability as a social practice, a framework for contextual factors, and an operationalization of users' involvement in creating relevant explanations.
For this, scholars across disciplines gathered at the Shonan meeting to account for how explanation generation can be tailored to diverse users and their heterogeneous goals when interacting with XAI. As a result, social interaction is the key to the involvement of the users. Accordingly, we define sXAI (social eXplainable AI) as systems that interact with the users in such a way that an incremental adaptation of explaining to the users is possible, along with the unfolding context of interaction, to yield a relevant explanation at the interface with both active partners--human and AI. To encourage novel interdisciplinary research, we propose to account for the following dimensions:
- Patterndness: XAI should account for different contexts that yield different social roles impacting the construction of explanations.
- Incrementality: XAI should build on the contribution of the involved partners who adapt to each other.
- Multimodality: XAI needs to use different communication modalities (e.g., visual, verbal, and auditory).
This book also addresses how to evaluate social XAI systems and what ethical aspects must be considered when employing sXAI. Together, the book pushes forward the building of a community interested in sXAI. To increase the readability across disciplines, each chapter offers a rapid access to its content.
商品描述(中文翻譯)
這本開放存取的書籍介紹了與可解釋人工智慧（XAI）研究和發展相關的社會層面。XAI的新一波浪潮回應了社會挑戰，許多算法方法（如機器學習或自主智能系統）正迅速增加其複雜性，使得用戶難以合理地使用其建議。目前已經存在大量方法，提出了算法應該如何可解釋，甚至能夠解釋其輸出的許多想法。然而，少數方法考慮了用戶的視角，更少數則針對使用XAI的社會層面進行探討。為了填補這一空白，本書提供了可解釋性作為社會實踐的概念化、一個針對情境因素的框架，以及用戶參與創建相關解釋的操作化。
為此，來自不同學科的學者們在湘南會議上聚集，探討如何根據不同用戶及其異質目標來定制解釋生成，當他們與XAI互動時。因此，社會互動是用戶參與的關鍵。因此，我們將sXAI（社會可解釋人工智慧）定義為與用戶互動的系統，這樣可以根據用戶的需求逐步調整解釋，並隨著互動的情境展開，在人類和AI這兩個主動夥伴的界面上提供相關的解釋。為了鼓勵新穎的跨學科研究，我們建議考慮以下幾個維度：
- 模式性：XAI應考慮不同的情境，這些情境會產生不同的社會角色，影響解釋的構建。
- 漸進性：XAI應基於參與夥伴的貢獻，這些夥伴彼此適應。
- 多模態性：XAI需要使用不同的溝通模態（例如，視覺、口頭和聽覺）。
本書還探討了如何評估社會XAI系統，以及在使用sXAI時必須考慮的倫理方面。總體而言，本書推動了對sXAI感興趣的社群的建立。為了提高跨學科的可讀性，每一章都提供了快速訪問其內容的方式。
作者簡介
This open access book introduces social aspects relevant to research and developments of explainable AI (XAI). The new surge of XAI responds to the societal challenge that many algorithmic approaches (such as machine learning or autonomous intelligent systems) are rapidly increasing in complexity, making justified use of their recommendations difficult for the users. A large body of approaches now exists with many ideas of how algorithms should be explainable or even be able to explain their output. However, few of them consider the users' perspective, and even less address the social aspects of using XAI. To fill the gap, the book offers a conceptualization of explainability as a social practice, a framework for contextual factors, and an operationalization of users' involvement in creating relevant explanations.
For this, scholars across disciplines gathered at the Shonan meeting to account for how explanation generation can be tailored to diverse users and their heterogeneous goals when interacting with XAI. As a result, social interaction is the key to the involvement of the users. Accordingly, we define sXAI (social eXplainable AI) as systems that interact with the users in such a way that an incremental adaptation of explaining to the users is possible, along with the unfolding context of interaction, to yield a relevant explanation at the interface with both active partners--human and AI. To encourage novel interdisciplinary research, we propose to account for the following dimensions:
- Patterndness: XAI should account for different contexts that yield different social roles impacting the construction of explanations.
- Incrementality: XAI should build on the contribution of the involved partners who adapt to each other.
- Multimodality: XAI needs to use different communication modalities (e.g., visual, verbal, and auditory).
This book also addresses how to evaluate social XAI systems and what ethical aspects must be considered when employing sXAI. Together, the book pushes forward the building of a community interested in sXAI. To increase the readability across disciplines, each chapter offers a rapid access to its content.
This open access book introduces social aspects relevant to research and developments of explainable AI (XAI). The new surge of XAI responds to the societal challenge that many algorithmic approaches (such as machine learning or autonomous intelligent systems) are rapidly increasing in complexity, making justified use of their recommendations difficult for the users. A large body of approaches now exists with many ideas of how algorithms should be explainable or even be able to explain their output. However, few of them consider the users' perspective, and even less address the social aspects of using XAI. To fill the gap, the book offers a conceptualization of explainability as a social practice, a framework for contextual factors, and an operationalization of users' involvement in creating relevant explanations.
For this, scholars across disciplines gathered at the Shonan meeting to account for how explanation generation can be tailored to diverse users and their heterogeneous goals when interacting with XAI. As a result, social interaction is the key to the involvement of the users. Accordingly, we define sXAI (social eXplainable AI) as systems that interact with the users in such a way that an incremental adaptation of explaining to the users is possible, along with the unfolding context of interaction, to yield a relevant explanation at the interface with both active partners--human and AI. To encourage novel interdisciplinary research, we propose to account for the following dimensions:
- Patterndness: XAI should account for different contexts that yield different social roles impacting the construction of explanations.
- Incrementality: XAI should build on the contribution of the involved partners who adapt to each other.
- Multimodality: XAI needs to use different communication modalities (e.g., visual, verbal, and auditory).
This book also addresses how to evaluate social XAI systems and what ethical aspects must be considered when employing sXAI. Together, the book pushes forward the building of a community interested in sXAI. To increase the readability across disciplines, each chapter offers a rapid access to its content.
作者簡介(中文翻譯)
這本開放存取的書籍介紹了與可解釋人工智慧（XAI）研究和發展相關的社會面向。XAI的新一波浪潮回應了社會挑戰，許多算法方法（如機器學習或自主智能系統）正迅速增加其複雜性，使得用戶難以合理地使用其建議。目前已經存在大量方法，提出了許多關於算法應如何可解釋或甚至能夠解釋其輸出的想法。然而，這些方法中很少考慮用戶的視角，更少有針對使用XAI的社會面向進行探討。為了填補這一空白，本書提供了可解釋性的社會實踐概念化、一個上下文因素的框架，以及用戶參與創建相關解釋的操作化。
為此，來自不同學科的學者在湘南會議上聚集，探討如何根據多樣的用戶及其異質目標來定制解釋生成，當他們與XAI互動時。因此，社會互動是用戶參與的關鍵。因此，我們將sXAI（社會可解釋人工智慧）定義為與用戶互動的系統，這樣可以在互動的上下文中逐步適應用戶的解釋，以便在與兩個主動夥伴——人類和AI的界面上提供相關的解釋。為了鼓勵新穎的跨學科研究，我們建議考慮以下幾個維度：
- 模式性：XAI應考慮不同的上下文，這些上下文產生不同的社會角色，影響解釋的構建。
- 漸進性：XAI應基於參與夥伴的貢獻，這些夥伴彼此適應。
- 多模態性：XAI需要使用不同的溝通模態（例如，視覺、口頭和聽覺）。
本書還探討了如何評估社會XAI系統，以及在使用sXAI時必須考慮的倫理方面。總體而言，本書推動了對sXAI感興趣的社群的建立。為了提高跨學科的可讀性，每一章都提供了快速訪問其內容的方式。
