What design principles should be take into consideration for designing transparent and explainable AI?
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When designing transparent and explainable AI systems, several design principles should be considered. Firstly, involving users in the design process of local model-agnostic explanations is crucial. Secondly, ensuring consistency in feature attribution explanations among users in distributed machine learning scenarios is essential. Thirdly, integrating interpretable components and visualization techniques can enhance the transparency and trustworthiness of deep learning models. Additionally, utilizing interpretability methods and transparency frameworks to explain and assess AI functionality and impact is vital. Lastly, incorporating user-specific and situational criteria into the design of XAI systems is important for improving subjective perception and objective usability. By adhering to these principles, AI systems can become more transparent, trustworthy, and understandable to users.
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| Papers (5) | Insight |
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| Design principles for transparent and explainable AI include integrating interpretable components, visualization techniques like saliency maps, feature attribution, and LIME, ensuring model agnosticism, and enhancing user trust through comprehensive insights. | |
| Design principles for transparent AI include using feature attribution methods like SHAP and model-agnostic algorithms such as KernelSHAP in privacy-preserving distributed machine learning to ensure consistent explanations among users. | |
| Design principles for transparent AI include involving users in local explanation representation design, considering user preferences influenced by individual characteristics like gender and AI experience, and context-dependent explanation selection. | |
12 Jan 2023 | Design principles for transparent and explainable AI should consider user-specific criteria, situational factors, and the impact of user experience on the perception and usefulness of explanations. |
| Design principles for transparent and explainable AI include interpretability methods to explain AI functionality and transparency frameworks to assess datasets, models, and applications, enhancing understanding and accountability. |
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