thông tin chi tiết - Explainable artificial intelligence - # Explanations for text generation tasks

Evaluating the Effectiveness of Human and Machine Explanations for Incorrect Predictions of Large Language Models

Q: How can we design explanations that effectively communicate the limitations and uncertainties of AI models, especially for incorrect predictions?

To design explanations that effectively communicate the limitations and uncertainties of AI models, especially for incorrect predictions, several strategies can be employed: Transparency: Clearly communicate the underlying processes and assumptions of the AI model to users. This includes explaining the data used for training, the model architecture, and the inherent limitations of the model. Uncertainty Quantification: Provide measures of uncertainty along with predictions. This can be done through techniques like confidence intervals or probabilistic models that convey the model's confidence in its predictions. Highlighting Weaknesses: Explicitly point out the areas where the model may struggle or make errors. This can help users understand the limitations of the AI system and manage their expectations accordingly. Contrastive Explanations: Show contrasting examples where the model's prediction was incorrect and explain why it went wrong. This can help users understand the boundaries of the model's capabilities. Interactive Explanations: Allow users to interact with the explanations, ask questions, and explore different scenarios to gain a deeper understanding of the model's limitations. By incorporating these strategies, explanations can effectively convey the uncertainties and limitations of AI models, fostering trust and understanding among users.

Q: What are the potential long-term effects of explanation confirmation bias on human trust and reliance on AI systems?

Explanation confirmation bias, where users tend to accept explanations that align with their preconceived beliefs or expectations, can have several long-term effects on human trust and reliance on AI systems: Over-Reliance: If users consistently accept explanations that confirm their biases, they may become overly reliant on AI systems without critically evaluating the accuracy of the predictions. This can lead to blind trust in the AI's decisions. Decreased Accountability: When users consistently accept explanations that confirm their biases, they may be less likely to hold the AI system accountable for errors or biases in its predictions. This can result in a lack of transparency and accountability in AI decision-making. Limited Learning: Confirmation bias can hinder users' ability to learn from incorrect predictions and improve their understanding of the AI system. Users may be less inclined to question the model's outputs and seek alternative explanations. Erosion of Trust: Over time, confirmation bias can erode trust in AI systems, especially if users perceive that the explanations provided are biased or misleading. This can lead to skepticism and decreased reliance on AI technologies. To mitigate the long-term effects of explanation confirmation bias, it is essential to promote critical thinking, encourage users to seek diverse perspectives, and provide balanced and transparent explanations that challenge preconceived notions.

Q: How can we leverage human-generated explanations to improve the transparency and interpretability of large language models beyond just mimicking their outputs?

To leverage human-generated explanations to improve the transparency and interpretability of large language models beyond just mimicking their outputs, the following approaches can be considered: Diverse Perspectives: Incorporate a variety of human-generated explanations to capture different interpretations and insights into the model's outputs. This can provide a more comprehensive understanding of the AI system's decision-making process. Contextual Understanding: Encourage users to provide explanations that contextualize the model's outputs within the broader context of the task or domain. This can help users grasp the reasoning behind the AI's predictions more effectively. Interactive Explanations: Enable users to interact with human-generated explanations, ask questions, and explore different aspects of the model's outputs. This interactive approach can enhance transparency and facilitate deeper insights into the AI system. Explainability Tools: Develop tools that facilitate the generation and visualization of human explanations alongside the model's outputs. These tools can help users compare and contrast human and machine-generated explanations for a more nuanced understanding. Feedback Mechanisms: Implement feedback mechanisms that allow users to provide input on the quality and relevance of human-generated explanations. This feedback can help improve the interpretability of large language models over time. By leveraging human-generated explanations in these ways, we can enhance the transparency and interpretability of large language models, fostering trust and understanding among users.

Khái niệm cốt lõi

Explanations, whether generated by humans or machines, can lead to over-reliance on incorrect AI predictions when the explanations are perceived as helpful, highlighting the dilemma of AI errors.

Tóm tắt

The study explored the effectiveness of human-generated and machine-generated explanations for text generation tasks, specifically question-answering using the SQuAD 1.1v dataset.

Key highlights:

156 human-generated text and saliency-based explanations were collected and analyzed. Human saliency maps had little overlap with machine explanations, and human text explanations mostly copied or paraphrased source text.
In a large human-participant study (N=136), the correctness of AI predictions had a strong, significant effect on all measures (performance, time, quality, helpfulness, and mental effort). Machine saliency maps were significantly less helpful than human saliency maps.
Participants trusted text extractions more than ChatGPT explanations. Measures of explanation satisfaction, trust in the AI, and explanation helpfulness were negatively correlated with performance scores.
The findings highlight the dilemma of machine explanations: "good" explanations for incorrect AI predictions can lead to over-reliance on AI, resulting in decreased performance. Participants may try to match their intuition of relevance with saliency maps, leading to explanation confirmation bias.

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Trích dẫn

"good" explanations for incorrect AI predictions can lead to over-reliance on AI resulting in decreased performance
participants may try to match their intuition of relevance with saliency maps themselves as a heuristic mechanism to evaluate explanations

Thông tin chi tiết chính được chắt lọc từ

Unraveling the Dilemma of AI Errors

by Marvin Pafla... lúc arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07725.pdf

Yêu cầu sâu hơn

How can we design explanations that effectively communicate the limitations and uncertainties of AI models, especially for incorrect predictions?

To design explanations that effectively communicate the limitations and uncertainties of AI models, especially for incorrect predictions, several strategies can be employed:

Transparency: Clearly communicate the underlying processes and assumptions of the AI model to users. This includes explaining the data used for training, the model architecture, and the inherent limitations of the model.

Uncertainty Quantification: Provide measures of uncertainty along with predictions. This can be done through techniques like confidence intervals or probabilistic models that convey the model's confidence in its predictions.

Highlighting Weaknesses: Explicitly point out the areas where the model may struggle or make errors. This can help users understand the limitations of the AI system and manage their expectations accordingly.

Contrastive Explanations: Show contrasting examples where the model's prediction was incorrect and explain why it went wrong. This can help users understand the boundaries of the model's capabilities.

Interactive Explanations: Allow users to interact with the explanations, ask questions, and explore different scenarios to gain a deeper understanding of the model's limitations.

By incorporating these strategies, explanations can effectively convey the uncertainties and limitations of AI models, fostering trust and understanding among users.

What are the potential long-term effects of explanation confirmation bias on human trust and reliance on AI systems?

Explanation confirmation bias, where users tend to accept explanations that align with their preconceived beliefs or expectations, can have several long-term effects on human trust and reliance on AI systems:

Over-Reliance: If users consistently accept explanations that confirm their biases, they may become overly reliant on AI systems without critically evaluating the accuracy of the predictions. This can lead to blind trust in the AI's decisions.

Decreased Accountability: When users consistently accept explanations that confirm their biases, they may be less likely to hold the AI system accountable for errors or biases in its predictions. This can result in a lack of transparency and accountability in AI decision-making.

Limited Learning: Confirmation bias can hinder users' ability to learn from incorrect predictions and improve their understanding of the AI system. Users may be less inclined to question the model's outputs and seek alternative explanations.

Erosion of Trust: Over time, confirmation bias can erode trust in AI systems, especially if users perceive that the explanations provided are biased or misleading. This can lead to skepticism and decreased reliance on AI technologies.

To mitigate the long-term effects of explanation confirmation bias, it is essential to promote critical thinking, encourage users to seek diverse perspectives, and provide balanced and transparent explanations that challenge preconceived notions.

How can we leverage human-generated explanations to improve the transparency and interpretability of large language models beyond just mimicking their outputs?

To leverage human-generated explanations to improve the transparency and interpretability of large language models beyond just mimicking their outputs, the following approaches can be considered:

Diverse Perspectives: Incorporate a variety of human-generated explanations to capture different interpretations and insights into the model's outputs. This can provide a more comprehensive understanding of the AI system's decision-making process.

Contextual Understanding: Encourage users to provide explanations that contextualize the model's outputs within the broader context of the task or domain. This can help users grasp the reasoning behind the AI's predictions more effectively.

Interactive Explanations: Enable users to interact with human-generated explanations, ask questions, and explore different aspects of the model's outputs. This interactive approach can enhance transparency and facilitate deeper insights into the AI system.

Explainability Tools: Develop tools that facilitate the generation and visualization of human explanations alongside the model's outputs. These tools can help users compare and contrast human and machine-generated explanations for a more nuanced understanding.

Feedback Mechanisms: Implement feedback mechanisms that allow users to provide input on the quality and relevance of human-generated explanations. This feedback can help improve the interpretability of large language models over time.

By leveraging human-generated explanations in these ways, we can enhance the transparency and interpretability of large language models, fostering trust and understanding among users.