PL
P. Lippmann
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1
Large language models (LMs) are increasingly used in critical tasks, making it important that these models can be trusted. The confidence an LM assigns to its prediction is often used to indicate how much trust can be placed in that prediction. However, a high confidence can be incorrectly trusted if it turns out to be incorrect, also known as a high-confident error, or unknown unknown (UU). Blind spots, clusters of UUs, are caused by out-of-distribution data (OOD), bias, covariate shift, or unseen data. Previous work by Lippmann et al. generated samples based on these UUs, however, they selected a random subset of all found UUs. Although this was the first study that could mitigate blind spots without the need for crowd workers or oracles, they did not address the underlying causes of blind spots, such as OOD and bias. Data valuation is the research field that is concerned with valuing samples to improve model performance and can identify OOD data, bias, and noise. This paper proposed to combine the research field of data valuation, with the mitigation of blind spots. We generated synthetic samples using the highest-valued samples and retrained the LM using a Weighted Loss based on Data Values (WLDV). We conducted an extensive evaluation of our approach on four tasks, demonstrating a reduction of UUs by up to 32.7%, while retaining the same level of accuracy. This was the first exploration of combining the research fields of data valuation and blind spot mitigation.
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Large language models (LMs) are increasingly used in critical tasks, making it important that these models can be trusted. The confidence an LM assigns to its prediction is often used to indicate how much trust can be placed in that prediction. However, a high confidence can be incorrectly trusted if it turns out to be incorrect, also known as a high-confident error, or unknown unknown (UU). Blind spots, clusters of UUs, are caused by out-of-distribution data (OOD), bias, covariate shift, or unseen data. Previous work by Lippmann et al. generated samples based on these UUs, however, they selected a random subset of all found UUs. Although this was the first study that could mitigate blind spots without the need for crowd workers or oracles, they did not address the underlying causes of blind spots, such as OOD and bias. Data valuation is the research field that is concerned with valuing samples to improve model performance and can identify OOD data, bias, and noise. This paper proposed to combine the research field of data valuation, with the mitigation of blind spots. We generated synthetic samples using the highest-valued samples and retrained the LM using a Weighted Loss based on Data Values (WLDV). We conducted an extensive evaluation of our approach on four tasks, demonstrating a reduction of UUs by up to 32.7%, while retaining the same level of accuracy. This was the first exploration of combining the research fields of data valuation and blind spot mitigation.
A 2022 Harvard Business Review report critically examines the readiness of AI for real-world decision-making. The report cited several incidents, like an experimental healthcare chatbot suggesting a mock patient commit suicide in response to their distress or when a self-driving car experiment was called off after it resulted in the death of a pedestrian.
These incidents, leading to media frenzies and public outcries, underscore a pressing concern: "How do these AI systems reach their conclusions?" It has created an urgent demand for transparency and clarity in AI decision-making processes. This urge to understand has translated into a significant uptick in the volume of work in Explainable AI (XAI). This makes it crucial to have consistent evaluation standards for streamlined growth in the field.
However, XAI, being a multidisciplinary field, faces the challenge of a lack of consensus on what constitutes a "good" explanation. Stakeholders with diverse backgrounds and needs can have diverging expectations from XAI. Some might prioritize simple and concise explanations, while others prioritize detailed information about AI predictions, all depending on their end goal.
This thesis addresses the standardization of an evaluation framework for XAI methods, that accounts for stakeholders' needs in different usage contexts. It presents a prototype that can be customized and extended to suit various XAI methods and tasks. Findings affirm the framework’s ability to yield insightful comparisons between different XAI methods. It also highlights issues with human perception of specific XAI features in those methods. The efforts in this work contribute to XAI techniques being integrated into real-world applications, ensuring more reliable and consistent performance assessment. ...
These incidents, leading to media frenzies and public outcries, underscore a pressing concern: "How do these AI systems reach their conclusions?" It has created an urgent demand for transparency and clarity in AI decision-making processes. This urge to understand has translated into a significant uptick in the volume of work in Explainable AI (XAI). This makes it crucial to have consistent evaluation standards for streamlined growth in the field.
However, XAI, being a multidisciplinary field, faces the challenge of a lack of consensus on what constitutes a "good" explanation. Stakeholders with diverse backgrounds and needs can have diverging expectations from XAI. Some might prioritize simple and concise explanations, while others prioritize detailed information about AI predictions, all depending on their end goal.
This thesis addresses the standardization of an evaluation framework for XAI methods, that accounts for stakeholders' needs in different usage contexts. It presents a prototype that can be customized and extended to suit various XAI methods and tasks. Findings affirm the framework’s ability to yield insightful comparisons between different XAI methods. It also highlights issues with human perception of specific XAI features in those methods. The efforts in this work contribute to XAI techniques being integrated into real-world applications, ensuring more reliable and consistent performance assessment. ...
A 2022 Harvard Business Review report critically examines the readiness of AI for real-world decision-making. The report cited several incidents, like an experimental healthcare chatbot suggesting a mock patient commit suicide in response to their distress or when a self-driving car experiment was called off after it resulted in the death of a pedestrian.
These incidents, leading to media frenzies and public outcries, underscore a pressing concern: "How do these AI systems reach their conclusions?" It has created an urgent demand for transparency and clarity in AI decision-making processes. This urge to understand has translated into a significant uptick in the volume of work in Explainable AI (XAI). This makes it crucial to have consistent evaluation standards for streamlined growth in the field.
However, XAI, being a multidisciplinary field, faces the challenge of a lack of consensus on what constitutes a "good" explanation. Stakeholders with diverse backgrounds and needs can have diverging expectations from XAI. Some might prioritize simple and concise explanations, while others prioritize detailed information about AI predictions, all depending on their end goal.
This thesis addresses the standardization of an evaluation framework for XAI methods, that accounts for stakeholders' needs in different usage contexts. It presents a prototype that can be customized and extended to suit various XAI methods and tasks. Findings affirm the framework’s ability to yield insightful comparisons between different XAI methods. It also highlights issues with human perception of specific XAI features in those methods. The efforts in this work contribute to XAI techniques being integrated into real-world applications, ensuring more reliable and consistent performance assessment.
These incidents, leading to media frenzies and public outcries, underscore a pressing concern: "How do these AI systems reach their conclusions?" It has created an urgent demand for transparency and clarity in AI decision-making processes. This urge to understand has translated into a significant uptick in the volume of work in Explainable AI (XAI). This makes it crucial to have consistent evaluation standards for streamlined growth in the field.
However, XAI, being a multidisciplinary field, faces the challenge of a lack of consensus on what constitutes a "good" explanation. Stakeholders with diverse backgrounds and needs can have diverging expectations from XAI. Some might prioritize simple and concise explanations, while others prioritize detailed information about AI predictions, all depending on their end goal.
This thesis addresses the standardization of an evaluation framework for XAI methods, that accounts for stakeholders' needs in different usage contexts. It presents a prototype that can be customized and extended to suit various XAI methods and tasks. Findings affirm the framework’s ability to yield insightful comparisons between different XAI methods. It also highlights issues with human perception of specific XAI features in those methods. The efforts in this work contribute to XAI techniques being integrated into real-world applications, ensuring more reliable and consistent performance assessment.
The state-of-the-art shows the potential of chatbots and other Machine Learning (ML) models to perform many tasks of high quality. Especially chatbots are already used by many companies to assist their customer service. However, chatbots will likely never be able to perform all tasks perfectly. Therefore, it is still the question whether such a chatbot is valuable for a business. Current research fails to describe how chatbots should be evaluated to compute the value of a chatbot for a business. In this research, we design an evaluation framework capturing the value of a chatbot in customer service. This framework consists of several key dimensions which should be computed in order to determine the value of the chatbot. To show that this evaluation framework captures the value of a chatbot, we perform a case study on water utility companies in The Netherlands. This case study showed the designed evaluation framework does capture the value of a chatbot in customer service.
...
The state-of-the-art shows the potential of chatbots and other Machine Learning (ML) models to perform many tasks of high quality. Especially chatbots are already used by many companies to assist their customer service. However, chatbots will likely never be able to perform all tasks perfectly. Therefore, it is still the question whether such a chatbot is valuable for a business. Current research fails to describe how chatbots should be evaluated to compute the value of a chatbot for a business. In this research, we design an evaluation framework capturing the value of a chatbot in customer service. This framework consists of several key dimensions which should be computed in order to determine the value of the chatbot. To show that this evaluation framework captures the value of a chatbot, we perform a case study on water utility companies in The Netherlands. This case study showed the designed evaluation framework does capture the value of a chatbot in customer service.
Master thesis
(2022)
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P.M. Lammerts, J. Yang, P. Lippmann, Y-C. Hsu, G.J.P.M. Houben, C.R.M.M. Oertel Genannt Bierbach
Hate speech detection on social media platforms remains a challenging task. Manual moderation by humans is the most reliable but infeasible, and machine learning models for detecting hate speech are scalable but unreliable as they often perform poorly on unseen data. Therefore, human-AI collaborative systems, in which we combine the strengths of humans' reliability and the scalability of machine learning, offer great potential for detecting hate speech. While methods for task handover in human-AI collaboration exist that consider the costs of incorrect predictions, insufficient attention has been paid to estimating these costs. In this work, we propose a value-sensitive rejector that automatically rejects machine learning predictions when the prediction's confidence is too low by taking into account the users' perception regarding different types of machine learning predictions. We conducted a crowdsourced survey study with 160 participants to evaluate their perception of correct, incorrect and rejected predictions in the context of hate speech detection. We introduce magnitude estimation, an unbounded scale, as the preferred method for measuring user perception of machine predictions. The results show that we can use magnitude estimation reliably for measuring the users' perception. We integrate the user-perceived values into the value-sensitive rejector and apply the rejector to several state-of-the-art hate speech detection models. The results show that the value-sensitive rejector can help us to determine when to accept or reject predictions to achieve optimal model value. Furthermore, the results show that the best model can be different when optimizing model value compared to optimizing more widely used metrics, such as accuracy.
...
Hate speech detection on social media platforms remains a challenging task. Manual moderation by humans is the most reliable but infeasible, and machine learning models for detecting hate speech are scalable but unreliable as they often perform poorly on unseen data. Therefore, human-AI collaborative systems, in which we combine the strengths of humans' reliability and the scalability of machine learning, offer great potential for detecting hate speech. While methods for task handover in human-AI collaboration exist that consider the costs of incorrect predictions, insufficient attention has been paid to estimating these costs. In this work, we propose a value-sensitive rejector that automatically rejects machine learning predictions when the prediction's confidence is too low by taking into account the users' perception regarding different types of machine learning predictions. We conducted a crowdsourced survey study with 160 participants to evaluate their perception of correct, incorrect and rejected predictions in the context of hate speech detection. We introduce magnitude estimation, an unbounded scale, as the preferred method for measuring user perception of machine predictions. The results show that we can use magnitude estimation reliably for measuring the users' perception. We integrate the user-perceived values into the value-sensitive rejector and apply the rejector to several state-of-the-art hate speech detection models. The results show that the value-sensitive rejector can help us to determine when to accept or reject predictions to achieve optimal model value. Furthermore, the results show that the best model can be different when optimizing model value compared to optimizing more widely used metrics, such as accuracy.