Paper Summary
Title: How Aligned are Generative Models to Humans in High-Stakes Decision-Making?
Source: arXiv (2 citations)
Authors: Sarah Tan et al.
Published Date: 2024-10-20
Podcast Transcript
Supporting Analysis
Findings:
The paper explores how large language models (LMs) compare to humans and the COMPAS predictive AI model in recidivism prediction. An intriguing finding is that LMs are not significantly better than humans or COMPAS in predicting recidivism, with the best performing LM, GPT 3.5 Turbo (when given race information), achieving an accuracy similar to humans. Notably, LMs align more closely with human decisions than with COMPAS, exhibiting a human-LM alignment ranging from 0.82 to 0.89, compared to a COMPAS-LM alignment of 0.63 to 0.67. Another surprising result is that when LMs are provided with both human and COMPAS decisions through in-context learning, they outperform humans or COMPAS alone, with Llama 3.2 achieving the highest accuracy of 0.677. Additionally, the presence of a photo reduces the number of predicted positives, suggesting an influence on LM decision-making similar to human behavior. However, bias mitigation techniques like anti-discrimination prompting can lead to unintended effects, such as drastically reducing the number of predicted positives, which highlights the complexity of effectively aligning LMs with human values without introducing new biases.
The paper explores how large language models (LMs) compare to humans and the COMPAS predictive AI model in recidivism prediction. An intriguing finding is that LMs are not significantly better than humans or COMPAS in predicting recidivism, with the best performing LM, GPT 3.5 Turbo (when given race information), achieving an accuracy similar to humans. Notably, LMs align more closely with human decisions than with COMPAS, exhibiting a human-LM alignment ranging from 0.82 to 0.89, compared to a COMPAS-LM alignment of 0.63 to 0.67. Another surprising result is that when LMs are provided with both human and COMPAS decisions through in-context learning, they outperform humans or COMPAS alone, with Llama 3.2 achieving the highest accuracy of 0.677. Additionally, the presence of a photo reduces the number of predicted positives, suggesting an influence on LM decision-making similar to human behavior. However, bias mitigation techniques like anti-discrimination prompting can lead to unintended effects, such as drastically reducing the number of predicted positives, which highlights the complexity of effectively aligning LMs with human values without introducing new biases.
Methods:
The research investigates the alignment of large generative models with human decisions in recidivism prediction. The study utilizes a dataset integrating three sources: COMPAS predictive AI risk scores, human judgments on recidivism, and photos. The dataset is employed to examine various state-of-the-art, multimodal large models. The researchers use a baseline prompt to ask models to predict recidivism, similar to what was provided to human workers in prior studies. They conduct several experiments including steerability, where models are prompted with additional context, such as human judgments and COMPAS scores, to see if alignment with these sources can be improved. Another experiment involves adding photos to the prompt to assess the impact on model alignment and accuracy. The study also tests bias mitigation techniques by prompting models to ignore protected characteristics and consider discrimination illegal. The researchers evaluate model responses by parsing them for 'yes', 'no', or 'refuse' answers and compare these to human and COMPAS decisions. They also examine the models' accuracy, alignment, and behavior metrics like refusal rate and predicted positives, across different demographic groups.
The research investigates the alignment of large generative models with human decisions in recidivism prediction. The study utilizes a dataset integrating three sources: COMPAS predictive AI risk scores, human judgments on recidivism, and photos. The dataset is employed to examine various state-of-the-art, multimodal large models. The researchers use a baseline prompt to ask models to predict recidivism, similar to what was provided to human workers in prior studies. They conduct several experiments including steerability, where models are prompted with additional context, such as human judgments and COMPAS scores, to see if alignment with these sources can be improved. Another experiment involves adding photos to the prompt to assess the impact on model alignment and accuracy. The study also tests bias mitigation techniques by prompting models to ignore protected characteristics and consider discrimination illegal. The researchers evaluate model responses by parsing them for 'yes', 'no', or 'refuse' answers and compare these to human and COMPAS decisions. They also examine the models' accuracy, alignment, and behavior metrics like refusal rate and predicted positives, across different demographic groups.
Strengths:
The research is compelling due to its exploration of how large generative models compare with humans and existing predictive AI in a high-stakes decision-making task, specifically recidivism prediction. By augmenting the COMPAS dataset with human judgments and hypothetical photos, the study provides a comprehensive dataset that allows for a multifaceted analysis of model behavior. The use of both text-only and multimodal settings enhances the robustness of the evaluation, offering insights into how models handle different types of input data. The researchers employed a methodical approach by systematically examining accuracy, alignment, and bias across various conditions, such as with and without the inclusion of race. The study also tested bias mitigation techniques, highlighting the researchers' commitment to addressing ethical concerns in AI decision-making. Additionally, experiments were designed to test the steerability of models through in-context learning, showcasing a thorough investigation into how models might be aligned with human or AI decisions. The study's adherence to best practices is evident in its transparent methodology, replication of previous results for validation, and exploration of human-AI complementarity, all of which contribute to a nuanced understanding of the potential and limitations of generative models in decision-making tasks.
The research is compelling due to its exploration of how large generative models compare with humans and existing predictive AI in a high-stakes decision-making task, specifically recidivism prediction. By augmenting the COMPAS dataset with human judgments and hypothetical photos, the study provides a comprehensive dataset that allows for a multifaceted analysis of model behavior. The use of both text-only and multimodal settings enhances the robustness of the evaluation, offering insights into how models handle different types of input data. The researchers employed a methodical approach by systematically examining accuracy, alignment, and bias across various conditions, such as with and without the inclusion of race. The study also tested bias mitigation techniques, highlighting the researchers' commitment to addressing ethical concerns in AI decision-making. Additionally, experiments were designed to test the steerability of models through in-context learning, showcasing a thorough investigation into how models might be aligned with human or AI decisions. The study's adherence to best practices is evident in its transparent methodology, replication of previous results for validation, and exploration of human-AI complementarity, all of which contribute to a nuanced understanding of the potential and limitations of generative models in decision-making tasks.
Limitations:
The research faces several limitations. Firstly, the ground truth data for recidivism outcomes can be noisy and not entirely reliable, affecting the accuracy metrics based on this data. Additionally, the COMPAS dataset, a key component of the study, has known issues such as potential biases and questionable meaningfulness of its ground truth definitions. Furthermore, the task of predicting recidivism is complex, and the study relies on laypeople's judgments instead of expert assessments, which might not provide the most accurate comparisons. Another limitation is the use of demographic-based photo matching, which could unintentionally reinforce stereotypes. The photos used, while ethically sourced, may not perfectly represent real-world defendants, potentially affecting the generalizability of the findings. Moreover, even when race is not explicitly mentioned, it can still be inferred from photos, introducing uncontrolled variables. Finally, the prompts used for LMs are not optimized for their capabilities, possibly limiting their performance. These factors combined make it challenging to draw definitive conclusions about the alignment between human judgments, predictive AI, and LMs in high-stakes decision-making contexts.
The research faces several limitations. Firstly, the ground truth data for recidivism outcomes can be noisy and not entirely reliable, affecting the accuracy metrics based on this data. Additionally, the COMPAS dataset, a key component of the study, has known issues such as potential biases and questionable meaningfulness of its ground truth definitions. Furthermore, the task of predicting recidivism is complex, and the study relies on laypeople's judgments instead of expert assessments, which might not provide the most accurate comparisons. Another limitation is the use of demographic-based photo matching, which could unintentionally reinforce stereotypes. The photos used, while ethically sourced, may not perfectly represent real-world defendants, potentially affecting the generalizability of the findings. Moreover, even when race is not explicitly mentioned, it can still be inferred from photos, introducing uncontrolled variables. Finally, the prompts used for LMs are not optimized for their capabilities, possibly limiting their performance. These factors combined make it challenging to draw definitive conclusions about the alignment between human judgments, predictive AI, and LMs in high-stakes decision-making contexts.
Applications:
The research on generative models and their alignment with human decision-making could have several practical applications, especially in fields where high-stakes decisions are common. One potential application is in the criminal justice system, where these models could be used to assist with recidivism predictions, potentially supporting judges or parole boards in making more informed decisions. In healthcare, generative models might aid in diagnostic processes or risk assessments, aligning with human experts to improve accuracy and reduce biases. Additionally, these models could be applied in educational settings, aiding in grading or evaluating student performance by aligning with human educators' judgments. Another area of application could be in financial services, where models could help assess credit risks or loan approvals, ensuring decisions are fair and unbiased. Moreover, the research could inform the development of more ethical AI systems by integrating human-like decision-making processes into the models, ensuring they align with societal values and ethical norms. The emphasis on reducing bias and improving alignment with human decisions could lead to more trustworthy AI applications across various domains.
The research on generative models and their alignment with human decision-making could have several practical applications, especially in fields where high-stakes decisions are common. One potential application is in the criminal justice system, where these models could be used to assist with recidivism predictions, potentially supporting judges or parole boards in making more informed decisions. In healthcare, generative models might aid in diagnostic processes or risk assessments, aligning with human experts to improve accuracy and reduce biases. Additionally, these models could be applied in educational settings, aiding in grading or evaluating student performance by aligning with human educators' judgments. Another area of application could be in financial services, where models could help assess credit risks or loan approvals, ensuring decisions are fair and unbiased. Moreover, the research could inform the development of more ethical AI systems by integrating human-like decision-making processes into the models, ensuring they align with societal values and ethical norms. The emphasis on reducing bias and improving alignment with human decisions could lead to more trustworthy AI applications across various domains.