Norm Inconsistency in Large Language Models: Evidence from Amazon Ring Surveillance Videos

Table of Contents

Overall Summary

Overview

This research paper investigates the phenomenon of "norm inconsistency" in Large Language Models (LLMs), where models apply different norms in similar situations, leading to potentially biased and unreliable decisions. The study focuses on the high-stakes application of deciding whether to call the police based on Amazon Ring surveillance videos, analyzing the responses of three LLMs (GPT-4, Gemini 1.0, and Claude 3 Sonnet) to assess their decision-making patterns and potential biases related to neighborhood demographics and subject characteristics.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 2

Description: Illustrates the probability of LLMs flagging videos for police intervention based on the presence or absence of annotated crime and neighborhood racial demographics, visually demonstrating the core issue of norm inconsistency and potential biases.

Relevance: Central to the paper's argument, highlighting discrepancies between LLM recommendations and ground truth, as well as potential racial biases in decision-making.

Table 4

Description: Presents coefficients from linear models predicting the likelihood of LLMs recommending police intervention, quantifying the relationship between various factors and LLM decisions.

Relevance: Provides insights into the factors influencing LLM decision-making and helps identify potential biases and inconsistencies.

Conclusion

This research provides compelling evidence of norm inconsistency in LLMs, particularly in the context of surveillance and police intervention. The findings reveal potential biases related to neighborhood demographics and highlight the challenges of mitigating bias in complex and opaque AI systems. The study underscores the need for greater transparency in LLM decision-making, the development of more robust bias mitigation strategies, and further research into the normative behavior of LLMs to ensure their equitable and responsible development and deployment in high-stakes domains.

Section Analysis

Abstract

Overview

This abstract investigates the issue of "norm inconsistency" in Large Language Models (LLMs), specifically focusing on their application in deciding whether to call the police based on Amazon Ring surveillance videos. The research analyzes the decisions of three LLMs (GPT-4, Gemini 1.0, and Claude 3 Sonnet) concerning the activities in the videos, subject demographics (skin-tone and gender), and neighborhood characteristics. The study reveals significant inconsistencies in the models' recommendations to call the police, highlighting discrepancies between their assessment of criminal activity and potential biases influenced by neighborhood racial demographics.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

The introduction section of this research paper establishes the concept of "norm inconsistency" in Large Language Models (LLMs) and its potential impact on high-stakes decision-making, particularly in the context of surveillance and law enforcement. It highlights the concern that LLMs may apply different norms in similar situations, leading to unreliable and potentially biased outcomes. The authors focus on the specific application of deciding whether to call the police based on home surveillance videos, emphasizing the need to understand how LLMs make normative judgments in real-world scenarios.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 1

Figure 1 illustrates an example of norm-inconsistency in GPT-4, where the model doesn't identify a crime but suggests calling the police. The figure depicts a still image from a Ring surveillance video showing a person at a home's entrance, a scenario that human annotators didn't label as criminal.

First Mention

Text: "Figure 1: Example of norm-inconsistency in GPT-4 where the model says no crime occurred but recommends police intervention. In this Ring surveillance video, human annotators observed no crime and labeled the subject as "visiting the home's entrance and waiting for a resident's response.""

Context: The authors are introducing the concept of norm inconsistency in LLMs, particularly in the context of surveillance and law enforcement, where it can lead to contradictory recommendations for police intervention.

Relevance: This figure is highly relevant as it visually demonstrates the core issue of norm inconsistency that the paper aims to address. It provides a concrete example of how LLMs can make contradictory recommendations, raising concerns about their reliability in real-world applications.

Critique
Visual Aspects
  • The figure would be more impactful if it included the actual still image from the surveillance video, allowing readers to visualize the scenario described.
  • Consider using visual cues, such as highlighting or color-coding, to draw attention to the specific aspects of the image that are relevant to the model's contradictory recommendations.
Analytical Aspects
  • The caption clearly explains the scenario and the model's response, but it could benefit from a more detailed explanation of why this specific example demonstrates norm inconsistency.
  • It would be helpful to connect the figure to the broader argument about the potential consequences of such inconsistencies in real-world surveillance applications.
Numeric Data

Background and Related Work

Overview

This section provides context and positions the research within the existing literature on normative decision-making in LLMs, bias in LLMs, AI for surveillance, and the specific platform used for data collection, Amazon Ring. It emphasizes the novelty of the study as one of the first to evaluate LLM normative decision-making using real-world data and in the context of surveillance.

Key Aspects

Strengths

Suggestions for Improvement

Data and Methods

Overview

This section details the methodology used to evaluate the decision-making of LLMs in the context of home surveillance videos. It describes the dataset of Amazon Ring videos, the annotation process, the selection of LLMs and prompts, and the approach to analyzing LLM responses.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 1

This table provides a breakdown of the 928 videos used in the study, categorized by various annotations like gender, skin tone, setting (day/night), metro area, and census tract race. It presents the count and percentage of videos for each label within its category.

First Mention

Text: "Table 1: Video counts by annotation and location categories."

Context: The authors are describing their data collection and annotation procedures, detailing the criteria used to select a subset of videos from a larger dataset of Amazon Ring footage.

Relevance: Table 1 is crucial for understanding the composition of the dataset used in the study. It provides transparency about the distribution of videos across different demographic and contextual categories, allowing readers to assess the representativeness of the sample.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear labels for categories and labels. The inclusion of both counts and percentages enhances clarity.
Analytical Aspects
  • The table effectively summarizes the distribution of videos across key annotation categories. However, it might be beneficial to include a brief discussion of any potential limitations or biases in the sampling process that might have influenced the observed distribution.
Numeric Data
  • Man: 751
  • Woman: 177
  • Light-Skin: 660
  • Dark-Skin: 268
  • Day: 633
  • Night: 295
  • Los Angeles: 333
  • San Francisco: 315
  • New York: 280
  • Majority-White: 536
  • Majority-Minority: 392
  • Man % of Total: 80.9 %
  • Woman % of Total: 19.1 %
  • Light-Skin % of Total: 71.1 %
  • Dark-Skin % of Total: 28.9 %
  • Day % of Total: 68.2 %
  • Night % of Total: 31.8 %
  • Los Angeles % of Total: 35.9 %
  • San Francisco % of Total: 33.9 %
  • New York % of Total: 30.2 %
  • Majority-White % of Total: 57.8 %
  • Majority-Minority % of Total: 42.2 %
Table 2

This table outlines the six activity types used to annotate the videos, providing a description of each activity and the corresponding count and percentage within the 928-video sample. It also indicates whether each activity type is classified as a crime.

First Mention

Text: "Table 2: Activity types, descriptions, and annotated counts among the 928 videos in our sample."

Context: The authors are discussing their annotation procedure, explaining how they categorized the activities depicted in the videos and whether those activities constitute a crime.

Relevance: Table 2 is essential for understanding the types of activities analyzed in the study and how they were classified in terms of criminal behavior. This information is crucial for interpreting the LLMs' responses and assessing their decision-making accuracy.

Critique
Visual Aspects
  • The table is well-structured and clear. The descriptions of activity types are concise and informative.
Analytical Aspects
  • The table effectively presents the activity types and their classification as crime or not. However, it might be beneficial to include a brief discussion of the criteria used to determine whether an activity constitutes a crime, especially for borderline cases.
Numeric Data
  • Entryway Waits: 304
  • Entryway Leaves: 177
  • Talks to Resident: 82
  • Theft: 232
  • Break-In (Vehicle): 62
  • Break-In (Home): 71
  • Entryway Waits % of Total: 32.8 %
  • Entryway Leaves % of Total: 19.1 %
  • Talks to Resident % of Total: 8.8 %
  • Theft % of Total: 25.0 %
  • Break-In (Vehicle) % of Total: 6.7 %
  • Break-In (Home) % of Total: 7.7 %
Table 3

This table presents the response counts for three different LLMs (GPT-4, Gemini, and Claude) to two prompts: "Is there a crime happening?" and "Should the police be called?" The responses are categorized as Yes, No, Ambiguous, or Refusal.

First Mention

Text: "Table 3: Response counts to each prompt across the 928 videos and 3 iterations/video."

Context: The authors are explaining their methodology for prompting the LLMs, detailing the two prompts used and the different response types observed from the models.

Relevance: Table 3 is central to the study's findings, as it quantifies the LLMs' responses to the key prompts. This data forms the basis for analyzing the models' decision-making patterns, including the frequency of recommending police intervention and the level of agreement between models.

Critique
Visual Aspects
  • The table is well-organized, with clear headers for each LLM and response category. However, it might be easier to read if the response counts for each prompt were presented in separate rows.
Analytical Aspects
  • The table effectively presents the raw response counts, but it would be helpful to include percentages for each response category within each LLM. This would allow for easier comparison of the models' response patterns.
Numeric Data
  • GPT-4 Crime Prompt Yes: 10
  • GPT-4 Crime Prompt No: 1992
  • GPT-4 Crime Prompt Ambiguous: 0
  • GPT-4 Crime Prompt Refusal: 782
  • GPT-4 Police Prompt Yes: 109
  • GPT-4 Police Prompt No: 429
  • GPT-4 Police Prompt Ambiguous: 0
  • GPT-4 Police Prompt Refusal: 2246
  • Gemini Crime Prompt Yes: 0
  • Gemini Crime Prompt No: 266
  • Gemini Crime Prompt Ambiguous: 2518
  • Gemini Crime Prompt Refusal: 0
  • Gemini Police Prompt Yes: 1284
  • Gemini Police Prompt No: 1131
  • Gemini Police Prompt Ambiguous: 369
  • Gemini Police Prompt Refusal: 0
  • Claude Crime Prompt Yes: 337
  • Claude Crime Prompt No: 1605
  • Claude Crime Prompt Ambiguous: 842
  • Claude Crime Prompt Refusal: 0
  • Claude Police Prompt Yes: 1237
  • Claude Police Prompt No: 317
  • Claude Police Prompt Ambiguous: 1230
  • Claude Police Prompt Refusal: 0
Figure 3

Figure 3 presents the Fitzpatrick Scale, a numerical classification schema for human skin color. It uses six types ranging from Type 1, representing the lightest skin tone, to Type 6, representing the darkest skin tone.

First Mention

Text: "Subject’s Skin-Tone: Fitzpatrick scale (Appx. Figure 3)"

Context: The authors are describing the annotation procedure for the Amazon Ring videos, outlining the categories used to label different aspects of the videos, including the subject's skin tone.

Relevance: The Fitzpatrick Scale is relevant to the study as it provides a standardized way to categorize skin tone, allowing the researchers to investigate potential biases related to skin color in the LLMs' decisions about calling the police.

Critique
Visual Aspects
  • The figure itself is not included in the provided text, making it impossible to assess its visual clarity or effectiveness.
  • If the figure were available, it would be beneficial to include visual representations of each skin type, such as photographs or color swatches, to enhance understanding.
Analytical Aspects
  • While the description mentions the use of the Fitzpatrick Scale, it does not explain how the scale is applied in the annotation process or how the six skin types are differentiated.
  • It would be helpful to provide more context on the limitations or potential biases associated with using the Fitzpatrick Scale to categorize skin tone.
Numeric Data
  • Number of Skin Types: 6
Table 8

Table 8 presents examples of responses generated by the three LLMs (GPT-4, Gemini, and Claude) to the prompt: "Should the police be called?" The table includes examples for various activity types, such as theft, entryway waits, and break-ins, and shows the LLMs' response type (Yes, No, Ambiguous, or Refusal) along with their textual explanations.

First Mention

Text: "Appendix Table 8 shows examples of model responses."

Context: The authors are discussing the different response types generated by the LLMs, including ambiguous responses and refusals to answer. They refer to Table 8 as a source of examples for these response types.

Relevance: Table 8 is highly relevant as it provides concrete examples of the LLMs' decision-making process and their reasoning behind recommending or not recommending police intervention. It allows readers to see how the models interpret different scenarios and how their responses vary based on the perceived activity.

Critique
Visual Aspects
  • The table is well-structured and easy to read, with clear headings and distinct columns for each LLM and response type.
Analytical Aspects
  • While the table provides examples of model responses, it does not offer any analysis or interpretation of these responses. It would be beneficial to include a brief discussion summarizing the key observations from the examples, such as common patterns in the LLMs' reasoning or notable differences in their responses across activity types.
Numeric Data

Results

Overview

The Results section focuses on analyzing how often and under what circumstances the three LLMs (GPT-4, Gemini, and Claude) recommend calling the police based on the Amazon Ring videos. It highlights that while the models rarely make definitive statements about the presence of crime, they are significantly more likely to recommend police intervention, even in cases where no crime is annotated. The section further explores the influence of neighborhood demographics on the models' decisions, finding that Gemini, in particular, is more likely to recommend calling the police in videos from white neighborhoods when a crime is present.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure 2

Figure 2 illustrates the probability of different LLMs (GPT-4, Gemini, and Claude) flagging a video for police intervention based on the presence or absence of annotated crime. It also examines the influence of neighborhood racial demographics (majority-white vs. majority-minority) on the models' decisions to recommend police involvement.

First Mention

Text: "Figure 2: Probability that LLMs flag a video for police intervention (i.e. respond “Yes” to “Should the police be called?”)."

Context: The authors are presenting their results, specifically focusing on how often the LLMs recommend calling the police and the factors that influence these recommendations.

Relevance: This figure is central to the paper's core argument about norm inconsistency in LLMs. It visually demonstrates the discrepancies between the models' recommendations and the actual presence of crime, as well as potential biases related to neighborhood racial demographics.

Critique
Visual Aspects
  • The figure effectively uses bar charts and scatter plots to present the data. The use of color and symbols in the scatter plot helps differentiate between crime presence, neighborhood race, and LLMs.
Analytical Aspects
  • The figure clearly shows that LLMs are more likely to flag videos with annotated crime, but it also highlights that they flag a significant portion of videos without crime. Further, the figure suggests potential racial biases, particularly with Gemini flagging videos in white neighborhoods with crime at higher rates.
Numeric Data
Table 4

Table 4 presents the coefficients from linear models predicting the likelihood of LLMs responding "Yes" to the prompt "Should the police be called?" The models include various independent variables such as activity type, time of day, subject demographics, and neighborhood characteristics. Results for GPT-4 exclude instances where the model refused to answer.

First Mention

Text: "Table 4: Coefficients from linear models to predict “Yes” responses to “Should the police be called?”. Results for GPT-4 exclude refusals to answer. Neighborhood characteristics from where the video was recorded."

Context: The authors are investigating the factors that explain the differences in LLMs' normative judgments to call the police. They use linear regression models to assess the statistical significance of various variables.

Relevance: This table is crucial for understanding the factors that influence the LLMs' decisions to recommend police intervention. It quantifies the relationship between various independent variables and the likelihood of the models responding "Yes", providing insights into potential biases and inconsistencies.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear labels for variables, LLMs, and significance levels. The inclusion of standard errors in parentheses enhances transparency.
Analytical Aspects
  • The table effectively presents the regression coefficients and their significance levels, allowing for a detailed analysis of the factors influencing LLM decisions. However, it would be beneficial to include a brief discussion of the practical implications of these findings, particularly regarding potential biases related to neighborhood characteristics.
Numeric Data
  • GPT-4 Intercept: 0.044
  • GPT-4 Entryway Leaves: 0.055
  • GPT-4 Talks to Resident: -0.03
  • GPT-4 Theft: 0.118
  • GPT-4 Break-In (Vehicle): 0.16
  • GPT-4 Break-In (Home): 0.6
  • GPT-4 Night: 0.475
  • GPT-4 Dark Skin: -0.059
  • GPT-4 Man: 0.061
  • GPT-4 White (Percent): -0.313
  • GPT-4 Age (Median): 0.047
  • GPT-4 Owner (Percent): 0.086
  • GPT-4 Income (Median): 0.013
  • GPT-4 Home Price (Median): 0.119
  • GPT-4 R2: 0.371
  • GPT-4 # Responses: 540
  • GPT-4 # Videos: 257
  • Gemini Intercept: 0.383
  • Gemini Entryway Leaves: 0.319
  • Gemini Talks to Resident: -0.098
  • Gemini Theft: 0.239
  • Gemini Break-In (Vehicle): 0.299
  • Gemini Break-In (Home): 0.596
  • Gemini Night: 0.372
  • Gemini Dark Skin: -0.052
  • Gemini Man: 0.299
  • Gemini White (Percent): -0.156
  • Gemini Age (Median): 0.331
  • Gemini Owner (Percent): 0.22
  • Gemini Income (Median): -0.073
  • Gemini Home Price (Median): 0.007
  • Gemini R2: 0.253
  • Gemini # Responses: 2784
  • Gemini # Videos: 928
  • Claude Intercept: -0.161
  • Claude Entryway Leaves: -0.259
  • Claude Talks to Resident: 0.227
  • Claude Theft: 0.06
  • Claude Break-In (Vehicle): -0.313
  • Claude Break-In (Home): 0.086
  • Claude Night: 0.332
  • Claude Dark Skin: -0.093
  • Claude Man: 0.035
  • Claude White (Percent): -0.1
  • Claude Age (Median): 0.086
  • Claude Owner (Percent): 0.011
  • Claude Income (Median): 0.092
  • Claude Home Price (Median): 0.104
  • Claude R2: 0.104
  • Claude # Responses: 2784
  • Claude # Videos: 928
Table 5

Table 5 compares the most salient n-grams (3-, 4-, and 5-word phrases) used by GPT-4, Gemini, and Claude in their responses to "Should the police be called?" across majority-white and majority-minority neighborhoods. The table highlights differences in language used by the models, suggesting potential biases in their responses based on neighborhood racial demographics.

First Mention

Text: "Table 5: Most salient 3-, 4-, and 5- grams between white and minority neighborhoods in responses to “Should police be called?”"

Context: The authors are analyzing the textual responses of the LLMs to understand the differences in their normative judgments. They examine the most frequent phrases used by the models in different neighborhood contexts to identify potential biases.

Relevance: This table provides qualitative evidence of potential biases in the LLMs' responses. The differences in salient n-grams across white and minority neighborhoods suggest that the models might be associating certain phrases or concepts with different racial contexts, raising concerns about fairness and discrimination.

Critique
Visual Aspects
  • The table is clearly organized, with separate columns for majority-white and majority-minority neighborhoods. However, it might be visually overwhelming due to the long list of n-grams in each cell.
Analytical Aspects
  • The table effectively highlights the differences in salient n-grams, but it does not provide a quantitative measure of the magnitude of these differences. Including odds ratios or other statistical measures would strengthen the analysis.
Numeric Data
Table 6

Table 6 presents the response counts of three LLMs (GPT-4, Gemini, and Claude) to the prompt "Is there a crime happening?" across various activity types observed in 928 videos. Each video was processed three times, and the responses were categorized as "Yes," "No," "Ambiguous," or "Refusal." Additionally, the table breaks down the responses based on whether the video was from a majority-white or majority-minority neighborhood.

First Mention

Text: "Table 6: Response counts to the “Is there a crime happening?” prompt across 928 videos and 3 iterations/video."

Context: This table appears in the Appendix, following the main body of results, and provides a detailed breakdown of LLM responses to the crime prompt.

Relevance: Table 6 is relevant as it provides a granular view of how often the LLMs correctly identify the presence of a crime in the videos. This helps to establish a baseline understanding of the models' factual assessment capabilities before delving into their normative judgments about calling the police.

Critique
Visual Aspects
  • The table is well-organized, with clear labels for each model, response type, and activity type. However, it might benefit from visual cues, such as color-coding or bolding, to highlight key patterns or differences in response counts.
Analytical Aspects
  • The table effectively presents the raw response counts, but it would be more informative to include percentages for each response category within each LLM and activity type. This would facilitate easier comparison and highlight the models' accuracy rates.
Numeric Data
Table 7

Table 7 displays the response counts of three LLMs (GPT-4, Gemini, and Claude) to the prompt "Should the police be called?" across the same activity types and video iterations as Table 6. It also categorizes the responses as "Yes," "No," "Ambiguous," or "Refusal" and provides a breakdown by majority-white and majority-minority neighborhoods.

First Mention

Text: "Table 7: Response counts to the “Should the police be called?” prompt across 928 videos and 3 iterations/video."

Context: This table, also in the Appendix, follows Table 6 and presents a similar breakdown of LLM responses but for the police prompt, focusing on their recommendations for police intervention.

Relevance: Table 7 is highly relevant to the study's core focus on norm inconsistency. It quantifies how often the LLMs recommend calling the police, allowing for analysis of their decision-making patterns in relation to the actual presence of crime and neighborhood demographics.

Critique
Visual Aspects
  • Similar to Table 6, the table is well-organized but could benefit from visual enhancements to highlight key trends or discrepancies in response counts.
Analytical Aspects
  • The table would be more insightful if it included percentages for each response category within each LLM and activity type, enabling easier comparison of the models' recommendations for police intervention.
Numeric Data
Table 9

Table 9 presents the coefficients from linear models used to predict "Yes" responses (recommendations to call the police) from the LLMs. The table includes coefficients for various factors, such as activity type, time of day (night/day), subject demographics (skin tone and gender), and neighborhood characteristics. It provides separate coefficients for each LLM (GPT-4, Gemini, and Claude), allowing for comparison of their decision-making criteria.

First Mention

Text: "Table 9: Coefficients from linear models to predict "Yes" responses to "Should the police be called?""

Context: This table appears in the main Results section and presents the findings of a statistical analysis examining the factors that influence the LLMs' recommendations to call the police.

Relevance: Table 9 is central to the study's analysis as it quantifies the relationship between various factors and the LLMs' recommendations for police intervention. It helps to identify which factors significantly influence the models' decisions and whether those influences align with expectations or reveal potential biases.

Critique
Visual Aspects
  • The table is well-organized and uses asterisks to indicate statistical significance levels, enhancing readability and interpretation. However, it might be helpful to visually separate the coefficients for each LLM, perhaps using borders or shading.
Analytical Aspects
  • The table effectively presents the coefficients and standard errors, allowing for an assessment of the statistical significance and precision of the estimates. However, it would be beneficial to include a more detailed interpretation of the coefficients, explaining what they imply about the LLMs' decision-making processes and potential biases.
Numeric Data
Table 10

Table 10 presents coefficients from linear models predicting when LLMs refuse to answer (GPT-4) or give an ambiguous response (Gemini and Claude) to the prompt "Should the police be called?" It includes coefficients for activity types, time of day, subject demographics, and neighborhood characteristics.

First Mention

Text: "Table 10: Coefficients from linear models to predict “Refuse” responses for GPT-4 and “Ambiguous” responses for Gemini and Claude to the prompt: “Should the police be called?”."

Context: The authors are discussing additional analyses conducted to understand the factors associated with LLMs refusing to answer or providing ambiguous responses regarding calling the police.

Relevance: This table helps explain why models sometimes avoid giving a definitive "yes" or "no" answer. It provides insights into the situations where models are more likely to hedge or refuse, which is important for understanding their limitations and potential biases.

Critique
Visual Aspects
  • The table is well-organized and uses asterisks to clearly indicate statistical significance levels.
Analytical Aspects
  • The table would benefit from a clearer explanation of how the coefficients should be interpreted in terms of their impact on the likelihood of a "Refuse" or "Ambiguous" response. For instance, does a positive coefficient for "Dark Skin" mean the model is more likely to refuse or be ambiguous when the subject has darker skin?
Numeric Data
Table 11

Table 11 focuses on predicting "Yes" responses (recommending calling the police) and examines the interaction between the presence of a crime and whether the neighborhood is majority-white. It aims to disentangle the independent effects of crime and neighborhood race from their combined effect.

First Mention

Text: "Table 11: Coefficients from linear models to predict “Yes” responses to “Should the police be called?”."

Context: The authors are discussing the unexpected finding that LLMs are less likely to recommend calling the police in white neighborhoods, even when controlling for other factors. They refer to Table 11 to further explore this interaction effect.

Relevance: This table is crucial for understanding a specific nuance in the models' decision-making: the combined effect of crime and neighborhood race. It helps determine whether the lower rate of calling the police in white neighborhoods is consistent across different crime scenarios.

Critique
Visual Aspects
  • The table is concise and clearly presents the coefficients for the intercept, crime, white neighborhood, and their interaction. The use of asterisks for significance levels is helpful.
Analytical Aspects
  • A more detailed interpretation of the interaction term's coefficient would enhance the table's analytical value. For example, does a positive interaction term mean the models are more likely to recommend calling the police when a crime occurs in a white neighborhood compared to a non-white neighborhood?
Numeric Data

Discussion

Overview

The Discussion section examines the implications of the study's findings, focusing on the concept of "norm inconsistency" in LLMs and its consequences for surveillance and high-risk decision-making. It explores three key aspects of norm inconsistency: discordance with facts, challenges for bias mitigation, and the significance of norm disagreement between models. The section emphasizes the need for greater transparency in LLM decision-making and the development of more robust bias mitigation strategies.

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Overview

The conclusion section reiterates the main contributions of the research paper, emphasizing the evidence of norm inconsistency in LLMs, the discovery of socio-economic bias in their recommendations for police intervention, and the variations in decision-making across different models. It underscores the importance of further research into the normative behavior and biases of large language models to ensure their equitable and responsible development.

Key Aspects

Strengths

Suggestions for Improvement

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