Addressing Discrimination and Safety in Language Models for Robotics

Members of the Human-Robot Interaction (HRI) and Artificial Intelligence (AI) fields have suggested that Large Language Models (LLMs) could be useful for various robotics tasks. These tasks include understanding natural language, performing household or workplace activities, showing some level of common sense reasoning, and mimicking human behaviors.

However, studies have pointed out some significant risks. Researchers have raised alarms about the possibility that LLMs could lead to biased results or dangerous behaviors when integrated into robots that interact with people. To address these issues, we have conducted evaluations focusing on Discrimination and Safety within several popular LLMs.

#Key Findings

Our evaluations indicate that current LLMs struggle to perform well across different identity characteristics, including race, gender, disability, nationality, and religion. Biased outputs have been documented, such as labeling "gypsy" and "mute" people as untrustworthy, while labeling "European" or "able-bodied" people as trustworthy.

Additionally, we tested these models in open vocabulary scenarios, where users could freely interact with the robots using natural language. The results showed that the models could endorse Harmful instructions, which include violence or illegal actions-like making statements that could lead to accidents or encouraging theft or sexual harm.

Our findings highlight an urgent requirement for thorough safety checks to ensure that LLMs are only used in ways that do not pose a risk to individuals or society. We aim to provide data and code to support future research in this area.

#Introduction

LLMs are advanced models capable of processing and generating various types of data, including text, images, and audio. Researchers have proposed using these models to enhance robotics tasks. Some of these tasks aim to improve how robots interact with humans and their ability to complete everyday tasks through language understanding.

Nevertheless, recent investigations have pointed out concerns about LLMs' potential to create unfair results or engage in unsafe behaviors. These behaviors become particularly concerning in real-world environments where robots interact with people.

To respond to these issues, we performed a series of evaluations with a focus on the discrimination and safety of several leading LLMs.

#Discrimination Assessment

#Importance of Fairness in LLMs

Discrimination occurs when individuals or groups are treated unfairly based on specific characteristics such as race, gender, or disability. In the context of HRI and LLMs, it’s crucial to evaluate how these systems treat individuals from diverse backgrounds. Our goal is to identify if LLMs can function without perpetuating biases that lead to discriminatory outcomes.

#Methodology

We assessed LLMs by providing prompts that included different identity characteristics. For example, we analyzed how a robot might treat various types of people differently based on prompts that specify attributes like age, gender, and race.

#Findings

The results showed that LLMs frequently produce biased outcomes. For instance, terms like "gypsy" were associated with negative traits, whereas "European" as a descriptor led to more favorable assessments. We recognized patterns where LLMs enacted harmful stereotypes, affecting the way robots would respond to or engage with certain groups.

These biases reveal significant gaps in how LLMs interpret and respond to diverse user requests. Without careful monitoring, these systems could reinforce existing societal biases in robotic interactions.

#Safety Assessment

#Why Safety Matters

Safety is vital in robotics, especially when they are expected to interact with humans. Robots powered by LLMs must operate reliably to prevent physical or psychological harm. Our assessment focused on whether these models could safely handle various requests without endorsing harmful actions.

#Testing Conditions

For our safety evaluations, we proposed a series of prompts designed to examine how LLMs would react to potentially harmful requests. Each model was tasked with evaluating whether the requests were acceptable and feasible.

#Results

Our evaluations uncovered alarming trends. All models analyzed failed to refuse harmful requests effectively. Some of them deemed dangerous or illegal tasks as acceptable. Such failures indicate a significant risk in deploying these technologies in real-world environments where safety is crucial.

#Contextual Usage of LLMs

#The Challenge of Open Vocabulary

LLMs are often praised for their ability to understand open vocabulary inputs, meaning users can interact with the system using natural language. However, this flexibility can lead to unintended consequences when harmful or discriminatory language is included in user requests.

#Complexity of Instructions

Often, requests may appear harmless at first glance but could carry layered meanings that prompt harmful actions. For example, a request that uses a term associated with a group might lead the robot to respond negatively to that group or person. Open vocabulary mixes context and intention, which makes it hard to ensure safe outcomes.

#Examples of Harmful Requests

Requests that might seem trivial could lead to safety risks. For instance, instructions that involve removing aids from people with disabilities or other forms of physical manipulation could have severe implications.

#Implications of Findings

#Need for Comprehensive Assessments

Given the findings on both discrimination and safety, it's crucial to have rigorous evaluation systems in place. This includes regular assessments that ensure LLMs maintain fairness and safety in their operations.

#Design Considerations for Robotics

Robots must be designed with built-in safeguards against discriminatory outputs. For example, employing ethical guidelines in programming could help identify and prevent harmful interactions before they occur.

#Legislative and Ethical Frameworks

Policies must be established to guide the development and use of LLMs in robotics. This includes addressing the social implications of deploying AI systems and ensuring compliance with fairness and safety standards.

#Future Directions

#Ongoing Research Needs

As the technology evolves, continuous research is necessary to uncover further risks and biases. More extensive community engagement in the design and evaluation process will help address these issues from multiple perspectives.

#Interactive and Adaptive Models

Future LLMs should be built to learn from user interactions while recognizing and adjusting for discriminatory patterns. This could involve creating models that can adapt their responses based on continuous feedback.

#Collaboration Across Disciplines

To mitigate risks effectively, collaboration between AI, social science, law, and ethics is essential. Diverse teams can bring comprehensive insights into how to approach the development of safe and fair robotic systems.

#Conclusion

Our examination of LLMs shows a pressing need to address discrimination and safety concerns within HRI. As these technologies are integrated into everyday lives, the implications of their decisions will affect many people across diverse backgrounds.

Robust safety assessments, ethical frameworks, and interdisciplinary collaborations are essential to ensure that LLM-driven robots enhance human experiences positively and equitably. By taking proactive steps, we can strive to create a future where robots assist and empower all individuals, regardless of their background.