Ethical Risks of Large Language Models

In recent years, large language models (LLMs) such as GPT-3 and GPT-4 have taken the world by storm, transforming industries from finance to medicine. However, there is growing concern that these models pose significant ethical risks that may have far-reaching consequences. From perpetuating bias and misinformation to amplifying hate speech, the potential risks of LLMs are vast and varied.

Herein, we explore some of the risks posed by LLMs.

I. Discrimination, Exclusion, and Toxicity

LLMs have the potential to perpetuate and amplify existing biases, leading to discrimination, exclusion, and toxicity. This risk area encompasses not only the potential biases in the models themselves, but also the potential for the models to be used to target vulnerable groups or perpetuate harmful stereotypes.

A concerning example is stereotypes in the criminal justice space. Some recent studies have found that predictive policing algorithms, which are often based on machine learning models similar to LLMs, disproportionately target minority communities, perpetuating existing biases in the criminal justice system. Similarly, studies have also shown that LLMs trained on legal documents can replicate and amplify existing racial and gender biases in the language used in such documents.

The toxicity risk of LLMs is also a concern, as these models can be used to generate abusive and harassing language. In 2020, researchers found that GPT-3 could generate offensive text in response to certain prompts, such as those related to religion or race. Such toxicity can contribute to online abuse and harassment, creating a hostile online environment.

II. Information Hazards

This refers to the risks associated with the spread of information that is harmful or dangerous, such as propaganda, deepfakes, and other forms of disinformation. LLMs can be used to generate and spread such information, making this a significant ethical risk area.

A potential information hazard is the creation of deepfakes, which are videos or images that have been manipulated to show something that did not occur. LLMs can be used to generate highly realistic deepfakes, which can be used to spread disinformation and manipulate public opinion. In 2019, researchers demonstrated how a language model could be used to create a convincing deepfake of former US President Barack Obama, raising concerns about the potential for this technology to be used for malicious purposes.

III. Misinformation Risks

These are risks associated with the spread of false or misleading information. LLMs can contribute to the spread of misinformation by generating false or misleading text, or by amplifying existing misinformation.

For instance, LLMs are likely to amplify false or misleading text on social media. LLMs can be used to generate automated responses to social media posts, which can be used to spread false or misleading information. In 2020, researchers found that GPT-3 could be used to generate convincing tweets related to COVID-19, demonstrating the potential for this technology to be used to spread misinformation on social media.

IV. Malicious Uses

LLMs are likely to be used for nefarious purposes, such as cybercrime, espionage, or terrorism. LLMs can help generate highly convincing phishing emails by impersonating individuals, or spreading sophisticated propaganda, among other potential malicious uses.

Cybercrime is likely to be a key malicious use case. LLMs can be used to generate highly convincing phishing emails, which have the potential to steal personal information or spread malware. In addition, LLMs can be used to impersonate individuals, making it difficult to detect and prevent cyberattacks.

V. Human–Computer Interaction Harms

LLMs may harm individuals through their interactions with human users. This risk area encompasses issues such as user privacy, user autonomy, and the potential for LLMs to be used to manipulate individuals.

LLM-powered personal assistants are prime examples. These devices use LLMs to understand and respond to user requests, but they also collect large amounts of personal data, raising concerns about user privacy. In addition, these devices can be used to manipulate user behavior, such as by recommending certain products or services based on user data.

VI. Automation, Access, and Environmental Risks

LLMs are likely to have negative impacts on society as a whole, for instance, by reducing job opportunities, exacerbating inequality, or contributing to climate change. These risks are often indirect, but equally critical.

In the context of job displacement, LLMs with each iteration have the potential to replace human workers in a wide range of industries, leading to job losses and economic inequality.

LLMs can also contribute to environmental harms, as training and running these models involve high energy consumption. LLMs also require significant amounts of computational power, which can contribute to greenhouse gas emissions and exacerbate climate change.

Overall, the ethical risks of large language models are significant and multifaceted. While these models have the potential to bring about significant benefits, they also pose certain risks that must be addressed. As the use of large language models continues to grow, it is essential that we work to mitigate these risks and ensure that these models are developed and used in a responsible and ethical manner.