Technological leaps in large language models (LLMs) have ushered in an era of groundbreaking AI applications that continue to gain momentum in quantity and quality.
While many in the creative and professional fields look on with trepidation, a growing pool of writers, coders, marketers, and other professionals are eager to adopt this new technology to reduce costs while significantly boosting content production to previously impossible levels.
Unfortunately, as with many technologies, bad actors will discover new ways to maliciously use LLMs to influence public opinions, amplify social and political biases, and spread propaganda.
How AI-Powered Influence Operations Influence Public Opinion
Research from Stanford Internet Observatory stresses the powerful influence LLMs can have on public perception. These models can exploit how misinformation gets shared and greatly expand its reach in the digital domain.
Propagandists have been given a tool that allows them to generate persuasive and emotionally resonant content at an incredible scale, enhancing their capacity to influence public opinion, political beliefs, and attitudes.
LLMs enable nefarious operators to boost existing influence ops and develop new tactics with little effort. For instance, adversaries could use LLMs to create personalized content at scale or introduce conversational chatbots that can interact with many individuals simultaneously.
A language model’s ability to generate original content will also help actors conceal influence campaigns.AI text creation tools generate output that can convey the same ideas in unique ways, which is why researchers often struggle to identify campaigns using them.
These tools help propagandists and purveyors of misinformation avoid copying and pasting text repeatedly across accounts, a technique that Dr Josh A. Goldstein, a co-author on the paper, refers to as “copypasta.”
While the current crop of LLM text generation tools can be coaxed to deliver human-like text, they do have limitations. Longer texts often lose coherency, or the AI will start spouting nonsense and making up facts. This makes them less helpful in creating politically influential campaigns best suited to long-form content.
However, when using LLMs to flood social media campaigns with misinformation or propaganda, their limitations become less of an issue.
The Impact of AI on Social Media Interactions
One of AI’s most notable contributions to social media is its role in content creation and customization. AI algorithms are customized to generate and tailor content that closely aligns with a user’s interests.
A newsfeed programmed to narrow the range and variation of content creates an echo chamber that reinforces misaligned social biases, political prejudices, and beliefs. The more people use system recommendations, the less diverse their content becomes, which can strengthen societal division and hinder open discourse.
The formation of echo chambers through AI customization underscores the need for evaluating algorithms and recommendation systems, promoting diversity, and mitigating their effects on public discourse and ability to degrade societal cohesion.
LLMs and the Perpetuation of Stereotypes
Biases in LLMs can manifest from biased data sets and the subjectivity of human reviewers, with biases getting introduced intentionally or not. While biases can take many forms, the most common include:
- Gender Biases such as gendered pronouns, occupational biases, and gender stereotypes
- Racial and ethnic biases, including racial slurs and hate speech
- Socioeconomic biases based on socioeconomic backgrounds and educational inequality
This comprehensive review, published by the Collective Intelligence Conference, highlights the biases present in the training data used for Large Language Models (LLMs) and how they can cause the models to perpetuate stereotypes related to gender, race, and ethnicity, among other factors.
This finding underscores the significant impact of training data on LLMs’ behavior and outputs. It also emphasizes the importance of addressing biases in the training data to prevent LLMs from perpetuating harmful stereotypes.
Evaluate and Implement De-Biasing Methods in Large Language Models
Tackling biases in AI language models is difficult because tackling it will require a multi-faceted approach. Suggestions for eliminating biases include making training data more diverse and using algorithms that focus on fairness and diversity. However, even with these ideas, models can still have biases that we don’t mean for them to have.
Creating training data that is more diverse will mean including information from various cultures and viewpoints, which should help prevent LLMs from learning biases during training.
Fairness-aware algorithms designed to identify and mitigate biases in models’ outputs are another idea that could help LLMs with fair and equitable language generation.
A report from the AI Now Institute reveals the need for more transparency in AI development and regulations to oversee it. Being transparent about how biases are introduced could show the limitations of current methods. Regulations will also be crucial to ensuring language models don’t have unfair or discriminatory biases when used.
As LLMs continue to permeate everyday life, we will need regulations and legislation that open up AI companies to scrutiny to ensure they support the ethical use of their technologies.
By promoting transparency and regulatory scrutiny, the AI community can work towards developing more accountable and fair LLMs, ultimately contributing to the responsible and ethical deployment of AI technologies.
Final Recommendations
Large Language Models (LLMs) will continue to influence society’s views and biases, and responsible use will require a multidisciplinary approach. Here’s how we can leverage these technologies so they are a force for good and able to drive positive change.
Transparency is vital. LLM developers must openly disclose training data and methods to address biases. This promotes trust and accountability, letting stakeholders assess AI content’s fairness and reliability.
Governments should create comprehensive regulations for ethical LLM deployment. These frameworks must require impact assessments and adherence to guidelines by developers and organizations. Clear rules ensure responsible deployment, minimizing unintended consequences.
Public education about LLMs’ abilities and limitations is key. Initiatives can help people recognize and evaluate AI-generated content biases, enabling informed decisions. An informed society will be better equipped to navigate the complexities of AI technology.
New AI models keep advancing and becoming smarter. With more capabilities, we get new chances and challenges. If we put ethics first and develop AI responsibly, these technologies could help society progress while preventing issues like biases.
Through collaboration and proactive steps, we can ensure language models grow in directions that will benefit mankind, creating a future where AI is beneficial, increases human knowledge and understanding, and fosters inclusivity.