Stochastic Parrots Producing Stochastic Terrorism

by claudia alick

In recent years, we have seen a rise in the use of Artificial Intelligence (AI) and language models like ChatGPT and DALL-E. While these models can be useful for generating human-like text and images, they can also have unintended and harmful effects. Stochastic Terrorism is the public demonization of a person or group resulting in the incitement of a violent act, which is statistically.

“Stochastic Parrots” is the term that refers to language models that unintentionally perpetuate harmful biases and stereotypes, which can have serious consequences. Stochastic Parrots can be thought of as unintentional forms of stochastic terrorism, which is when individuals or groups incite violence through the use of language.

For example, imagine a language model is trained on a dataset that contains biased information about certain groups of people. If the model is then used to generate text or images, it may perpetuate these biases without even realizing it. This can lead to material harm to Black, trans, and other marginalized classes, who may be negatively impacted by these stereotypes and biases.

Stochastic terrorism, includes the repeated use of hate speech or other vilifying, dehumanizing rhetoric. It is usually produced by public figures or political leaders and inspires one or more of the figure’s supporters to commit hate crimes or other acts of violence against a targeted person, group, or community. What happens when this is produced by computer programs?

Stochastic Parrots can also be used to spread false information and propaganda. For instance, a language model could be trained on misinformation about a political candidate, and then be used to generate text or images that perpetuate these falsehoods. This can lead to harmful consequences, such as the spread of misinformation and the manipulation of public opinion.

It’s important to recognize that impact matters more than intention. While the creators of these language models may not intend to harm marginalized communities, their use can have negative consequences. Therefore, it’s important for platforms and organizations to take responsibility for the impact of their technology and take steps to mitigate harm.

One way to address this issue is for platforms to slow down what they invite people to use. For example, companies could invest more time and resources into testing and evaluating language models for bias before making them widely available. Additionally, organizations can take steps to ensure that their models are trained on diverse and inclusive datasets, which can help to mitigate the risk of perpetuating harmful biases.

In conclusion, Stochastic Parrots are an important issue that must be addressed in the development and use of language models like ChatGPT and DALL-E. The potential for unintentional harm to marginalized communities is significant, and it’s important for platforms and organizations to take responsibility for the impact of their technology. By taking steps to address bias and promote diversity and inclusion, we can work towards creating a more just and equitable society.

Source: Bender, E. M., Gebru, T., McMillan-Major, A., & Mitchell, M. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610-623).

This essay was inspired by the paper “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” that talks about the potential risks and negative consequences that could arise from using very large language models like GPT-3. The authors of the article argue that these models can be “stochastic parrots,” which means they may be able to generate convincing language without truly understanding what they are saying. Relying too heavily on these large language models could lead to several problems, such as reinforcing biases in language and data, spreading misinformation, and potentially even causing harm in fields like medicine and law. The authors are calling for greater consideration and scrutiny of the use of large language models, and for more efforts to mitigate the potential risks they pose.

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