LLM use may threaten freedom of thought and equality across law, journalism and education

Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-09-09T23:10:50.436Z • Permanent link

Recent intelligence from the Human Rights Center at UC Berkeley School of Law has shed light on the growing concerns surrounding the use of large language models (LLMs) in law, journalism, and education. The assessment was conducted by a team of experts led by Professor Kate Crawford, a renowned scholar on the social implications of AI. According to the report, LLMs have been increasingly used in these fields to generate content, answer questions, and even produce original research. However, the report warns that the unchecked proliferation of LLMs could have far-reaching consequences for human rights, particularly in the areas of equality, freedom of thought, and access to information.

One of the key findings of the report is that LLMs have been used to produce biased and inaccurate content, often with serious implications for individuals and communities. For example, a study published in the Journal of Human Rights found that LLM-generated news articles were more likely to contain hate speech and discriminatory language than articles written by human journalists. Furthermore, the report highlights the lack of transparency and accountability in the use of LLMs, making it difficult to track and correct errors or biases.

The report also highlights the potential risks of LLMs in education, where they are being used to create personalized learning materials and even grade student assignments. However, the report warns that the use of LLMs in education could exacerbate existing inequalities, particularly for students from low-income backgrounds who may not have access to the same quality of education as their more affluent peers. For instance, a study by the Economic Policy Institute found that students from low-income families are more likely to attend schools with limited resources and outdated technology, making it more difficult for them to access high-quality educational materials.

The use of LLMs in law, journalism, and education has significant implications for the scientific and academic research community. Many researchers rely on LLMs to generate hypotheses, identify patterns, and analyze large datasets. However, the report's findings raise serious concerns about the reliability and validity of LLM-generated research. For example, a study published in the Journal of Machine Learning found that LLM-generated predictions were often no more accurate than random chance, highlighting the need for more rigorous testing and validation of LLM-generated research.

The report also highlights the potential risks of LLMs in the research community, particularly in terms of intellectual property and authorship. For instance, a study by the American Association for the Advancement of Science found that LLM-generated research was often not properly cited or credited, raising questions about the ownership and authorship of research. Furthermore, the report warns that the use of LLMs could exacerbate existing biases and inequalities in the research community, particularly for underrepresented groups.

The use of LLMs in law, journalism, and education is not a new phenomenon. In fact, LLMs have been used in these fields for several years, with many companies and institutions investing heavily in their development and deployment. However, the report's findings highlight the need for greater regulation and oversight of LLMs, particularly in terms of their impact on human rights and equality. For example, the European Union has implemented strict regulations on the use of LLMs in journalism, requiring publishers to disclose the use of AI-generated content. Similarly, the United States has passed legislation aimed at regulating the use of LLMs in education, including the AI in Education Act.