Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts
This work addresses potential political and societal risks from malicious text generation for organizations like the UN, though it is incremental as it applies an existing method to new data.
The researchers tackled the risks of AI-generated text by fine-tuning a pretrained AWD-LSTM model on UN General Assembly speeches to generate remarks mimicking political leaders, demonstrating how easily this can be done and highlighting associated threats.
Automated text generation has been applied broadly in many domains such as marketing and robotics, and used to create chatbots, product reviews and write poetry. The ability to synthesize text, however, presents many potential risks, while access to the technology required to build generative models is becoming increasingly easy. This work is aligned with the efforts of the United Nations and other civil society organisations to highlight potential political and societal risks arising through the malicious use of text generation software, and their potential impact on human rights. As a case study, we present the findings of an experiment to generate remarks in the style of political leaders by fine-tuning a pretrained AWD- LSTM model on a dataset of speeches made at the UN General Assembly. This work highlights the ease with which this can be accomplished, as well as the threats of combining these techniques with other technologies.