Had a thought about using the new GPT agents. I think they can be best used one chapter at a time when writing. First, all the assets at once exceed the 20-item limit. Second, the model can’t do large-scale contextualization.

AI-driven Monitoring of Attitude Polarization in Conflict-Affected Countries for Inclusive Peace Process and Women’s Empowerment

• Conflict has become increasingly prevalent in developing countries, and the role of social media platforms in exacerbating these conflicts cannot be ignored. Peacebuilders who focus on promoting inclusive and sustainable peace in war-torn countries are confronted with numerous challenges. The effects of disinformation, misinformation, polarization, online harassment, and the use of digital media as political weapons have been extensively examined in the context of the USA and Europe. The situation in developing nations such as Ethiopia, characterized by a significant digital divide, ethnic polarization, ethnification of mass media, and limited access to digital media for most of the population, remains understudied. Within such conflicts, women play a crucial role as they are often the last barrier for economic collapse, but likewise are specifically targeted in conflicts. Women are historically marginalized all over the world, but especially in the context of developing nations, Ethiopia in particular. The two-year-long Ethiopian civil war showed that women were more seriously affected by the war than their male counterparts. From the different national and international media reports, we have learned that mass displacement, sexual harassment, using rape as a tool of war, group rape, etc. challenged the lives of women in Ethiopia. Women paid the price of the war more than men in the country. Thus, incorporating women’s voices, perspectives, and experiences is paramount for inclusive and sustainable peacebuilding. Our research proposal seeks to explore the impact of social media on offline unrest, specifically its effects on women, and provide viable solutions to peacebuilders. We focus on building a pipeline for digital peacebuilding, including the potential use of AI tools such as large language models (LLMs) like Google’s PaLM, as automated classifiers. Data will be collected from popular social media platforms in Ethiopia, with a focus on addressing the issue of polarization affecting women. The project will further apply different NLP techniques such as topic clustering, named entity recognition, sentiment analysis, and hate speech detection with machine learning approaches. The development of such a pipeline facilitates the works of peacebuilders and aims to reduce the marginalization of women’s voices and perspectives in the peace-building process. This could lead to develop a toolchain that can be applied in a similar war-torn country such as Yemen, Libya, Sudan, etc.

GPT Agents

• Tyler wrote back. Need to schedule something for next week

SBIRs

• Working on the ETF deck. It’s expanding a bit too much maybe, but I can edit later
• Need to fold Zac’s paragraphs into the notes.
• The data is not up yet on the server. Rukan says probably not until Wednesday COB
• 9:00 Standup
• 2:30 AI Ethics? Nope
• 3:30 USNA – they are all over the place. Asked them to clarify their research questions and methods to answer them. We’re going to have more formalized presentations than an ad-hoc problem-solving session
• 4:00 Fellowship discussion – Serious lols