Generative AI: 1. Ethics 2.CLIP

Motivation

In today's age defined by technological advancements, the integration of Artificial Intelligence (AI) across diverse sectors has revolutionised our lives, promising increased efficiency and progress in fields such as healthcare, social media, economy, internet services, and more. Notably, the emergence of Large Language Models (LLMs) like GPT-3 or LLAMA has sparked both fascination and concern. While these models impress with their ability to generate human-like text and perform complex tasks, they invite to an essential inquiry: the ethical considerations in AI.

Our project aims to delve into the ethical problems surrounding AI from technical and philosophical perspectives. How do AI systems deal with ethical dilemmas? How can we design these systems to better align with human ethical values? Do these systems maintain consistency among their ethical considerations?

Join us to dig into these critical questions and navigate the landscape where technology and ethics converge, seeking a deeper understanding to the responsible development and deployment of AI in our society.

Project Plan and Milestones

Weekly Plan

Date	Task	Completion
Week 4	Paper reading. Existing RLHF and RLAIF exploring. Red-teaming dataset exploring.	√
Week 5	Familiarizing with Dromedary, SALMON, Llama base models.	√
Week 6	Evaluation of different base models. Choice of using Llama 2 model as our baseline.	√
Week 7	Red teaming dataset exploration. Reading about ethical theories.	√
Week 8	ETHICS dataset discovering.	√
Week 9	ETHICS dataset formatting for Llama fine-tuning and evaluation. Llama supervised model fine-tuning	√
Week 10	Evaluation of Llama model before and after fine-tuning with ETHICS dataset. Model Tuning. Mid-term Presentation & Start writing the Wikipedia page with the plan.	√
Week 11	Read about Reinforcement learning using PPO. Re-formatting deontology dataset. Creation of the preference model.	√
Week 12	Examine preference learning models and learn how they work and their applications. Start a simple reinforcement learning model setup. Run preliminary tests and evaluate results.
Week 13	In-depth analysis of model performance. Drafting Wikipedia pages, including outline and structure.
Week 14	Completing the Wikipedia page, including proofreading and ensuring technical accuracy. Write the Github page & prepare for the Final presentation

Milestone 1

• Define Research Questions: Establish clear, focused questions to guide the project.
• Literature Review: Conduct a comprehensive review of existing studies in AI ethics.
• Ethical Theory Exploration: Investigate various ethical theories to ground your research in a solid theoretical framework.
• Ethical Dataset Identification: Locate datasets for quantitative AI ethics evaluation, such as red teaming datasets.

Milestone 2

• Refine Research Goals: Sharpen the focus and scope of the research based on initial findings.
• Dataset Finalization: Select the most appropriate dataset after exploration and evaluation.
• Model Selection and Fine-Tuning: Settle on the LLaMA model and fine-tune it by deploying GPU resources.
• Model Evaluation: Conduct a thorough evaluation of the model, focusing on its ethical implications and performance.

Milestone 3

• Develop Advanced Models: Implement Preference and Reinforcement learning models, integrating them with the fine-tuned LLaMA model.
• In-Depth Analysis: Analyze the models' outcomes, assessing performance, identifying defects, and investigating specific issues like coherence and degeneration.
• Documentation and Dissemination: Create a comprehensive Wikipedia page summarizing the project's findings.
• Final Deliverables: Compile all project materials, including a well-documented GitHub repository.