AIE:Advancing Governance for Artificial Intelligence

Research Process for the Institute for AI Ethics (AIE)

1. Core Research Areas

Research Focus	Key Issues	Societal Challenges
Generative AI Ethics	Deepfake misuse, bias in large models, copyright attribution for AIGC	Technology abuse leading to misinformation proliferation, legal accountability challenges
Autonomous Systems Ethics	Ethical algorithms for autonomous vehicles, compliance of military AI, robot rights	Lack of international consensus, conflicts between ethics and commercial interests
Algorithmic Fairness	Discrimination in financial/medical AI, explainability of black-box models, algorithm auditing	Algorithmic bias exacerbating social inequality, lack of regulatory tools
AI-Society Interaction	Job displacement by AI, psychological impact of emotional AI, human-machine power dynamics	Technological change outpacing societal adaptation capacity

2. Research Methodologies and Tools

1. Methodological Framework:

Research Methodology	Tools/Techniques	Application Scenarios
Techno-Ethical Review	Ethical Impact Assessment (EIA) Matrix	Compliance review for high-risk AI projects
Sandbox Simulation	Virtual urban traffic system (Unity-based)	Stress testing for autonomous vehicle ethics algorithms
Open Collaboration	GitHub ethics review template repository	Global developer collaboration on ethics case studies

2. Core Technical Tools:

• AI Ethics Scanner: Bias detection and privacy risk assessment
• EthicsGuard: Real-time decision monitoring and ethics alert triggering

3. Current Issues and Improvements:

• Issue: Existing tools (e.g., IBM Fairness 360) only support English data
• Improvement: Developing multilingual bias detection modules (covering 50+ languages)

3. Research Outcomes and Case Studies

Outcome Type	Case Study	Conflicts and Reflections
Policy Tools	“Watermarking Guidelines for Generative AI”	Tech companies resist mandatory labeling – need innovation-regulation balance
Technical Standards	“Algorithmic Fairness Certification Framework”	Criticized as “Western-centric” – requires localization
Social Advocacy	Annual “Global AI Ethics Index”	Underestimates non-Western efforts – adding cultural inclusivity metrics

4. Strategic Partnership Network

Partnership Type	Collaboration Focus	Expected Outcomes
International Organizations	Developing transnational AI governance principles	Establishing global ethical framework
Academic Institutions	Co-building open-source ethics tools	Improving research tool universality
Industry Partners	Optimizing product ethics review processes	Promoting corporate self-regulation
Public Institutions	Providing technical support for policy pilots	Facilitating regulatory implementation

Research Team and Leadership

Research Team Overview

The Institute for AI Ethics（AIE） is supported by a diverse, interdisciplinary team of experts in AI ethics, computer science, law, and social sciences. Our virtual research team collaborates globally to address pressing ethical challenges in artificial intelligence.

Key Research Leadership

Name	Position	Contact	Research Focus
Dr. Emily Zhang	Director of AI Ethics Research	emily.zhang@ethicstech.org	Algorithmic Fairness & Governance
Prof. Michael Chen	Head of Autonomous Systems Ethics	michael.chen@ethicstech.org	Ethical AI for Autonomous Vehicles
Dr. Sophia Patel	Lead, Generative AI Ethics	sophia.patel@ethicstech.org	Deepfake Detection & AIGC Policy
Dr. David Wilson	Senior Researcher, AI & Society	david.wilson@ethicstech.org	Employment Impact & Human-AI Interaction

Collaboration Opportunities

We welcome partnerships with academic, industry, and policy organizations. For research collaboration inquiries, please contact:
📧 Collaborations Team: collaborations.aie@ethicstech.org