Social Impact of AI: Research, Diversity and Inclusion Frameworks

The workshop proceedings SIAI 2025 constitutes the refereed proceedings of the fourth International Workshop on Social Impact of AI, SIAI 2025, which was held in Philadelphia, PA, USA, in March 2025.

The 8 full papers, 4 short papers, and one poster presented in these proceedings were carefully reviewed and selected from 26 submissions.

SIAI-ReDI 2025 centered on the development and assessment of inclusive AI frameworks
that prioritize ethical design, equitable deployment, and cultural context. The workshop's
call for papers invited submissions across a range of urgent topics, including:

Algorithmic fairness and transparency
Inclusive AI education and workforce strategies
AI in marginalized and underrepresented communities
Intersectionality, gender, and accessibility in AI
Cross-cultural AI governance and regulation
Public trust, participatory AI, and responsible design

Inhalt

.- AI for Social Good and Public Impact in Marginalized Communities: A
Cross-Cultural Framework.
.- Balancing Power and Ethics: A Framework for Addressing Human Rights
Concerns in Military AI.
.- Amharic Language Audio Data Search Engine.
.- Scaling Inclusive AI Education through Feedback Analytics in Underserved Contexts: Empowering Learners for the Generative AI Era.
.- Towards Culturally Inclusive Knowledge Dissemination Using AI Agents.
.- Evaluating the Feasibility of Universal Basic Income (UBI) in Bangladesh Using Multivariate Forecasting with XGBoost.
.- Enhancing Crop Yield Forecasting in Bangladesh Using Deep Learning Approach and Time-Series Analysis.
.- Group Privacy in AI Systems.
.- Shaping the Future of Learning: AI-Driven Adaptive Feedback for Programming Education in Resource-Constrained Setting.
.- Understanding vs. Generation: LLMs Are Better at Comprehending Low-Resource Languages Like Urdu Than Generating Text in Them.
.- Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection
in Textual Data.
.- Regional Determinants of Domestic Violence in India: Machine Learning Approach.