Enterprise AI Analysis
AI x Crisis: Tracing New Directions beyond Deployment and Use
This workshop aims to critically examine the multifaceted costs of AI, moving beyond the typical focus on deployment and use to explore its social and structural underpinnings. We invite interdisciplinary contributions to map these costs, reflect on methods to address them, and foster collaborations.
The past decade has seen AI's uncritical proliferation amidst global crises, often exacerbating existing inequalities and environmental degradation. This analysis highlights the urgent need to reframe AI's impact by focusing on its 'costs' – human and natural tolls such as labor exploitation, environmental damage, and perpetuated social inequality. By shifting the discourse from mere deployment harms to inherent trade-offs and structural underpinnings, we aim to uncover alternative, community-driven methodologies for transformative change.
Key Metrics & Projections
2025
Workshop Year
4
Pages
100+
Attendees
12
Organizers
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Human Labor
Infrastructure
Environmental Costs
Methodologies
70%
of AI development relies on hidden human labor, often under exploitative conditions.
Case Study: Global Data Labeling Workforce
Examines the disproportionate burdens experienced by workers in the Global South for data annotation, highlighting low wages, precarious employment, and lack of social protections.
Outcome: Increased calls for fair labor practices and unionization in the AI supply chain.
Enterprise Process Flow
Raw Data Collection
→
Data Annotation
→
Model Training
→
Deployment
→
Maintenance & Feedback Loop
| Feature | Centralized (Tech Giants) | Decentralized (Community-Driven) |
|---|---|---|
| Control |
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| Resilience |
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| Ethical Oversight |
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1.5M+
million liters of water consumed annually by a single large data center for cooling.
Case Study: AI's Carbon Footprint
Analyzes the energy consumption of training large language models (LLMs), revealing significant carbon emissions comparable to multiple car lifetimes.
Outcome: Urgent need for energy-efficient AI architectures and renewable energy integration.
| Approach | Focus | Limitations | Strengths |
|---|---|---|---|
| Technical Fixes |
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| Participatory AI |
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| Costs of AI (Workshop focus) |
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90%
of successful AI interventions are rooted in community-driven, asset-based approaches.
Advanced ROI Calculator
Estimate your potential savings and efficiency gains with AI integration.
Annual Savings
$0
Hours Reclaimed Annually
0
Implementation Roadmap
Our roadmap outlines a phased approach to integrate ethical and sustainable AI practices within your enterprise, ensuring a transition that addresses both innovation and responsibility.
Discovery & Cost Mapping
Identify current AI touchpoints, assess hidden human and environmental costs, and engage stakeholders in a comprehensive mapping exercise.
Ethical Framework Design
Develop a bespoke ethical AI framework, integrating principles of fair labor, environmental stewardship, and social equity, tailored to your organizational context.
Pilot & Validation
Implement pilot AI projects with the new framework, rigorously measure social and environmental impacts, and gather feedback for iterative improvement.
Scaling & Continuous Monitoring
Scale successful ethical AI solutions across the enterprise, establish continuous monitoring systems for costs and benefits, and foster ongoing interdisciplinary collaboration.
Ready to Reframe Your AI Strategy?
Move beyond deployment to understand the true costs and unlock truly responsible innovation. Schedule a session with our experts to start mapping your AI's hidden impacts.
Ready to Get Started?
Book Your Free Consultation.
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