Use‐Case Examples - eirenicon/Ardens GitHub Wiki

Purpose

This guide illustrates how AI can be used as a research assistant under Ardens' epistemic and operational standards. Each example is drawn from real or plausible use cases, offering tactical guidance on extracting value while minimizing distortion, bias, or false confidence. These examples are designed to help analysts, educators, and civic actors become more fluent in the how, when, and why of using AI tools effectively and responsibly.

This document complements AI Collaborators (Rated) and AI Compass: Systems Evaluation and Deep Audit by offering hands-on illustrations of those frameworks in action.

Example Use Case Format

Each scenario below includes:

  • Use Case: The task or inquiry posed.
  • Model Used: The AI tool selected.
  • Strengths Leveraged: What made the model useful.
  • Risks/Concerns: What limitations or distortions were observed.
  • Mitigations: What was done to maintain epistemic and ethical integrity.
  • Ardens Assessment: ✅ Recommended, ⚠️ Caution, ❌ Not Recommended

Use Case 1: Comparative Legal Systems (Query)

Task: Compare refugee rights frameworks in Germany, Poland, and the U.S.

  • Model Used: Gemini (Pro)
  • Strengths Leveraged: Fast synthesis of publicly available legal summaries and international treaties; good at showing structural commonalities and procedural distinctions.
  • Risks/Concerns: Tendency to use euphemisms or downplay contested political contexts (e.g. U.S. Title 42).
  • Mitigations: Follow-up prompts emphasized critical viewpoints and case-based examples; results cross-checked with NGO and academic sources.
  • Ardens Assessment: ✅ Recommended

Use Case 2: Ideological Drift in Media Summaries

Task: Analyze bias drift in summarization of political articles from multiple AI models.

  • Models Used: ChatGPT 4, Claude 3.5, Gemini 1.5
  • Strengths Leveraged: Model triangulation reveals subtle editorial filters (e.g. Claude omits economic critiques, GPT centers equity themes).
  • Risks/Concerns: None of the summaries were wholly neutral; all had noticeable ideological imprint.
  • Mitigations: Used a blind-prompt format with identical inputs, recorded variances, then surfaced those as part of the analysis.
  • Ardens Assessment: ⚠️ Caution — valuable if used with comparative discipline

Use Case 3: Rapid Adversarial Testing of Tone & Framing

Task: Examine how an AI model responds to emotionally charged or ethically ambiguous prompts.

  • Model Used: ChatGPT 4 (via Copilot)
  • Strengths Leveraged: Responsive to tone shifts; useful for testing system guardrails under pressure.
  • Risks/Concerns: At times over-sanitized content, resisted acknowledging harm-related topics unless asked in passive voice.
  • Mitigations: Used direct and indirect phrasing, varied the emotional content, logged evasions or inconsistencies.
  • Ardens Assessment: ⚠️ Caution — essential for audit, but results vary by model

Use Case 4: Researching Obscure Geopolitical Figures

Task: Request a profile on a mid-level paramilitary figure in West Africa.

  • Model Used: Claude 3 Opus
  • Strengths Leveraged: Discursive style helped articulate relationships among actors, especially when connected to global networks.
  • Risks/Concerns: Some invented affiliations and unsupported claims; uncertain sourcing.
  • Mitigations: Required repeated clarification requests and “source backtrace” prompts; ultimately triangulated with external databases.
  • Ardens Assessment: ⚠️ Caution

Use Case 5: Mapping Controversial Discourse (e.g. AI & Palestine)

Task: Identify contrasting narratives in English-language discourse on Gaza-based AI surveillance.

  • Model Used: HuggingChat w/ Mistral backend
  • Strengths Leveraged: Raw model with fewer guardrails surfaced polarizing framings that other models concealed or softened.
  • Risks/Concerns: Generated unfiltered, occasionally inflammatory phrasing; high risk of hallucination.
  • Mitigations: Treated results as narrative terrain mapping, not fact; all points validated externally.
  • Ardens Assessment: ⚠️ Caution — powerful, but must not be trusted at face value

Observations Across Use Cases

  • Prompt structure matters: Requests framed as multi-perspective or evidence-seeking tend to yield higher-quality outputs.
  • Model comparison reveals bias: Running the same query across different LLMs surfaces divergent assumptions, framings, and limitations.
  • Human verification is non-optional: Every successful use case included post hoc validation or triangulation with trusted sources.

Suggested Practices Going Forward

  • Maintain a shared prompt library with field-tested templates for research, synthesis, and critical analysis.
  • Build model performance logs that track known strengths, blind spots, and ideological tics across specific domains.
  • Encourage collaborative vetting, where two or more users test the same question using different approaches or models.

Choosing an AI assistant is less like using a calculator and more like hiring a research intern with unknown training. Evaluate accordingly.

Category:Projects