Neutrality Score for Bias Detection & Fact-Checking
Inspired by discussions around content neutrality, this challenge focuses on building an advanced AI agent system capable of analyzing text for bias, factual inaccuracies, and neutrality standards. You will use LangGraph to design a Directed Acyclic Graph (DAG) workflow, orchestrating several specialized agents. Gemini 2.5 Pro (leveraging its Deep Think mode for nuanced analysis) will be central for identifying subtle biases and performing robust factual verification. OpenAI GPT 5 will provide alternative phrasing or counter-arguments to assess different perspectives. The system must implement the A2A (Agent-to-Agent) Protocol for seamless and secure communication between agents during cross-verification processes, ensuring claims are independently assessed. Hybrid instant/deep reasoning will allow agents to quickly triage simple facts while engaging in thorough, multi-step analysis for complex or contentious statements. The output should include a neutrality score and suggested revisions.
What you are building
The core problem, expected build, and operating context for this challenge.
Inspired by discussions around content neutrality, this challenge focuses on building an advanced AI agent system capable of analyzing text for bias, factual inaccuracies, and neutrality standards. You will use LangGraph to design a Directed Acyclic Graph (DAG) workflow, orchestrating several specialized agents. Gemini 2.5 Pro (leveraging its Deep Think mode for nuanced analysis) will be central for identifying subtle biases and performing robust factual verification. OpenAI GPT 5 will provide alternative phrasing or counter-arguments to assess different perspectives. The system must implement the A2A (Agent-to-Agent) Protocol for seamless and secure communication between agents during cross-verification processes, ensuring claims are independently assessed. Hybrid instant/deep reasoning will allow agents to quickly triage simple facts while engaging in thorough, multi-step analysis for complex or contentious statements. The output should include a neutrality score and suggested revisions.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
What you should walk away with
Master LangGraph for building complex, stateful, and observable DAG-based agent workflows.
Implement Gemini 2.5 Pro's Deep Think mode for intricate reasoning, factual precision, and subtle bias detection in controversial texts.
Design and build agents that communicate using the A2A Protocol, ensuring secure and structured exchange of information for cross-verification.
Integrate RAG pipelines with curated knowledge bases (e.g., fact-checking databases, reputable news archives) to provide authoritative context for content analysis.
Develop a hybrid reasoning system where agents can employ instant, heuristic checks for clear-cut facts and transition to deep, multi-step deliberation for ambiguous or highly biased statements.
Utilize OpenAI GPT 5 to generate alternative perspectives or reformulations of biased statements to aid in neutrality assessment.
Create a robust evaluation mechanism for assigning a 'neutrality score' based on agent findings and suggesting concrete revisions.
How this agent runs
The evaluation will focus on the system's ability to accurately detect bias, verify facts, generate meaningful neutrality scores, and propose effective revisions. Agent collaboration via A2A and the use of hybrid reas...
Challenge input
{ "text_passage": "string", "context_sources": [{ "title": "string", "url": "string", "content": "string" }] }
Agent execution
The configured agent processes the input under the challenge policy.
Evaluated output
{ "overall_neutrality_score": "float (0.0 - 1.0)", "biased_statements_detected": [{"statement": "string", "bias_type": "string", "reasoning": "stri...
- Verifies that the output is valid JSON and matches the specified schema.
- Confirms that the 'agent_collaboration_log' is populated, indicating successful agent-to-agent communication.
- Bias Detection Accuracy target: 4
- Python execution harness
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- The evaluation module defines the checks.
Recipe state
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Run this agent on your dataset and AI stack
Bring your dataset, model providers, and success criteria. We will scope the right managed run for your team.
Scope a managed run[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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