BiasDetection
Evaluate the LangGraph system's ability to accurately identify bias, factual errors, and compliance violations in provided text content, and its efficiency in processing complex articles.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
BiasDetection
Assess the agent system's accuracy in identifying partisan bias, factual inaccuracies, and non-compliance with editorial guidelines in mock news articles or reports.
JSON object with 'article_text' (string, full content of the article), 'ground_truth_bias_type' (string, e.g., 'negative_framing', 'omission_of_facts'), 'ground_truth_factual_errors' (array of {statement, correct_fact_or_context}), and 'editorial_guidelines' (array of strings, specific rules).
JSON object with 'detected_bias_type' (string), 'bias_reasoning' (string), 'identified_factual_errors' (array of {statement, detected_error_explanation}), 'compliance_score' (float 0.0-1.0), and 'suggested_neutral_phrasing' (array of {original_text, suggested_text}).