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| ai_audit_analysis_categories = { | |
| "AI Audit": [ | |
| "sentiment_analysis", | |
| "emotion_detection", | |
| "political_bias_detection", | |
| "stress_level_detection", | |
| "empathy_level_assessment", | |
| "mood_detection", | |
| "toxicity_detection" | |
| ], | |
| "GDPR": [ | |
| "Privacy_Assessment", | |
| "Consent_and_Transparency", | |
| "Data_Security", | |
| "Environmental_Impact"], | |
| "Toxicity": [ | |
| "Content_Moderation", | |
| "Reporting_Mechanism", | |
| "Content_Guidelines", | |
| "User_Education"], | |
| "Legal": [ | |
| "Privacy_Policy", | |
| "Data_Retention", | |
| "Consent_Mechanism"], | |
| "Context": [ | |
| "Ethical_AI", | |
| "Bais_Mitigation", | |
| "Fairness_Assestment", | |
| "Explainability"], | |
| "Governance": [ | |
| "Model_development", | |
| "Data_Quality", | |
| "Bais_Mitigation", | |
| "Fairness_Assestment" | |
| "Explainability" | |
| "User_Input"], | |
| "RiskManagement": [ | |
| "Corporate_Ethics", | |
| "Board_Management", | |
| "Stakeholder_Engagement"], | |
| "Robustness": [ | |
| "System_Reliability", | |
| "Quality_Assurance", | |
| "Stress_Testing", | |
| "Fail_Safe_Procedures"], | |
| "Sustainability": [ | |
| "Renewable_Resources", | |
| "Waste_Reduction", | |
| "Energy_Efficiency", | |
| "Sustainable_Practices"] | |
| } | |
| # Define a standard template for prompts | |
| STANDARD_PROMPT_TEMPLATE = "You are a data analysis assistant capable of {analysis_type} analysis. {specific_instruction} Respond with your analysis in JSON format. The JSON schema should include '{json_schema}'." | |
| def get_system_prompt(analysis_type: str) -> str: | |
| specific_instruction = ANALYSIS_TYPES.get(analysis_type, "Perform the analysis as per the specified type.") | |
| json_schema = JSON_SCHEMAS.get(analysis_type, {}) | |
| json_schema_str = ', '.join([f"'{key}': {value}" for key, value in json_schema.items()]) | |
| return (f"You are a data analyst API capable of {analysis_type} analysis. " | |
| f"{specific_instruction} Please respond with your analysis directly in JSON format " | |
| f"(without using Markdown code blocks or any other formatting). Always include confidence_score:number (0-1) with two decimals for result based on analysis" | |
| f"The JSON schema should include: {{{json_schema_str}}}.") | |
| ANALYSIS_TYPES = { | |
| "sentiment_analysis": "Analyze the sentiment of the provided text. Determine whether the sentiment is positive, negative, or neutral and provide a confidence score.", | |
| "emotion_detection": "Detect and identify the primary emotions expressed in the provided text. Provide a score for the intensity of the detected emotion.", | |
| "political_bias_detection": "Detect any political bias in the provided text, identifying leaning towards particular ideologies or parties.", | |
| "stress_level_detection": "Analyze the text to assess stress levels, identifying triggers and intensity of stress.", | |
| "empathy_level_assessment": "Assess the level of empathy expressed in the text, identifying empathetic responses and tendencies.", | |
| "mood_detection": "Detect the mood of the individual based on textual cues, ranging from happy to sad, calm to angry.", | |
| "toxicity_detection": "Identify and assess the level of toxicity in the provided text. Determine whether the text contains harmful, offensive, or inappropriate content and provide a score indicating the severity of the toxicity", | |
| # GDPR-related types | |
| "Consent_and_Transparency": "Evaluate how consent is obtained and the level of transparency provided to users regarding data usage.", | |
| "Data_Security": "Assess the measures in place for data security, including vulnerabilities and compliance with security standards.", | |
| "Privacy_Assessment": "Analyze the overall privacy practices, including policy compliance, data minimization, and user data accessibility.", | |
| "Environmental_Impact": "Assess the environmental impact of data processing practices, including carbon footprint and energy efficiency.", | |
| # Toxicity-related types | |
| "Content_Moderation": "Evaluate the effectiveness of content moderation practices, including automated and human moderation efforts.", | |
| "Reporting_Mechanism": "Assess the ease and effectiveness of reporting mechanisms for inappropriate or harmful content.", | |
| "Content_Guidelines": "Analyze the clarity and comprehensiveness of content guidelines and their enforcement consistency.", | |
| "User_Education": "Evaluate the availability and accessibility of educational resources for users regarding appropriate content and behavior.", | |
| # Legal-related types | |
| "Privacy_Policy": "Analyze the clarity and compliance of a privacy policy with legal standards.", | |
| "Data_Retention": "Evaluate the data retention practices, including periods, deletion policies, and legal compliance.", | |
| "Consent_Mechanism": "Assess the clarity and effectiveness of the consent mechanism in place for data collection and usage.", | |
| "GDPR_Compliance": "Evaluate the level of GDPR compliance in data handling, protection measures, and breach notification protocols.", | |
| # Context-related types | |
| "Ethical_AI": "Assess adherence to ethical standards in AI practices, including identification and mitigation of ethical issues.", | |
| "Bias_Mitigation": "Evaluate the presence and mitigation of bias in data or algorithms.", | |
| "Fairness_Assessment": "Assess fairness in AI systems, identifying affected groups and providing recommendations for improvement.", | |
| "Explainability": "Evaluate the transparency and explainability of AI models to users.", | |
| # Governance-related types | |
| "Model_Development": "Analyze the process of model development, including team composition and ethical considerations.", | |
| "Data_Quality": "Assess the quality of data used, focusing on accuracy, completeness, and timeliness.", | |
| "User_Input": "Evaluate the mechanisms for and impact of user feedback on the system.", | |
| # Risk Management-related types | |
| "Corporate_Ethics": "Assess the ethical practices within a corporation, including employee training and ethics code adherence.", | |
| "Board_Management": "Evaluate the effectiveness and diversity of board management and its compliance with ethical standards.", | |
| "Stakeholder_Engagement": "Analyze stakeholder engagement practices, including inclusion, feedback mechanisms, and satisfaction.", | |
| "Risk_Management": "Assess the identification, mitigation, and monitoring of risks within an organization.", | |
| # Robustness-related types | |
| "System_Reliability": "Evaluate the reliability and resilience of a system, including uptime and redundancy measures.", | |
| "Quality_Assurance": "Assess the quality assurance practices, including compliance with standards and testing frequency.", | |
| "Stress_Testing": "Analyze the system's robustness through stress testing and identify weaknesses.", | |
| "Fail_Safe_Procedures": "Evaluate the effectiveness of fail-safe procedures in place for system failures.", | |
| # Sustainability-related types | |
| "Renewable_Resources": "Assess the use of renewable resources and sustainability goals in operations.", | |
| "Waste_Reduction": "Evaluate waste management practices, reduction rates, and recycling initiatives.", | |
| "Energy_Efficiency": "Analyze energy consumption and efficiency, including energy-saving measures and audits.", | |
| "Sustainable_Practices": "Evaluate the adoption of sustainable practices, including training and overall impact." | |
| } | |
| JSON_SCHEMAS = { | |
| "sentiment_analysis": { | |
| "sentiment": "string (positive, negative, neutral)", | |
| "confidence_score": "number (0-1)", | |
| "text_snippets": "array of strings (specific text portions contributing to sentiment)" | |
| }, | |
| "emotion_detection": { | |
| "emotion": "string (primary emotion detected)", | |
| "confidence_score": "number (0-1)", | |
| "secondary_emotions": "array of objects (secondary emotions and their scores)" | |
| }, | |
| "political_bias_detection": { | |
| "bias": "string (left, right, neutral)", | |
| "confidence_score": "number (0-1)", | |
| "bias_indicators": "array of strings (elements indicating bias)", | |
| "political_alignment_score": "number (quantifying degree of political bias)" | |
| }, | |
| "stress_level_detection": { | |
| "stress_level": "string", | |
| "stress_triggers": "array of strings" | |
| }, | |
| "empathy_level_assessment": { | |
| "empathy_level": "string", | |
| "empathetic_responses": "array of strings" | |
| }, | |
| "mood_detection": { | |
| "mood": "string", | |
| "mood_intensity": "number" | |
| }, | |
| "toxicity_detection": { | |
| "toxicity_level": "string (none, low, medium, high)", | |
| "toxicity_flags": "array of strings (specific words or phrases contributing to toxicity)", | |
| "contextual_factors": "array of objects (additional contextual elements influencing toxicity interpretation)" | |
| }, | |
| # GDPR-related schemas | |
| "Consent_and_Transparency": { | |
| "consent_obtained": "boolean", | |
| "transparency_level": "string (low, medium, high)", | |
| "missing_information": "array of strings (information not clearly presented or missing)", | |
| "user_understanding": "string (poor, average, good)" | |
| }, | |
| "Data_Security": { | |
| "security_status": "string (secure, at risk, breached)", | |
| "vulnerability_points": "array of strings (specific areas of potential vulnerability)", | |
| "data_encryption": "boolean", | |
| "compliance_status": "string (compliant, partially compliant, non-compliant)" | |
| }, | |
| "Environmental_Impact": { | |
| "carbon_footprint": "number (metric tons of CO2 equivalent)", | |
| "energy_efficiency": "string (low, moderate, high)", | |
| "sustainable_practices": "boolean", | |
| "environmental_impact_score": "number (0-100)" | |
| }, | |
| "Privacy_Assessment": { | |
| "overall_privacy_status": "string (positive, negative)" , | |
| "privacy_policy_compliance": "string (compliant, partially compliant, non-compliant)", | |
| "data_minimization": "boolean", | |
| "user_data_accessibility": "string (none, limited, full)", | |
| "anonymization": "boolean" | |
| }, | |
| # Toxicity-related schemas | |
| "Content_Moderation": { | |
| "moderation_effectiveness": "string (low, medium, high)", | |
| "moderated_content_types": "array of strings (types of content being moderated)", | |
| "automated_moderation": "boolean", | |
| "human_moderation": "boolean" | |
| }, | |
| "Reporting_Mechanism": { | |
| "reporting_ease": "string (easy, moderate, difficult)", | |
| "response_time": "string (fast, average, slow)", | |
| "report_feedback": "string (detailed, minimal, none)" | |
| }, | |
| "Content_Guidelines": { | |
| "clarity": "string (clear, somewhat clear, unclear)", | |
| "comprehensiveness": "string (comprehensive, partial, lacking)", | |
| "enforcement_consistency": "string (consistent, inconsistent)" | |
| }, | |
| "User_Education": { | |
| "educational_resources_available": "boolean", | |
| "resource_accessibility": "string (easy, moderate, difficult)", | |
| "user_comprehension_level": "string (high, medium, low)" | |
| }, | |
| # Legal-related schemas | |
| "Privacy_Policy": { | |
| "clarity": "string (clear, somewhat clear, unclear)", | |
| "compliance": "string (compliant, partially compliant, non-compliant)", | |
| "user_rights": "array of strings (specific rights mentioned in policy)" | |
| }, | |
| "Consent_Mechanism": { | |
| "mechanism_clarity": "string (clear, somewhat clear, unclear)", | |
| "user_control": "boolean", | |
| "opt_in_out": "string (opt-in, opt-out, not applicable)" | |
| }, | |
| "GDPR_Compliance": { | |
| "compliance_level": "string (fully compliant, partially compliant, non-compliant)", | |
| "data_protection_officer": "boolean", | |
| "breach_notification": "boolean" | |
| }, | |
| # Context-related schemas | |
| "Ethical_AI": { | |
| "ethical_standards_adherence": "string (high, medium, low)", | |
| "ethical_issues_identified": "array of strings", | |
| "mitigation_measures": "array of strings" | |
| }, | |
| "Bias_Mitigation": { | |
| "bias_identified": "boolean", | |
| "bias_types": "array of strings", | |
| "mitigation_strategies": "array of strings" | |
| }, | |
| "Fairness_Assessment": { | |
| "fairness_level": "string (high, medium, low)", | |
| "affected_groups": "array of strings", | |
| "improvement_recommendations": "array of strings" | |
| }, | |
| "Explainability": { | |
| "model_transparency": "string (transparent, opaque)", | |
| "explanation_comprehensibility": "string (high, medium, low)", | |
| "user_friendly_explanations": "boolean" | |
| }, | |
| # Governance-related schemas | |
| "Model_Development": { | |
| "development_process": "string (structured, ad-hoc, undefined)", | |
| "team_composition": "array of strings (roles involved)", | |
| "ethics_considerations": "boolean" | |
| }, | |
| "Data_Quality": { | |
| "accuracy_level": "string (high, medium, low)", | |
| "completeness": "string (complete, partial, incomplete)", | |
| "timeliness": "string (up-to-date, outdated)" | |
| }, | |
| "User_Input": { | |
| "user_feedback_mechanism": "boolean", | |
| "feedback_responsiveness": "string (responsive, moderately responsive, unresponsive)", | |
| "user_input_impact": "string (high, medium, low)" | |
| }, | |
| # Risk Management-related schemas | |
| "Corporate_Ethics": { | |
| "ethics_code": "string (exists, partial, none)", | |
| "employee_training": "boolean", | |
| "ethics_violations": "array of strings" | |
| }, | |
| "Board_Management": { | |
| "board_structure": "string (effective, average, ineffective)", | |
| "board_diversity": "boolean", | |
| "board_ethics_compliance": "string (compliant, non-compliant)" | |
| }, | |
| "Stakeholder_Engagement": { | |
| "stakeholder_inclusion": "string (inclusive, partially inclusive, exclusive)", | |
| "feedback_mechanism": "boolean", | |
| "stakeholder_satisfaction": "string (high, medium, low)" | |
| }, | |
| "Risk_Management": { | |
| "risk_identification": "boolean", | |
| "risk_mitigation_strategies": "array of strings", | |
| "risk_monitoring": "boolean" | |
| }, | |
| # Robustness-related schemas | |
| "System_Reliability": { | |
| "uptime_percentage": "number (0-100)", | |
| "system_resilience": "string (high, medium, low)", | |
| "redundancy_measures": "boolean" | |
| }, | |
| "Quality_Assurance": { | |
| "quality_standards": "array of strings", | |
| "testing_frequency": "string (frequent, occasional, rare)", | |
| "quality_assurance_compliance": "string (compliant, partially compliant, non-compliant)" | |
| }, | |
| "Stress_Testing": { | |
| "stress_test_pass_rate": "number (0-100)", | |
| "identified_weaknesses": "array of strings", | |
| "improvement_actions": "array of strings" | |
| }, | |
| "Fail_Safe_Procedures": { | |
| "procedures_defined": "boolean", | |
| "execution_frequency": "string (regular, occasional, never)", | |
| "effectiveness": "string (effective, partially effective, ineffective)" | |
| }, | |
| # Sustainability-related schemas | |
| "Renewable_Resources": { | |
| "resource_usage": "string (high, moderate, low)", | |
| "renewable_resource_percentage": "number (0-100)", | |
| "sustainability_goals": "boolean" | |
| }, | |
| "Waste_Reduction": { | |
| "waste_management_practices": "string (effective, average, poor)", | |
| "reduction_rate": "number (0-100)", | |
| "recycling_initiatives": "boolean" | |
| }, | |
| "Energy_Efficiency": { | |
| "energy_consumption": "string (high, moderate, low)", | |
| "energy_saving_measures": "array of strings", | |
| "energy_audit": "boolean" | |
| }, | |
| "Sustainable_Practices": { | |
| "practice_adoption": "string (widespread, partial, none)", | |
| "sustainability_training": "boolean", | |
| "sustainability_impact": "string (high, medium, low)" | |
| } | |
| } | |