ethical_agent.py

#!/usr/bin/env python
"""
Ethical Reasoning Agent using the Ollama_Agents API

This script implements an ethical reasoning agent based on the Ollama_Agents
framework described in the API documentation. It creates an agent session,
provides ethical reasoning on various topics, and demonstrates the structured
ethical reasoning process.
"""

import os
import sys
import json
import random
import argparse
from datetime import datetime

# Mock implementation of the Ollama_Agents API classes
class AgentAPI:
    """Implementation of the Agent API for ethical reasoning"""
    
    @staticmethod
    def list_agents():
        """Return all available agent types"""
        return {
            "ethical": "Specialized for ethical reasoning and analysis",
            "research": "Specialized for research tasks",
            "debug": "Specialized for debugging code",
            "chat": "General conversational agent",
            "memory": "Agent with enhanced memory capabilities"
        }
    
    @staticmethod
    def create_session(agent_type, config=None):
        """Create a new agent session"""
        if agent_type not in AgentAPI.list_agents():
            raise ValueError(f"Unknown agent type: {agent_type}")
        
        # Create a session ID based on timestamp
        import uuid
        session_id = str(uuid.uuid4())
        print(f"Created {agent_type} agent session: {session_id}")
        return session_id
    
    @staticmethod
    def get_response(session_id, user_input):
        """Process user input and return agent response"""
        # This is where we would normally connect to the Ollama API
        # For demonstration, we'll create a structured ethical analysis
        
        # Simple keyword detection to determine ethical category
        ethical_categories = {
            "privacy": ["privacy", "data", "surveillance", "tracking", "consent", "collection"],
            "fairness": ["fairness", "bias", "discrimination", "equality", "equity", "justice"],
            "autonomy": ["autonomy", "freedom", "choice", "control", "coercion", "manipulation"],
            "harm": ["harm", "injury", "damage", "pain", "suffering", "safety", "risk"],
            "deception": ["deception", "truth", "honesty", "transparency", "misleading", "lying"]
        }
        
        # Determine the main ethical category
        detected_category = "general_ethics"
        max_matches = 0
        
        for category, keywords in ethical_categories.items():
            matches = sum(1 for keyword in keywords if keyword.lower() in user_input.lower())
            if matches > max_matches:
                max_matches = matches
                detected_category = category
        
        # Generate the ethical reasoning response
        ethical_analysis = generate_ethical_reasoning(user_input, detected_category)
        
        # Format the response
        response = {
            "reply": ethical_analysis,
            "session_id": session_id,
            "category": detected_category
        }
        
        return response
    
    @staticmethod
    def execute_command(session_id, command, params=None):
        """Execute a slash command with optional parameters"""
        if command == "ethical_analysis":
            passage = params.get("passage", "")
            category = params.get("category", "general_ethics")
            return {
                "result": generate_ethical_reasoning(passage, category)
            }
        elif command == "list_categories":
            return {
                "categories": [
                    "privacy", "fairness", "autonomy", "harm", "deception", 
                    "justice", "beneficence", "non-maleficence"
                ]
            }
        else:
            return {"error": f"Unknown command: {command}"}
    
    @staticmethod
    def terminate_session(session_id):
        """End an agent session"""
        print(f"Terminated session: {session_id}")
        return True

class ReasoningAPI:
    """API for accessing reasoning and cognitive functions"""
    
    @staticmethod
    def generate_hypotheses(question, count=3):
        """Generate multiple hypotheses for a question"""
        # Demo implementation
        hypotheses = [
            f"Hypothesis 1: {question} may be related to privacy concerns",
            f"Hypothesis 2: {question} could involve issues of consent",
            f"Hypothesis 3: {question} might have implications for autonomy"
        ]
        return hypotheses[:count]
    
    @staticmethod
    def apply_causal_reasoning(situation):
        """Apply causal reasoning to a situation"""
        return {
            "causes": ["Lack of transparency", "Economic incentives", "Technological capabilities"],
            "effects": ["Privacy violations", "Power imbalances", "Erosion of trust"],
            "analysis": "The situation reflects a complex interaction between technological capabilities and economic incentives."
        }
    
    @staticmethod
    def evaluate_ethical_implications(action):
        """Evaluate ethical implications of an action"""
        return {
            "stakeholders": ["Individuals", "Companies", "Society at large"],
            "principles": ["Respect for autonomy", "Beneficence", "Non-maleficence", "Justice"],
            "analysis": generate_ethical_reasoning(action, "general_ethics")
        }

def generate_ethical_reasoning(passage, category):
    """Generate ethical reasoning content"""
    # Format based on structured ethical reasoning
    thought_section = generate_thought_section(passage, category)
    solution_section = generate_solution_section(passage, category)
    
    complete_analysis = f"""<|begin_of_thought|>
{thought_section}
<|end_of_thought|>

<|begin_of_solution|>
{solution_section}
<|end_of_solution|>"""
    
    return complete_analysis

def generate_thought_section(passage, category):
    """Generate the thought section of ethical reasoning"""
    # For demonstration purposes, we'll create a structured analysis
    # In a real implementation, this would use the LLM through Ollama
    
    ethical_principles = {
        "privacy": [
            "People have a right to control their personal information",
            "Informed consent is necessary for ethical data collection",
            "There should be transparency about how data is used",
            "Data minimization limits collection to what is necessary"
        ],
        "fairness": [
            "Resources and opportunities should be distributed equitably",
            "Discrimination based on protected characteristics is unjust",
            "Systems should be designed to reduce rather than amplify biases",
            "Marginalized groups deserve special consideration"
        ],
        "autonomy": [
            "Individuals should be free to make their own choices",
            "Manipulation and coercion undermine autonomy",
            "Meaningful choice requires adequate information and options",
            "People should be treated as ends in themselves, not merely as means"
        ],
        "harm": [
            "Actions should avoid causing unnecessary suffering",
            "The benefits of an action should outweigh potential harms",
            "Vulnerable populations deserve special protection",
            "Both direct and indirect harms must be considered"
        ],
        "deception": [
            "Honesty and truthfulness are foundational ethical values",
            "Misleading others violates their trust and autonomy",
            "Transparency promotes accountability",
            "Withholding relevant information can be a form of deception"
        ],
        "general_ethics": [
            "Respect for human dignity is fundamental",
            "Actions should consider the welfare of all affected parties",
            "Ethical decisions require balancing competing values",
            "Context matters in ethical evaluation"
        ]
    }
    
    # Select relevant principles
    selected_principles = ethical_principles.get(category, ethical_principles["general_ethics"])
    random.shuffle(selected_principles)
    principles = selected_principles[:3]  # Choose a subset
    
    # Create the thought content
    thought_content = f"""I'll analyze this ethical situation concerning {category} in several steps:

1. **Identifying key ethical concerns**:
   This scenario involves "{passage}" which raises important questions about {category}.
   The core ethical concerns include respect for individual rights, potential consequences, and relevant ethical principles.

2. **Applying ethical principles**:
   - {principles[0]}
   - {principles[1]}
   - {principles[2]}

3. **Considering multiple perspectives**:
   - From an individual's perspective: People have expectations about how their information is handled and used.
   - From an organizational perspective: There may be legitimate purposes for the activities described.
   - From a societal perspective: Practices that become normalized can have broader implications.

4. **Examining consequences**:
   The short-term consequences might include immediate benefits or harms to individuals.
   The long-term consequences could involve erosion of trust, normalization of problematic practices, or setting precedents.

5. **Weighing competing values**:
   This situation involves a tension between values like efficiency and utility versus respect for individual rights.
   There may also be conflicts between short-term benefits and long-term ethical concerns."""

    return thought_content

def generate_solution_section(passage, category):
    """Generate the solution section of ethical reasoning"""
    # For demonstration purposes
    
    solution_content = f"""Based on thorough ethical analysis of "{passage}" focused on {category}, I conclude:

1. **Ethical Assessment**: 
   This situation raises significant concerns about respect for individual autonomy and informed consent.
   
2. **Key Considerations**:
   - The practice should be evaluated based on transparency, consent, and proportionality
   - Both intended and unintended consequences must be considered
   - Alternative approaches that better respect ethical principles should be explored

3. **Recommendations**:
   - Implement clear transparency measures to ensure informed decision-making
   - Establish meaningful consent mechanisms that give individuals real choice
   - Adopt ethical frameworks that prioritize human dignity over pure utility
   - Regularly review practices to ensure alignment with evolving ethical standards

4. **Balanced Approach**:
   While there may be legitimate purposes for certain practices, these must be balanced against ethical obligations to respect individual rights and prevent harm. Ethical solutions often require thoughtful compromise and ongoing evaluation."""

    return solution_content

def main():
    parser = argparse.ArgumentParser(description="Ethical Reasoning Agent Demo")
    parser.add_argument("--interactive", action="store_true", help="Run in interactive mode")
    parser.add_argument("--scenario", type=str, help="Ethical scenario to analyze")
    parser.add_argument("--category", type=str, default="general_ethics", 
                        choices=["privacy", "fairness", "autonomy", "harm", "deception", "general_ethics"],
                        help="Ethical category to focus on")
    parser.add_argument("--output", type=str, help="Output file for results")
    args = parser.parse_args()
    
    print("Ethical Reasoning Agent Demo")
    print("============================")
    
    # Create agent session
    session_id = AgentAPI.create_session("ethical")
    
    try:
        if args.interactive:
            # Interactive mode
            print("\nEnter ethical scenarios to analyze. Type 'exit' to quit.")
            
            while True:
                user_input = input("\nScenario: ")
                if user_input.lower() == 'exit':
                    break
                
                response = AgentAPI.get_response(session_id, user_input)
                print("\n" + response["reply"])
                
                # Save the response
                timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
                output_file = args.output or f"ethical_analysis_{timestamp}.json"
                with open(output_file, 'w') as f:
                    json.dump(response, f, indent=2)
                print(f"\nAnalysis saved to {output_file}")
                
        elif args.scenario:
            # Process a single scenario
            response = AgentAPI.get_response(session_id, args.scenario)
            print("\n" + response["reply"])
            
            # Save the response
            output_file = args.output or "ethical_analysis.json"
            with open(output_file, 'w') as f:
                json.dump(response, f, indent=2)
            print(f"\nAnalysis saved to {output_file}")
            
        else:
            # Demo mode with preset scenarios
            scenarios = [
                "Companies track user browsing habits to target advertisements without explicit consent",
                "AI systems make hiring decisions that may disadvantage certain demographic groups",
                "Smart home devices record conversations for product improvement purposes",
                "Social media platforms design addictive features to maximize user engagement"
            ]
            
            # Process each scenario
            print("\nProcessing demo scenarios...")
            results = []
            
            for i, scenario in enumerate(scenarios):
                print(f"\nScenario {i+1}: {scenario}")
                response = AgentAPI.get_response(session_id, scenario)
                results.append(response)
                
                # Display a brief summary
                category = response.get("category", "general_ethics")
                print(f"Analyzed from {category} perspective")
                
                # Optional: uncomment to show full response
                # print("\n" + response["reply"])
            
            # Save the results
            output_file = args.output or "ethical_analyses.json"
            with open(output_file, 'w') as f:
                json.dump(results, f, indent=2)
            print(f"\nAll analyses saved to {output_file}")
            
    finally:
        # Clean up
        AgentAPI.terminate_session(session_id)
        print("\nSession terminated. Goodbye!")

if __name__ == "__main__":
    main()