Use Case #9 Agentic AI

[anirao28]

Discussed in #13

^{Originally posted by wenjing April 24, 2024}

1. Context
(i) Problem: Current AI assistants lack transparency, provenance, and secure distribution mechanisms, leading to trust issues and privacy concerns.
(ii) Existing methods' shortcomings: Current solutions often rely on centralized systems, lack verifiable content origins, and have limited privacy safeguards.
(iii) Proposed solution: Integrating TSP and C2PA can enhance AI assistants by providing provenance information, secure content distribution, and decentralized communication.

2. Actors

• End Users: Interact with AI assistants for various tasks, benefiting from improved trust and privacy.
• Content Creators: Generate and distribute AI-assisted content with verifiable provenance.
• Service Providers: Develop and maintain AI assistants with enhanced security and transparency.
• Regulators: Ensure compliance with data protection and AI ethics standards.

These actors can use TSP+C2PA to create, distribute, and consume AI-generated content with verifiable origins and secure transmission, addressing trust and privacy concerns.

3. Data

• User Queries: Text or voice inputs from users.
• AI-Generated Responses: Text, audio, or visual content produced by the AI.
• Provenance Metadata: C2PA-compliant information about content origin and processing history.
• User Preferences and History: Securely stored and transmitted via TSP.
• Third-party Information: External data sources integrated into AI responses.

4. Governance

• Data Protection: Implement robust measures to safeguard user data and ensure GDPR compliance.
• Transparency: Provide clear information on AI capabilities, limitations, and data usage.
• Accountability: Establish mechanisms for auditing AI decisions and addressing user concerns.
• Ethical AI: Develop guidelines for responsible AI development and deployment.
• Interoperability: Ensure compatibility with existing standards and regulations.

5. AI Models

• Natural Language Processing (NLP) Models: For understanding and generating human-like text.
• Computer Vision Models: For image and video analysis and generation.
• Speech Recognition and Synthesis Models: For voice interactions.
• Recommendation Systems: For personalized suggestions based on user preferences.
• Multi-modal Models: Integrating various data types for comprehensive understanding and generation.

6. Output

• Conversational Interfaces: AI-generated responses in chat applications or voice assistants.
• Content Creation: AI-assisted generation of text, images, or videos with C2PA provenance.
• Decision Support: AI-generated recommendations for user actions or choices.
• Data Visualization: AI-processed information presented in graphical formats.
• Integration with External Systems: AI outputs securely transmitted to third-party applications or services via TSP.
  Agentic AI
  Agent AI, also known as intelligent agents, refers to autonomous entities that perceive their environment, reason about their goals, and take actions to achieve those goals. These agents can operate independently or collaboratively to perform tasks, make decisions, and interact with humans or other agents. These AI assistants integrate with devices or act as virtual companions, providing a range of personalized services.

Problems to Solve:
• Response Accuracy: Existing virtual assistants may struggle with accurately understanding and responding to user queries, leading to frustration and inefficiency.
• Personalization: Current virtual assistants often lack personalization, providing generic responses that may not meet individual user needs.
• Privacy Concerns: Users may be apprehensive about sharing sensitive information with virtual assistants due to concerns about data privacy and security.
• Integration Complexity: Integrating virtual assistants with various systems and platforms can be complex and time-consuming.
• Difficulty in controlling and understanding how your personal AI assistant is using your data.
• Lack of transparency in the decision-making process of your personal AI assistant.
• Difficulty in ensuring that your personal AI assistant is acting in your best interests.

Weaknesses in Current Practices:
• Limited Context Understanding: Virtual assistants often struggle to understand the context of a conversation, leading to inaccurate responses.
• Lack of Provenance: Current virtual assistants may not provide information about the source or credibility of the data they provide, undermining trust.
• Privacy Risks: Virtual assistants may store and analyze sensitive user data, raising concerns about privacy breaches and data misuse.
• Fragmented Ecosystem: Integration with third-party services and platforms may be challenging due to the fragmented nature of the virtual assistant ecosystem.

How C2PA+TSP Can Help:
• Provenance Information: C2PA can provide provenance information for certain content types, allowing users to verify the credibility and reliability of information provided by the virtual assistant.
• Secure and Confidential Communication: TSP supports authentic and optionally confidential distribution of content, ensuring that user interactions with the virtual assistant are secure and private.
• Decentralized Communication: Unlike typical web service models, TSP facilitates decentralized and distributed communication, enabling virtual assistants to interact seamlessly across boundaries without relying on centralized servers.
• Scalability: TSP's scalability ensures that virtual assistants can handle a large volume of user interactions efficiently, improving responsiveness and performance.

What Is Not in Scope:
• Natural Language Processing (NLP) Algorithms: Detailed specifications of the NLP algorithms used by the virtual assistant.
• User Interface Design: Design considerations for the user interface of the virtual assistant application.
• Integration with Specific Platforms: Detailed integration requirements with specific third-party services or platforms.
• Potential Bias: C2PA and TSP don't address potential biases within the AI model used by the assistant, which could influence health-related recommendations.

Additional Considerations:
the importance of responsible development and deployment of agentic AI systems, which includes personal AI assistants. This translates to ensuring:
Clear and Transparent Communication: The AI assistant should communicate its capabilities and limitations clearly, and how it uses your data.
User Control: You should have control over how the AI assistant interacts with your data and environment.
Accountability: There should be mechanisms in place to hold developers and service providers accountable for any misuse of your data or harm caused by the AI assistant.