Design Principles

Based on the insights from the workshops, the design and architecture of a reputation system for Deep Funding and the SingularityNET ecosystem are centered around a few key principles and objectives, with certain areas still requiring further exploration and decision-making. These principles are fundamental to creating a reputation system that is robust and equitable.

Design Principles:

• Interoperability: "keeping in mind that reputation is highly dependent on its context"​​. The system must be designed to seamlessly integrate and interact with various platforms within the SingularityNET ecosystem and possibly with external systems, ensuring that reputation scores are meaningful across different contexts.

• Layered Reputation Structure: Emphasizes the existence of "different layers of reputation"​​, suggesting that individuals may have multiple reputation scores reflecting different types of activities or contributions, from technical development to community engagement.

• Context Sensitivity: Acknowledges that "context can be bound to environments or time"​​, indicating that the value and impact of contributions may vary across different projects, times, or situations, necessitating a flexible approach to reputation assessment.

• Group-Based Contributions Recognition: "Individual contributions can be tagged to specific groups" and "Tasks on group tags generate group reputation"​​. This principle emphasizes the importance of acknowledging not just individual efforts but also collective contributions, recognizing the collaborative nature of projects within the ecosystem.

• Dynamic Adaptability of Reputation: Highlighting that "Reputation changes quickly" based on contributions and community engagement, underscoring the need for a system that can rapidly adapt to the evolving landscape and varying impact of contributions over time​​.

• Support for Restart and Redemption: The system's openness to allowing contributors to restart or improve their reputation, addressing concerns that "bad reputation bad actors should not be seen as banned forever for bad actions"​​. This underscores the value of growth, learning, and second chances within the community.

• Use of AI to Enhance Contributor Quality: The integration of "AI as copilot to support contributors in their actions" aims at "improving contributor quality where it matters the most"​​, suggesting a blend of technology and human judgment to elevate the overall quality of contributions and interactions within the ecosystem.

• Contributor Autonomy: Contributors have the right to "decide on which data they want to subscribe to the reputation system"​​, highlighting the importance of consent and control over personal data and contributions within the reputation framework.

• Verification Over Judgment: The system "just verifies subscribed contributions" without making value judgments (good or bad statements on contributions)​​. This principle focuses on factual verification of contributions rather than subjective evaluation, aiming to maintain objectivity in the reputation assessment process.

• Recognition of Diversity: A commitment to "recognise a wide range of skills"​​, ensuring that the reputation system values different forms of contributions equally, from coding to community building and beyond.

The next steps in the design and architecture phase will require translating these principles into specific technical requirements, developing prototypes, and engaging with the community for feedback and iteration.