Overcoming Voting Apathy: A Facebook AI-Inspired Solution

Overcoming Voting Apathy: A Facebook AI-Inspired Solution
Photo by Glen / Unsplash

In the world of Decentralized Autonomous Organizations (DAOs), voting is a crucial process that allows members to make collective decisions. Everyone gets a say, everyone gets a vote. But there's a catch.

With so many votes to cast on so many issues, people can get tired. This is known as 'voting Apathy.' And when people get tired, they stop voting. That's a problem.

But what if there was a way to make this process less overwhelming? What if you could see only the proposals that matter most to you?

Enter the concept of a "ranking score," inspired by none other than Facebook's AI-powered feature that personalizes user feeds.

Facebook's AI uses a ranking score to determine the content that users see on their feed. When a user interacts with a post by clicking "Show More" or "Show Less," the AI adjusts the ranking score of that post and similar content accordingly. This allows the AI to tailor the user's feed based on their preferences, even if they don't interact with the "Show More" or "Show Less" options frequently.

Imagine applying a similar concept to DAO voting. By assigning a 'ranking score' to each proposal based on a member's past voting behavior, interests, and the proposal's overall relevance, we could potentially streamline the voting process.

Source: Facebook AI - The left column, known as the user tower, takes into account a user's profile and recent Facebook activity, including Show More/Show Less feedback, and translates this into a continuous token sequence. This sequence is then processed using cutting-edge transformer models to create an embedded representation. Conversely, the content tower on the right processes various elements of a post – be it text, image, poster details, audio, video, comments, and so on – and creates its own embedding. These two embeddings are then concurrently trained to predict a user's preference for showing more or less of a specific post.

DAOs, Are You Ready for an Upgrade?

Here's how a "ranking score" system, underpinned by AI, could make the process less daunting, reducing voting fatigue and encouraging greater participation.

  1. Prioritize Proposals: By weighing various factors like proposal importance, historical voting patterns, and individual members' interests, a ranking score system could handpick crucial proposals, helping build a targeted voting approach.
  2. Reduce Noise: The system could filter out less relevant proposals, ensuring members focus their attention on key issues.
  3. Direct Feedback: The "Show More/Show Less" feature could allow members to provide direct feedback on the types of proposals they are interested in, further refining the prioritization process.
  4. Automated Voting Suggestions: Inspired by past voting behaviors and specific interests, the ranking system could offer personalized voting suggestions. Make way for informed and no-stress decisions!
  5. Scheduled Voting: The system could optimize voting times based on member availability, allowing individuals to engage more efficiently.

So, instead of wrestling with voting apathy, you could have a personalized system doing all the grunt work. Sounds like a dream, right?

Of course, there are potential challenges, like avoiding algorithmic bias and ensuring true representation isn’t lost. But if done right, it could revolutionize the way voting is done in DAOs, making the process more manageable and engaging for members.

Pushing the Boundaries of DAO Governance

In the end, the goal is to make DAOs more efficient and democratic, and a ranking score system could be a step in the right direction. By infusing some AI & ML wisdom into DAO voting, we can alleviate voting apathy, making members' lives easier while maximizing the strengths of the DAO model.

This post was published by Vattan PS — a startup influencer and board member at Internet Native Organization.