The Rise of Web3 Crowdsourcing
beyond “X to Earn” which issues its own project token and distributes it as a Ponzi scheme, let’s think about crowdsourcing as peer production.
Crowdsourcing, the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, has taken a new and exciting turn in the realm of Web3. As the crypto bubble post-Web3 has expanded, crowdsourcing’s role in this ecosystem is becoming more pronounced.
What is Crowdsourcing?
In 2005, Wired Magazine’s editors Jeff Howe and Mark Robinson coined the term “crowdsourcing.” Simply put, it is the act of a company or institution outsourcing functions, once performed by employees, to a large, usually extensive network of people, through a public call. Daren C. Brabham later defined it as an online distributed problem-solving and production model, sometimes known as “commons-based peer production.”
Crowdsourcing has already been implemented on a variety of platforms. some of the project’s structure is powered by distributed computing, or input from humans as the wisdom of the crowd. Examples include:
- Discovering extraterrestrial life by analyzing space data together (SETI@home)
- Contribution to pharmaceuticals by analyzing protein structures (Folding@home)
- OCR and bot-resistance(reCAPTCHA)
- Mapping using everyone’s smartphones (Mapillary, Hivemapper)
- Content moderation (Community Notes)
Credible Neutrality
Some crowdsourcing projects involve crowdsourcers seeking some benefit.
At this point, when building a mechanism to receive input from each crowdsourcer and determine the final outcome, it is very important that the mechanism be neutral enough to be trusted. With cryptocurrency as an Internet-native currency, contribution and reward distribution could be designed more seamlessly.
First, what is a mechanism? essentially, a mechanism is an algorithm plus incentives. A mechanism is a tool that takes in inputs from multiple people and uses these inputs as a way to determine things about its participants’ values, so as to make some kind of decision that people care about. In a well-functioning mechanism, the decision made by the mechanism is both efficient — in the sense that the decision is the best possible outcome given the participants’ preferences, and incentive-compatible, meaning that people have the incentive to participate “honestly”.
We are entering a hyper-networked, hyper-intermediated and rapidly evolving information age, in which centralized institutions are losing public trust and people are searching for alternatives. As such, different forms of mechanisms — as a way of intelligently aggregating the wisdom of the crowds (and sifting it apart from the also ever-present non-wisdom of the crowds) — are likely to only grow more and more relevant to how we interact.
Building credibly neutral mechanisms
There are four primary rules to building a credibly neutral mechanism:
- Don’t write specific people or specific outcomes into the mechanism
- Open-source and publicly verifiable execution
- Keep it simple
- Don’t change it too often
Credibly neutral mechanisms for solving many kinds of problems do exist in theory and need to be developed and improved in practice. Examples include:
- Prediction markets, eg. electionbettingodds.com as a “credibly neutral” source of probabilities of who will win near-future elections (see also Scott Alexander on this topic)
- Quadratic voting and funding as a way of coming to an agreement on matters of governance and public goods
- Harberger taxes as a more efficient alternative to pure property rights for allocating non-fungible and illiquid assets (eg. see thread: capped Harberger taxes for domain names)
- Peer prediction, a much more formalized version of the “meta-moderation” mentioned above
- Reputation systems involving transitive trust graphs
Web3 Crowdsourcing: The Next Step
With the advent of Web3, crowdsourcing takes a unique turn. Web3 emphasizes the principles of decentralization, transparency, and peer-to-peer interactions. Within Web3, we can find novel concepts such as:
- Staking: A mechanism to incentivize participation.
- Play to Earn: An evolution toward an autonomous world.
- DAOs and Proofs of Humanity: These can also be considered forms of crowdsourcing.
How Does Web3 Change Crowdsourcing?
Web3 introduces mechanisms like decentralized trust and transparent transactions. Projects like DeCartography use crowdsourcing as a social graph oracle, leveraging the power of decentralized communities. Issues around integrating AI into human-based crowdsourcing tasks and the optimization of quality also emerge as vital considerations.
A list of projects powered by Web3 crowdsourcing
There are several platforms embracing this new concept, such as:
- Hedge Funds in Machine Learning Model Competition (numer.ai)
- Distributed Courts (kleros)
- 1Person, 1ID(Proof of Humanity)
- Code auditing (code4rena)
- TCR, content curation (DBDAO, relevant)
Future Use Cases
Potential use cases of Web3 crowdsourcing include:
- Citizen Science: Projects like DBDAO offer token-based incentives for data DAOs.
- Human-Based Computation: Innovative approaches to problem-solving.
- Prediction Markets and Content Curation DAOs: Such as TCRs and platforms like relevant.
Risk
The common issue among these is the question of how to evaluate the crowd workers. Of course, they have an incentive to maximize their personal profit, so they may try to set up Keynesian beauty pageants, Sybil attacks, collusion, vote buying, etc. This is why they are trying to require Staking, and tying to DID with the goal of 1Person, 1ID, etc.
In addition, if Grand Truth does not exist, the evaluation may be based on the information entered by each cloud worker. (Peer Prediction Method, Peer Selection Mechanism, etc.) I feel that this area is quite similar to the discussion of the evaluation system for distributed Oracle validators.
One of the issues is whether or not the mechanism will work even if a bot like this emerges, which just keeps raising the applicability function in this direction.
Conclusion
The integration of crowdsourcing into the decentralized Web3 space signifies a remarkable advancement in collective collaboration and production. From crowdsourced map creation to AI-driven competitions, the potential for decentralized, transparent, and incentivized collaboration is enormous.
As Howe and Robinson envisioned crowdsourcing as a bridge between the crowd’s wisdom and corporate outsourcing, the Web3 version of crowdsourcing adds layers of decentralization, trust, and technology, paving the way toward a more democratized future.
With the rise of projects like numerai, kleros, Proof of Humanity, and others, the challenges and potentials of Web3 crowdsourcing become even more pertinent. Whether it’s enhancing AI capabilities or creating a decentralized oracle, the evolution of crowdsourcing within the Web3 space is something to closely watch, as it might indeed be the endgame for a labor-free world.
