The Internet Should Be More Like Wikipedia

Those core values of deliberation, stewardship, and common ownership and common good seem old-fashioned, but that retro-ness is worth reconsidering and re-centering as we think about what values we want from the platforms we invest our data, time and attention in.”

Still, it’s incorrect and counterproductive to characterize Wikipedia as a bastion of algorithm-free people power. In a 2019 op-ed, Katherine Maher cautioned against empty Web 1.0 nostalgia and underscored the important role that artificial intelligence plays in spotting potential vandalism and directing editors to pages that might need evaluation.

Wikipedia may not be actively throwing up roadblocks to participation, but it’s “also OK with not everyone being an editor — the way a Mark Zuckerberg or Jack Dorsey would like all seven billion people in the world to be users of their platforms

Anyone can suggest edits to a Wikipedia page, but the foundation isn’t incentivized to maximize participation — there are no shareholders closely tracking monthly active users or click-through rates. Various barriers of entry — the many-layered editing process, revision displays that can be confusing to newcomers — become, in practice, the opposite... See more

Robust editorial oversight.

Peer production over ad- and engagement maximization.

So how exactly has Wikipedia kept its good name while newer platforms have failed? And what transferable lessons does it offer?

Wikipedia operates on a central database, but its peer production model and edit transparency bears some similarities to decentralized networks. And this option feels decidedly less radical a step for the major social platforms than, say, exit to community.

Wikipedia has a built-in editorial lodestar that social media platforms lack. Whereas sites like Facebook have recently invested more heavily in moderation (with limited results), their sense of neutrality remains “passive” — compared to Wikipedia’s “active” sense of neutrality

No reliance on personalization or amplification algorithms.