madisen

the way in which TikTok enables creativity and community building by supporting the reuse of soundtracks, stitching, and dueting, leading to cultural phenomena founded on collaboration

they view the encyclopedia as a “consensus truth,” as one of them put it: It acts as a reality check in a society where facts are increasingly contested. That truth is less about data points — “How old is Joe Biden?” — than about complex events like the Covid-19 pandemic, in which facts are constantly evolving, frequently distorted and furiously... See more

The key is that knowledge is a human endeavor, and the development of Wikipedia and its sister projects depends on human collaboration and consensus. Machine learning tools can augment the process, but AI cannot replace volunteer Wikimedians.

“Home is where we know and where we are known, where we love and are beloved. Home is mastery, voice, relationship, and sanctuary: part freedom, part flourishing ... part refuge, part prospect.” “We can choose its form and location but not its meaning.” (Zuboff 2019).

dealing with bad actors and reducing harm is not the same as in -centivizing good actors to establish best practices and good dataset governance.

How do you solve that by collecting everything? Because even if you’re collecting it and it’s right there, they don’t want to believe it.

Some may. All we can do is to collect it and make it easily available. What’s exciting to me is that not only can we now see that there are these separate worlds, but also we can see across universes. We can see... See more

Open systems – free speech, democracy, open content, open science – will sometimes produce results we do not like. That openness, of course, will be used by the powerful as well as the powerless. There will be cruel speech, bad democratic decisions and uses of openly licensed content with which we disagree. Thus, it is very easy to blame openness.... See more

The three principles that drive the Foundation’s deployment of machine learning solutions in Wikimedia projects are sustainability, equity, and transparency.

The key premise of algorithms is efficiency. Algorithms optimise data for decision-making that we previously did not have before. But humans are not efficient. We are messy and inherently flawed, which makes us human. We value serendipity, community and experiences, which may not be efficient but is impactful.