📚 Quinn's Highlights

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A batch of the best highlights from what Quinn's read, .

The need for transparent and democratic decision-making: Human bullshit and algorithmic bullshit are two sides of the same coin Summary: Data and algorithms are not inherently bad, but they should be used in a transparent and democratic way that empowers everyone. Instead of arguing about whether computer or human decision-making is better, we should focus on accountable and transparent decision-making. This means avoiding human biases and stereotypes as well as naive machine learning without considering its real-world implications. Transcript: Speaker 1 So the point is that it's not that data and algorithms are bad it's that they need to be applied in a way which is transparent and which is democratic and which empowers all of us to carry On these debates rather than simply being tools which accurately or inaccurately are being used to buy the powerful to control the rest of us it's silly to argue about which is better You know computer decision making or human decision making that's really not the point I mean the point is we should have accountable transparent decision making instead of bs there's Human bs which comes in the form of stereotypes in ideology and there's algorithmic bs which comes in the form of naive machine learning without thinking enough about its applications

Glen Weyl & Cris Moore on Plurality, Governance, and Decentralized Society

COMPLEXITY: Physics of Life

The Self-Reinforcing Stigmatization of Public Spaces (Like Libraries) Summary: Public libraries are facing various physical problems due to under-investment. They are often the last option for people who lack access to basic services. Libraries are used as shelters for the homeless, warm places for those suffering from addiction, and even childcare centers. This over-reliance on libraries to solve societal issues has stigmatized these public spaces. The lack of investment in addressing core problems has turned libraries into spaces of last resort. This sends a message to affluent Americans that if they want a gathering place, they should build their own in the private sector. Transcript: Speaker 1 One of the problems we have now is most cities, suburbs, towns in America have public libraries there. There's neighborhood libraries. The building is there. The buildings are generally not updated. They need to have new HVACs. They need new bathrooms. They need new furniture, but a lot of new books. Stomachs still not accessible to people in wheelchairs. There's all kinds of problems with libraries, just physically because we've under-invested in them. Libraries, unfortunately, have become the place of last resort for everyone who falls through the safety net. If you wake up in the morning in the American city and you don't have a home, you're told to go to a library. If you wake up in the morning and you're suffering from an addiction problem, you need a warm place. They'll send you to a library. If you need to use a bathroom, you'll go to a library. If you don't have child care for your kid, you might send your kid to a library. If you're old and you're alone, you might go to the library. We've used the library to try to solve all these problems that deserve actual treatment. How many times have you talked to someone who said it's basically a homeless shelter? What's happened is we've stigmatized our public spaces because we've done so little to address core problems that we've turned them into spaces of last resort for people who need a Hand. As we do that, we send another message to affluent middle-class Americans, and that is if you want a gathering place, build your own in the private sector.

The Infrastructure of Community

How to Keep Time

The Danger of Incorrectly Mapping Between Scientific Measures and Truth Transcript: Speaker 1 And it's a problem when scientific culture tolerates too much ambiguity. There's always a caveat there, which is that at the early stage of theory development, sometimes you need ambiguity because you don't actually know really what you're talking about Yet. And so you need to allow for multiple interpretations to be possible until you can figure out what you mean. But a mature theory should be minimally ambiguous. This is at odds with things like metrics in terms of let's say how to evaluate something because people think, oh, well, it's scientific. Therefore, I want to use this to then therefore impose a value judge on something. It's better because it has a higher score on it. But that's not what science is actually able to do. Science can say, it has this score and it measures this thing because what it measures is this. If you say what it measures is this, and therefore it means this other thing, that's a problem because that's a false mapping. And it's not really about ambiguity versus precision. It's about, I think, the imprecision of the mapping between the measure and the term. So if you want to measure something like happiness or economic prosperity, you can say, well, we'll measure the genie coefficient, we'll measure GDP. But those are rigorous, clearly unambiguous measures. They have a meaning. This is what they are. This is how we measure them. We can compare things on this measure. And that's not problematic until you then say, and it is better to have a higher GDP full stop.

Paul Smaldino & C. Thi Nguyen on Problems With Value Metrics & Governance at Scale

COMPLEXITY: Physics of Life

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