# Blogs by Category

## Film Study for Research

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Research ability, like most tasks, is a trainable skill. However, while PhD students and other researchers spend a lot of time doing research, we often don’t spend enough time training our research abilities in order to improve. For many researchers, aside from taking classes and reading papers, most of our training is implicit, through doing research and interacting with mentors (usually a single mentor–our PhD advisor or research manager). By analogy, we are like basketball players who somehow made it to the NBA, and are now hoping that simply playing basketball games will be enough to keep improving.

Drawing on this analogy, I want to talk about two habits that are ubiquitous among elite athletes, that have analogs in research that I feel are underutilized. Those who do pursue these habits as PhD students often improve quickly as researchers.

## How Much Do Recommender Systems Drive Polarization?

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Polarization caused by social media is seen by many as an important societal problem, which also overlaps with AI alignment (since social media recommendations come from ML algorithms). I have personally begun directing some of my research to recommender alignment, which has gotten me curious about the extent to which polarization is actually driven by social media. This blog post is the first in a series that summarizes my current take-aways. I’ll start (in this post) by looking at aggregate trends in polarization, then connect them with micro-level data on Facebook feeds in later posts.

I started out feeling that most polarization probably comes from social media. As I read more, my views have shifted: I think there’s pretty good evidence that other sources, including cable news, have historically driven a lot of polarization (see DellaVigna and Kaplan (2006) and Martin and Yurukoglu (2017)), and that we would be highly polarized even without social media. In addition, most readers of this post (and myself) are “extremely online”, and probably intuitively overestimate the impact of social media on a typical American. However, it is possible that social media has further accelerated polarization to an important degree, but the data are too noisy to provide strong evidence either way.

## Economic AI Safety

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There is a growing fear that algorithmic recommender systems, such as Facebook, Youtube, Netflix, and Amazon, are having negative effects on society, for instance by manipulating users into behaviors that they wouldn’t endorse (e.g. getting them addicted to feeds, leading them to form polarized opinions, recommending false but convincing content).

Some common responses to this fear are to advocate for privacy or to ask that users have greater agency over what they see. I argue below that neither of these will solve the problem, and that the problem may get far worse in the future. I then speculate on alternative solutions based on audits and “information marketplaces”, and discuss their limitations.

## Donations for 2019/2020

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Each year I aim to donate around 10% of my income. In 2019, I fell behind on this, probably due to the chaos of COVID-19 (but really this was just an embarassing logistical failure on my part). I’ve recently, finally, finished processing donations for 2019 and 2020. In this post I write about my decisions, in case they are useful to others; see also here for a past write-up from 2016.

## Individual Project Fund: Further Details

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In my post on where I plan to donate in 2016, I said that I would set aside $2000 for funding promising projects that I come across in the next year: The idea behind the project fund is … [to] give in a low-friction way on scales that are too small for organizations like Open Phil to think about. Moreover, it is likely good for me to develop a habit of evaluating projects I come across and thinking about whether they could benefit from additional money (either because they are funding constrained, or to incentivize an individual who is on the fence about carrying the project out). Finally, if this effort is successful, it is possible that other EAs will start to do this as well, which could magnify the overall impact. I think there is some danger that I will not be able to allocate the$2000 in the next year, in which case any leftover funds will go to next year’s donor lottery.

In this post I will give some further details about this fund. My primary goal is to give others an idea of what projects I am likely to consider funding, so that anyone who thinks they might be a good fit for this can get in contact with me. (I also expect many of the best opportunities to come from people that I meet in person but don’t necessarily read this blog, so I plan to actively look for projects throughout the year as well.)

I am looking to fund or incentivize projects that meet several of the criteria below: