
<h2 className="micro">The Context</h2>

We are all familiar with the recommendation algorithm – the dark forces that shove fad-diet-shills, flat-earthers, and Jordan Peterson into our feeds.

They've become a modern myth. There are a thousand moral panic op-eds raising the alarms; algorithms are controlling us, manipulating us, radicalizing us, and preying on our insecurities and "secret desires." All just to sell us overengineered toothbrushes, vegan whey powder, and blow-up paddling pools, among other goodies.

While it's unclear whether algorithms are truly "harming the children"<Footnote idName={1}>And other such hand-wavy claims that fail to clearly define 'algorithms', 'harm', or identify which specific children we should be morally panicked about</Footnote>, there's still a few legitimate concerns in the ways we're allowed to interact with them in modern interfaces.

For a start, we aren't told what kinds of data go into our algorithmic systems, and have no visibility into why certain results come out. Most algorithm-driven interfaces blackbox their internal logic and decision-making rules. Faced with these opaque systems, we end up fetishising the algorithms – we attribute human agency and capacities to them, and in doing so hide the very real human agents designing and maintaining them. We call them "magic" to dismiss the need to make them legible to users.

As an audience we have no visibility into what metrics the engineers are optimising for. The engineers themselves don't always understand how the algorithms choose what to recommend. The lack of transparency makes it difficult to understand why we're shown certain content. It hides the fact some algorithms maximise for qualities like emotional outrage, shock value, and political extremism. We lack the agency to evaluate and change the algorithms serving us content.

<Spacer size="small" />

<h2 className="micro">The Pattern</h2>

When an automated system recommends a piece of content, it should include an [[Epistemic Disclosure]] message explaining **why** it suggested it, and what factors went into that decision.

Transparency tips integrated into user interfaces like “**Recomended because you liked Pride and Prejudice**" or “**Recommended because Mary Douglas read this**” help us see the chain of logic.

Transparency alone isn't enough though. Users should have control over the data that feeds algorithmic systems they're subjected to. We should be able to to remove whole input sources, as well as individual pieces of content.

Pinterest does a good job of telling you why you're seeing certain pins, as well as giving you controls to remove the pin and see fewer like it:

<BasicImage
  width="800px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:best,w_800/v1640730625/maggieappleton.com/patterns/algo_6_kai29c.jpg"
  alt="A screenshot of the Pinterest interface saying why a specific pin was recommended"
/>

They also have a "tune your home feed" settings page where you can remove specific pins, boards, or topics:

<ImageFrame
  width="1000px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:best,w_1000/v1640730625/maggieappleton.com/patterns/algo_4_pqaggw.jpg"
  alt="A screenshot of the Pinterest setting page where you can tune your home feed"
/>

Despite being a notorious algorithmic bad boy, YouTube also gives you the ability to mark videos in your feed as "not interested"<Footnote idName={1}>Although anecdotally, no matter how many times I say I don't want Jordan Peterson videos, they keep showing up</Footnote> or "don't recommend channel", then follows up asking _why_ you marked it.

<ImageFrame
  width="1100px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1100/v1640730625/maggieappleton.com/patterns/algo_1_r8i1y4.jpg"
  alt="The YouTube homescreen with the options menu expanded on a single video"
/>

<ImageFrame
  width="1100px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1100/v1640730625/maggieappleton.com/patterns/algo_2_tw5bws.jpg"
  alt="The YouTube homescreen after you select 'not interested' with a modal asking why you're not interested"
/>

They also offer a "remove this video" option on your watch history page:

<ImageFrame
  width="1100px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1100/v1640730625/maggieappleton.com/patterns/algo_3_yd68as.jpg"
  alt="The YouTube watch history page with the option to remove a video from it"
/>

Letting users remove single videos/posts/tweets/whathaveyous from their algorithmic soup is the bare minimum for any platform. We're a long way from meaningful transparency that makes internal algorithmic decisions clear and empowers users to design their own feeds.


Algorithms that make their reasoning visible

Algorithmic Transparency


<h2 className="micro">The Context</h2>

For most of the words we read online, we have a limited amount of context about the writer and how much effort they've put into the piece we're reading.

We might have a name and a tiny 32px profile image. We don't know how long it took them to write these words, how many hundreds of tabs they read through, or whether they spent years earning a graduate degree on the topic. We rarely have a rich backstory with a comprehensive educational and professional history at hand.

If we disagree with their points, we're more likely to be ungenerous Epistemic Peers who assume the worst: they've barfed this garbage up in a single, frenzied caffeine rush and haven't so much as opened Google Scholar to check for prior art.

That might all be true. Which is fine. As long as they _disclose_ the situation upfront. The only true source of disappointment – in both reading and life – is unmet expectations.

<Spacer size="small" />

<h2 className="micro">The Pattern</h2>

At the beginning of a piece of writing, the author writes a sentence or two about the **epistemic status** of the work. _Epistemology_ is the study of knowledge – how we know things, the methods we use to gain knowledge, and how we validate knowledge as "true.” So an _epistemic status_ usually covers how the author came to know what they know. This includes how much effort they put into researching it, how they validated the information, and how sure they are about their conclusions.

Scott Alexander of [Slate Star Codex](https://slatestarcodex.com/) was one of the earliest well-known bloggers to do this. Many of his posts include statuses that range from "total conjecture” to "pretty good, but I make no claim this is original.” Here are a few more choice examples:

<ImageFrame
  style={{ margin: "0 auto 1rem" }}
  width="900px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1641161435/maggieappleton.com/patterns/epistemic1_w0umsf.png"
  alt="A Scott Alexander post with the epistemic status more crackpottish than usual"
/>

<ImageFrame
  style={{ margin: "0 auto 1rem" }}
  width="900px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1641161787/maggieappleton.com/patterns/epistemic2_syifue.png"
  alt="A Scott Alexander post with the epistemic status very speculative"
/>

<ImageFrame
  style={{ margin: "0 auto 2rem" }}
  width="900px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1641161787/maggieappleton.com/patterns/epistemic3_meynmd.png"
  alt="A Scott Alexander post with the epistemic status very speculative and not necessarily endorsed"
/>

[Gwern](https://www.gwern.net/) is another blogger who adds rich metadata to each post. They have a numerical "certainty" rating on each post:

<ImageFrame
  style={{ marginTop: 0 }}
  width="900px"
  src="https://res.cloudinary.com/dxj9qr5gj/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1641198514/maggieappleton.com/patterns/gwernepistemic_1_jpo0dp.png"
  alt="A Gwern post on spaced repetition with a certainty rating of highly likely"
/>

The rationalist and Effective Altruist communities have been particularly enthusiastic about the pattern. In true Rationalist style, one person writes up a comprehensive proposal to [standardize epistemic statuses](https://www.reddit.com/r/LessWrong/comments/eesr4y/a_proposal_for_standardizing_epistemic_status/) on the r/LessWrong subreddit.

<References>
  <ReferencesLink
    title="Epistemic Effort on LessWrong"
    href="https://www.lesswrong.com/posts/oDy27zfRf8uAbJR6M/epistemic-effort"
    author="Raemon"
  />
  <ReferencesLink
    title="Epistemic Statuses are Lazy and that is a Good Thing"
    href="https://devonzuegel.com/post/epistemic-statuses-are-lazy-and-that-is-a-good-thing"
    author="Devon Zuegal"
  />
</References>


Providing clear metadata on the epistemic validity of content

Pattern Catalogue

A catalogue of design patterns gathered from my own observations and research.

Algorithmic Transparency

Epistemic Disclosure