
<AssumedAudience>

Anyone who thinks they can't draw, but would like to learn

</AssumedAudience>

<Spacer size="small" />

<IntroParagraph>

I know plenty of intelligent and highly motivated folks who are dying to learn to draw. Some are so intent on their desire, they've gone out and bought themselves iPads. The other half are actively thinking about buying an iPad right now.

</IntroParagraph>

They message me a lot.

> Which iPad should I buy? What app do you use? What brush is that? What size should my canvas be?

These questions aren't wrong. In fact they're perfectly reasonable.

I'm vocal about the fact many of my illustrations are made on [an iPad](https://www.apple.com/ipad-pro/) in an app called [Procreate](https://procreate.art/). I have a full post about my drawing equipment and workflows over at [[What App is That?]]

How are you supposed to draw if you don't also have these fancy tools?

This is the tricky bit.

If you go out and buy yourself these same things, you'll have all the right **materials** to draw. But that's only the dressing on the drawing dish.

## What you have is **Material** without the **Medium** or the **Meat**

What on earth do I mean by _material_, _medium_, and _meat_?

<MediumMaterialsMeat sectionTitle='Materials'>

_Materials_ are the physical tools you draw with. Suffice to say, you
need some way to make visible marks on a surface. Having precise control over
how you make those marks is essential. Being able to erase is helpful. Having
access to all the colors of the rainbow is a indulgent bonus.

An [iPad](https://www.apple.com/ipad-pro/) and
[Procreate](https://procreate.art/) is one overtly expensive
option. [Prismacolor Premier](https://www.prismacolor.com/colored-pencils/premier-colored-pencils/premier-soft-core-colored-pencils/PCPremierSoftCoreColoredPencils)
pencils on [Canson Newsprint](https://en.canson.com/xl-series/xlr-newsprint) is a far more financially reasonable choice.
[Col-Erase](https://www.prismacolor.com/colored-pencils/premier-colored-pencils/premier-col-erase-colored-pencils/PCPremierCol-EraseColoredPencils)
pencils with [Koh-i-Noor Progressos](https://www.cultpens.com/i/q/KH24668/koh-i-noor-progresso-woodless-graphite-pencil)
in [Stillman & Birn Epsilon](https://stillmanandbirn.com/products_epsilon.php)
series sketchbooks are another.
[Staedler Fineliner](https://www.staedtler.com/uk/en/products/products-for-coloring/fineliners/pigment-liner-308-fineliner-m308/)
pens in a [Moleskine](https://gb.moleskine.com/sketchbook-black/p0437)
notebook work great. Or
[Copic Markers](https://www.copic-shop.co.uk/) in
[Cottonwood Arts](https://www.cottonwoodarts.com/) sketchbooks. Or
any 4B pencil in any bog-standard sketchbook. These are all good options. <Footnote idName={1}> If it's not obvious yet, this is a list of all my favourite drawing gear, which you can and should take as a shining endorsement of all those options. </Footnote>

Everyone obsesses over the materials when they want to begin drawing. Which exact pen? What brand of paper? These questions matter. A little. Your materials affect what you can and can't do. But they don't matter nearly as much as everyone thinks they do.

Pick any of the above suggestions I've linked to and move onto the next step as soon as possible. This is not a lifelong commitment. You can change materials later if you ask nicely. <Footnote idName={2}>Hot tip: it's easier to swap materials if you don't pay an arm, a leg, and a kidney for them up front</Footnote>

</MediumMaterialsMeat>

<MediumMaterialsMeat sectionTitle='Medium'>

When you set out to draw, you're working in the _Medium_ of **visual language**. Just like writing a non-fiction essay, giving a speech, or programming in Python, visual language is a form of communication. It has it's own syntax, style, and structure.

Composition. Value. Contrast. Light. Shadow. color. Form. Shape language. Linear perspective. 3D construction. Line weight. Emphasis. Pattern. Balance. Rhythm. These are the equivalent of characters, verbs, sentences, introductory paragraphs, and satirical post-modern novels. They're a means to an end – a way to get a message across the gap between your mind and someone else's.

Mastering the medium of visual language is the true struggle of drawing. Not picking which iPad app to draw in. The material is a convenient distraction when understanding the medium is an incomprehensible and daunting task.

You are right to be afraid of the medium. I'm still afraid of it. Learning all it's dimensions, elements, and variations is a lifelong challenge. It's an [infinite game](/infinite-games). There is no end goal. You just get to keep playing.

</MediumMaterialsMeat>

<MediumMaterialsMeat sectionTitle='Meat'>

The _Meat_ is **the whole point of your illustration**. What is your drawing _about?_ What are you _saying?_ Why does it _matter?_

As one of my favourite writing professors used to say, -39 points for not having a point.

If we're going to put in the effort to craft an entire illustration, it better be for something worthwhile. You need an idea, a concept, or a piece of information you want to move from your mind into the minds of other people. Without meat, you're not serving any brain food to your audience. Which is why paintings of carefully arranges vases, daffodils, and clementines are disastrously dull. <Footnote idName={3}>IMHO. You might be really into Baroque vase paintings. It's great craftsmanship, but they don't say much beyond **"Look, a vase! Also, I've figured out how to pedantically paint realistic subjects!"**
Which has now been said enough times, and you probably don't need to add to the chorus.</Footnote>

</MediumMaterialsMeat>

---

## Moving past materials

Worrying about the materials should take up - at most - 10% of your attention and concern. Focus 70% of your attention on learning the medium while you're just starting out.

You can keep 20% of your focus on the Meat. But I wouldn't worry too much about communicating original, profound ideas at first. Doing so _in addition to_ learning a whole new language is going to be overwhelming.

In the same way it's a bad idea to learn Spanish while simultaneously trying to write a philosophy dissertation in it.

Once you get more comfortable “speaking" in visual language, you'll be able to shift a much higher percentage of your attention onto the Meat. You'll get to focus on The Thing You Want to Say, and know enough visual language to say it well.

That's the ideal I'm currently striving for. To reach a point where the Material is irrelevant, the Medium is a baked into my subconscious, and I'm all about the Meat.

<Center>

<h1 style={{ fontWeight: 500, marginTop: 0.25, }}>2% Material, 18% Medium, 80% Meat</h1>

</Center>

That's the goal. Don't let the shiny iPad reflection blind you.

Get past picking the Material. Focus on the Medium. Aim for the Meat.


The failure of drawing materials without mediums and meat

Why You Own an iPad and Still Can't Draw


<AssumedAudience>
  People who have heard of GPT-3 / ChatGPT, and are vaguely following the
  advances in machine learning, large language models, and image generators.
  Also people who care about making the web a flourishing social and
  intellectual space.
</AssumedAudience>

<Spacer size="xs" />

<IntroParagraph>

The [[dark forest theory]] of the web points to the increasingly life-like but life-less state of being online.<Footnote idName={1}>[Dark Forest](https://onezero.medium.com/the-dark-forest-theory-of-the-internet-7dc3e68a7cb1) [Theory of](https://onezero.medium.com/the-dark-forest-theory-of-the-internet-7dc3e68a7cb1) [the Internet](https://onezero.medium.com/the-dark-forest-theory-of-the-internet-7dc3e68a7cb1) by Yancey Strickler</Footnote> Most open and publicly available spaces on the web are overrun with bots, advertisers, trolls, data scrapers, clickbait, keyword-stuffing “content creators,” and algorithmically manipulated junk.

</IntroParagraph>

It's like a dark forest that seems eerily devoid of human life – all the living creatures are hidden beneath the ground or up in trees. If they reveal themselves, they risk being attacked by automated predators.

Humans who want to engage in informal, unoptimised, personal interactions have to hide in closed spaces like invite-only Slack channels, Discord groups, email newsletters, small-scale blogs, and [[digital gardens]]. Or make themselves [illegible](https://www.ribbonfarm.com/2010/07/26/a-big-little-idea-called-legibility/) and algorithmically incoherent in public venues.

<ResponsiveImage
  width="1000px"
  alt="An illustration of layers of the web: the dark forest on top, followed by layers of cozyweb holes where people hide away from bots and ad scavengers"
  src="/images/posts/ai-dark-forest/cozyweb.png"
/>

That dark forest is about to <div style={{transform: "scaleX(1.85)", display: "inline-flex", margin: "0 2.2rem"}}>expand</div>. Large Language Models (LLMs) that can instantly generate coherent swaths of human-like text have just joined the party.

Over the last six months, we've seen a flood of LLM copywriting and content-generation products come out: [Jasper](https://www.jasper.ai/), [Moonbeam](https://www.gomoonbeam.com/), [Copy.ai](https://www.copy.ai/), and [Anyword](https://anyword.com/) are just a few. They're designed to pump out advertising copy, blog posts, emails, social media updates, and marketing pages. And they're _really_ good at it.<Footnote idName={2}>Primarily because [GPT-3](https://en.wikipedia.org/wiki/GPT-3) which powers many of these products was specifically trained on text from the web. It's intimately familiar with the style of language we use online.</Footnote>

These models became competent copywriters much faster than people expected – too fast for us to fully process the implications. Many people had their come-to-Jesus moment a few weeks ago when OpenAI released [ChatGPT](https://openai.com/blog/chatgpt/), a slightly more capable version of GPT-3 with an accessible chat-bot style interface. <Footnote idName={3}>They're calling it GPT-3.5. It's the same model with human [reinforcement learning](https://huggingface.co/blog/rlhf) layered on top.</Footnote> The [collective](https://twitter.com/elonmusk/status/1599128577068650498?s=20&t=MgaSdgsaF0uU1lITCVPghw) [shock](https://twitter.com/volodarik/status/1600854935515844610?s=20&t=MgaSdgsaF0uU1lITCVPghw) and [awe](https://twitter.com/yu_angela/status/1599808692085743616?s=20&t=MgaSdgsaF0uU1lITCVPghw) [reaction](https://twitter.com/levie/status/1599156293050433536?s=20&t=MgaSdgsaF0uU1lITCVPghw) made clear how few people had been tracking the progress of these models.

To complicate matters, language models are not the only mimicry machines gathering speed right now. Image generators like [Midjourney](https://www.midjourney.com/home/?callbackUrl=/app/), [DALL-E](https://openai.com/dall-e-2/), and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion) have been on a year-long sprint. In January they could barely render a low-resolution, disfigured human face. By the autumn they reliably produced images indistinguishable from the work of human photographers and illustrators.

<TwoColumn maxWidth="1100px">

<ResponsiveImage src="/images/posts/ai-dark-forest/mj8.png" alt="..." />

<ResponsiveImage src="/images/posts/ai-dark-forest/mj1.png" alt="..." />

<ResponsiveImage src="/images/posts/ai-dark-forest/mj7.png" alt="..." />

<ResponsiveImage src="/images/posts/ai-dark-forest/mj3.png" alt="..." />

<ResponsiveImage src="/images/posts/ai-dark-forest/mj9.png" alt="..." />

<ResponsiveImage src="/images/posts/ai-dark-forest/mj4.png" alt="..." />

</TwoColumn>

<Subtext>Images I generated with Midjourney's V4 model</Subtext>

## A Generated Web

There's a swirl of optimism around how these models will save us from a suite of boring busywork: writing formal emails, internal memos, technical documentation, marketing copy, product announcement, advertisements, cover letters, and even negotiating with medical [insurance companies](https://twitter.com/StuartBlitz/status/1602834224284897282).

But we'll also need to reckon with the trade-offs of making insta-paragraphs and 1-click cover images. These new models are poised to flood the web with generic, generated content.

You thought the first page of Google was bunk before? You haven't seen Google where SEO optimizer bros pump out billions of perfectly coherent but predictably dull informational articles for every longtail keyword combination under the sun.

Marketers, influencers, and growth hackers will set up [OpenAI → Zapier](https://zapier.com/apps/openai/integrations) pipelines that auto-publish a relentless and impossibly banal stream of LinkedIn #MotivationMonday posts, “engaging” tweet <span role="img">🧵</span> threads, Facebook outrage monologues, and corporate blog posts.

It goes beyond text too: [video essays on YouTube](https://twitter.com/SamRo/status/1605919856808714240?s=20&t=QB-eYoISuymlqbHFFDnrWw), TikTok clips, podcasts, slide decks, and Instagram stories can all be generated by [patchworking](https://runwayml.com/) together ML systems. And then [regurgitated](https://byautomata.io/) for each medium.

We're about to drown in a sea of pedestrian takes. An explosion of noise that will drown out any signal. Goodbye to finding original human insights or authentic connections under that pile of cruft.

Many people will say we already live in this reality. We've already become skilled at sifting through unhelpful piles of “optimised content” designed to gather clicks and advertising impressions.

4chan proposed [dead internet theory](https://www.theatlantic.com/technology/archive/2021/08/dead-internet-theory-wrong-but-feels-true/619937/) years ago: that most of the internet is “empty and devoid of people” and has been taken over by artificial intelligence. A milder version of this theory is simply that we're overrun [with bots](https://nymag.com/intelligencer/2018/12/how-much-of-the-internet-is-fake.html). Most of us take that for granted at this point.

But I think the sheer volume and scale of what's coming will be meaningfully different. And I think we're unprepared. Or at least, I am.

---

## Passing the Reverse Turing Test

Our new challenge as little snowflake humans will be to prove we aren't language models. It's the reverse [turing test](https://en.wikipedia.org/wiki/Turing_test).

<TweetEmbed tweetId="1601541418153304064" />

<TweetEmbed tweetId="1601750189974245377" />

After the forest expands, we will become deeply sceptical of one another's _realness_. Every time you find a new favourite blog or Twitter account or Tiktok personality online, you'll have to ask: Is this really a whole human with a rich and complex life like mine? Is there a _being_ on the other end of this web interface I can form a relationship with?<Footnote idName={4}>“Relationship” in the holistic sense – friend, acquaintance, pen pal, intellectual interlocutor, frenemy, drinking buddy, and sure, maybe a lover.</Footnote>

Before you continue, pause and consider: How would _you_ prove you're not a language model generating predictive text? What special human tricks can you do that a language model can't?

### 1. Triangulate objective reality

As language models become increasingly capable and impressive, we should remember they are, at their core, linguistic [prediction systems](https://www.datacamp.com/blog/a-beginners-guide-to-gpt-3#:~:text=Language%20modeling%20is,predicting%20word%20sequences.). They cannot (yet) reason like a human.

They do not have beliefs based on evidence, claims, and principles. They cannot consult external sources and run experiments against objective reality. They cannot go outside and touch grass.

In short, they do not have access to the same shared reality we do. They do not have embodied experiences, and cannot sense the world as we can sense it; they don't have vision, sound, taste, or touch. They cannot feel emotion or tightly hold a coherent set of values. They are not part of cultures, communities, or histories.

They are a language model in a box. If a historical event, fact, person, or concept wasn't part of their training data, they can't tell you about it. They don't know about events that happened after a certain cutoff date. <Footnote idName={5}>Currently 2021 for GPT-3 / ChatGPT, but we can expect that to regularly update as new models are trained</Footnote>

I found Murray Shanahan's paper on [Talking About Large Language Models](http://arxiv.org/abs/2212.03551) (2022) full of helpful reflections on this point:

<BlockquoteCitation author="Murray Shanahan" title="Talking About Large Language Models" url="http://arxiv.org/abs/2212.03551">

Humans are members of a community of language-users inhabiting a shared world, and this primal fact makes them essentially different to large language models. We can consult the world to settle our disagreements and update our beliefs. We can, so to speak, “triangulate” on objective reality.

</BlockquoteCitation>

This leaves us with some low-hanging fruit for humanness. We can tell richly detailed stories grounded in our specific contexts and cultures: place names, sensual descriptions, local knowledge, and, well the _je ne sais quoi_ of being alive. Language models can decently mimic this style of writing but most don't without extensive prompt engineering. They stick to generics. They hedge. They leave out details. They have trouble maintaining a coherent sense of self over thousands of words.

Hipsterism and recency bias will help us here. Referencing obscure concepts, friends who are real but not famous, niche interests, and recent events all make you plausibly more human.<Footnote idName={6}>This feels eerily like a hostage holding up yesterday's newspaper to prove they are actively in danger. Perhaps a premonition.</Footnote>

### 2. Be original, critical, and sophisticated

Easier said than done, but one of the best ways to prove you're not a predictive language model is to demonstrate critical and sophisticated thinking.

Language models spit out text that sounds like a B+ college essay. Coherent, seemingly comprehensive, but never truly insightful or original (at least for now).

In a repulsively evocative metaphor, they engage in “[human centipede](<https://en.wikipedia.org/wiki/The_Human_Centipede_(First_Sequence)>) epistemology.”<Footnote idName={7}>I found this phrase via Twitter, but posted from a private account so I won't cite the original author.</Footnote> Language models regurgitate text from across the web, which some humans read and recycle into "original creations," which then become fodder to train other language models, and around and around we go recycling generic ideas and arguments and tropes and ways of thinking.

Hard exiting out of this cycle requires coming up with unquestionably original thoughts and theories. It means seeing and synthesising patterns across a broad range of sources: books, blogs, cultural narratives served up by media outlets, conversations, podcasts, lived experiences, and market trends. We can observe and analyse a much fuller range of inputs than bots and generative models can.

It will raise the stakes for everyone. As both consumers of content and creators of it, we'll have to foster a greater sense of critical thinking and scepticism.

This all sounds a bit rough, but there's a lot of hope in this vision. In a world of automated intelligence, our goalposts for intelligence will shift. We'll raise our quality bar for what we expect from humans. When a machine can pump out a great literature review or summary of existing work, there's no value in a person doing it.

### 3. Develop creative language quirks, dialects, memes, and jargon

The linguist [Ferdinand de Saussure](https://en.wikipedia.org/wiki/Ferdinand_de_Saussure) argued there are two kinds of language:

- **La langue** is the formal concept of language. These are words we print in the dictionary, distribute via educational institutions, and reprimand one another for getting it wrong.
- **La parole** is the speech of everyday life. These are the informal, diverse, and creative speech acts we perform in conversations, social gatherings, and text to the group WhatsApp. This is where language evolves.

We have designed a system that automates a standardised way of writing. We have codified _la langue_ at a specific point in time.

What we have left to play with is _la parole_. No language model will be able to keep up with the pace of weird internet lingo and memes. I expect we'll lean into this. Using neologisms, jargon, euphemistic emoji, unusual phrases, ingroup dialects, and memes-of-the-moment will help signal your humanity.

Not unlike teenagers using language to subvert their elders, or oppressed communities developing dialects that allow them to safely communicate amongst themselves.

### 4. Consider institutional verification

This solution feels the least interesting. We're already hearing rumblings of how “verification” by centralised institutions or companies might help us distinguish between meat brains and metal brains.

The idea is something like this: you show up in person to register your online accounts or domains. You then get some kind of special badge or mark online legitimising you as a Real Human. It may or may not be on the blockchain somehow.

Google might look something like this:

<ResponsiveImage
  framed
  src="/images/posts/ai-dark-forest/human_google.jpg"
  width="1300px"
  alt="Google search results showing a 'certified human' badge on some items"
/>

The whole thing seems fraught with problems, susceptible to abuse, and ultimately impractical. Would it even be the web if everyone knew you were really a dog?

<ResponsiveImage
  src="/images/posts/ai-dark-forest/Internet_dog.jpeg"
  width="460px"
  alt="The original internet dog meme: 'On the internet, no one knows you're a dog'"
  showalt
/>

### 5. Show up in meatspace

The final edge we have over language models is that we can prove we're real humans by showing up IRL with our real human bodies. We can arrange to meet Twitter mutuals offline over coffee. We can organise meetups and events and conferences and unconferences and hangouts and pub nights.

In [Markets for Lemons and the Great Logging Off](https://www.fortressofdoors.com/ai-markets-for-lemons-and-the-great-logging-off/), Lars Doucet proposed several knock-on effects from this offline-first future. We might see increased [fetishisation](https://www.fortressofdoors.com/ai-markets-for-lemons-and-the-great-logging-off/#:~:text=a%20resurgence%20and%20even%20fetishization%20of%20explicitly%20%22offline%22%20culture%2C%20where%20the%20%22Great%20Logging%20Off%22%20becomes%20literal) of anti-screen culture, as well as [real estate](https://www.fortressofdoors.com/ai-markets-for-lemons-and-the-great-logging-off/#:~:text=Seventh%2C%20real,amenities%20are%20available) price increases in densely populated areas.

For the moment we can still check humanness over Zoom, but live video generation is getting good enough that I don't think that defence will last long.

There are, of course, many people who can't move to an offline-first life; people with physical disabilities. People who live in remote, rural places. People with limited time and caretaking responsibilities for the very young or the very old. They will have a harder time verifying their humanness online. I don't have any grand ideas to help solve this, but I hope we find better solutions than my paltry list.

As the forest grows darker, noisier, and less human, I expect to invest more time in in-person relationships and communities. And while I love meatspace, this still feels like a loss.

<References>
  <ReferencesLink
    title="Talking About Large Language Models"
    href="http://arxiv.org/abs/2212.03551"
    author="Murray Shanahan"
  />
  <ReferencesLink
    title="AI: Markets for Lemons, and the Great Logging Off"
    href="https://www.fortressofdoors.com/ai-markets-for-lemons-and-the-great-logging-off/"
    author="Lars Doucet"
  />
</References>

{/* ### 4. Be weird */}

{/* We're going to need to get weird. Post-post-post-modern-dadaist level weird. Being [illegible](https://www.ribbonfarm.com/2010/07/26/a-big-little-idea-called-legibility/) is a fairly effective defence mechanism against automated forces. */}

{/* Being so unpredictable and strange that your work only makes sense to a select few is a trade-off. But I bet it's one many online creators will take. */}


Proving you're a human on a web flooded with generative AI content

The Expanding Dark Forest and Generative AI


<BasicImage
  width="700px"
  src="https://res.cloudinary.com/dg3gyk0gu/image/upload/c_scale,w_700/v1580828081/maggieappleton.com/egghead-course-notes/advancedjs-notes/AdvancedJS_1_2x.png"
  alt="Advanced JavaScript Fundamentals"
/>

<IntroParagraph>

Being humans, we rarely learn things linearly. No one begins their JavaScript journey by reading [the TC39 language specification](https://www.ecma-international.org/ecma-262/5.1/) line-by-line like a pedantic masochist.

</IntroParagraph>

```js
let banana = "banana";
let apple = "apple";

console.log(banana);
console.log(apple);
```

Instead we all cobble together a good-enough understanding, leaving holes here and there to fill in later.

So far I've stumbled along without 100% grokking prototypical inheritance, or how `call, apply,` and `bind` work on functions.

It's fine. Stuff mostly works. Until it doesn't ¯\_(ツ)\_/¯

But at some point, it's useful to stop stumbling about and peek back at those holes.

<ImageLink href="https://egghead.io/courses/advanced-javascript-foundations">
  <ImageFrame
    alt="Course illustration of a building with a bright yellow foundation floor for advanced javascript foundations"
    src="https://og-image-egghead-course.now.sh/advanced-javascript-foundations"
  />
</ImageLink>

[Tyler Clark](https://twitter.com/iamtylerwclark)'s egghead course on [Advanced JavaScript Foundations](https://egghead.io/courses/advanced-javascript-foundations) that was exactly that hole-filling opportunity for me.

As usual, I drew some notes...

<BasicImage
  width="900px"
  src="https://res.cloudinary.com/dg3gyk0gu/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1580828081/maggieappleton.com/egghead-course-notes/advancedjs-notes/AdvancedJS_2_2x.png"
  alt="Advanced JavaScript foundations"
/>

<BasicImage
  width="900px"
  src="https://res.cloudinary.com/dg3gyk0gu/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1580828082/maggieappleton.com/egghead-course-notes/advancedjs-notes/AdvancedJS_3_2x.png"
  alt="Advanced JavaScript foundations"
/>

<BasicImage
  width="900px"
  src="https://res.cloudinary.com/dg3gyk0gu/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1580828081/maggieappleton.com/egghead-course-notes/advancedjs-notes/AdvancedJS_4_2x.png"
  alt="Advanced JavaScript foundations"
/>

<BasicImage
  width="900px"
  src="https://res.cloudinary.com/dg3gyk0gu/image/upload/c_scale,f_auto,q_auto:good,w_1200/v1580828081/maggieappleton.com/egghead-course-notes/advancedjs-notes/AdvancedJS_5.png"
  alt="Advanced JavaScript foundations"
/>

### Want more illustrated notes on web development?

Take a look at [[JavaScript's ...spread Operator]], [[Building Custom React Hooks]] or [[Fixing Common Git Mistakes]]


Illustrated notes on advanced but fundamental topics in JavaScript

JavaScript Bits You Skipped the First Time Around


<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

Epistemic Disclosure


<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

The Garden

A collection of essays, notes, and half-baked explorations I'm always tending to.

Why You Own an iPad and Still Can't Draw

Epistemic Disclosure

The Expanding Dark Forest and Generative AI

Algorithmic Transparency

JavaScript Bits You Skipped the First Time Around