Folk Theorization, Quickly

I have developed the habit of dropping folk theorization concepts into casual conversation inside and outside of academia. I believe knowledge of folk theorization is a useful reflective and adaptive tool for the everyday user of AI-driven systems, and a useful research tool for many AI-focused HCI researchers. If you’re here, we’ve probably been chatting, or we should – here’s a brief guide to what I was talking about, or what we can talk more about in the future.

Folk Theory

intuitive, informal theories that individuals develop to explain the outcomes, effects, or consequences of technological systems, which guide reactions to and behavior towards said systems

“Algorithms ruin everything”: #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media

We naturally form folk theories throughout our lives when we need to explain something, but don’t exactly have the knowledge we need to do so. This includes when we’re babies – your first folk theory was most likely something like “let go of ball, ball go down,” which you eventually learned to call “gravity.” When it comes to technology, folk theories are essentially how everyday users understand complex systems when direct technical knowledge isn’t available or is too complex to use for decision making. They’re informal (not based on direct, formal knowledge of the system), socially-constructed (you usually take what you’ve read, seen, and the ideas of others into account), and highly malleable (they can easily change if new evidence appears). They’re also very individualized – your folk theories are at least partially a product of who you are, what your goals are, etc, and they tend to be constructed in ways that will help you reach your own goals. In very complex systems with multiple moving parts, multiple folk theories often co-exist and play off of each other. There’s no such thing as a “good” or “bad” folk theory – the only way to judge a folk theory is on if it is useful to the user and helps them accomplish their goals.

Folk theories are usually used in place of direct knowledge, and they can be deployed in all sorts of processes. For example, in the diagram below, you can see how the folk theory sits between the user and a curation algorithm during self-presentation, acting to translate between the two. Consider how different a user’s outcomes might be in this process with two different folk theories of what a social media platform will boost to its main feed: one that centers careful hashtag use, and one that centers hyperbolic language and the rapid-fire interaction typical of online fights.

From How People Form Folk Theories of Social Media Feeds and What it Means for How We Study Self-Presentation

Folk Theorization

an inferential and ongoing sensemaking process in which users develop their folk theories

How People Form Folk Theories of Social Media Feeds and What it Means for How We Study Self-Presentation

Folk theorization is the process by which we form, test, and update our folk theories. It’s best to look at it as a loop in which we continually make sense of changing systems by taking in new information, adjusting our theories, and seeing how our newly-adjusted theories perform when we try to use them. One such loop is represented above, showing you how folk theorization works during self-presentation on social media platforms. Note that we take in both endogenous information (e.g., looking at the platform ourselves and running tests on it) and exogenous information (e.g., articles about the platform, anecdotes from friends), which we interpret through the lens of our own goals, theorization complexity level, and individual characteristics, which then gets integrated into an adaptive sense making process, which results in an updated folk theory, which motivates behavior, which produces feedback that starts another iterative folk theorization loop.

Folk theorization happens constantly – you might not be aware of it all the time, though you’ll probably be aware of any major changes to your theory, because they’re always responses to something you’ve observed or some information you’ve encountered. Think back to the last time you changed how you post to a social media platform – that was you engaging in folk theorization, most likely because you observed that the old way of doing things just didn’t work anymore.

Theorization Complexity Level

the level of system complexity a user is aware of, takes into account, and employs to pursue their own goals when folk theorizing 

Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

Your theorization complexity level (TCL) is the type of thinking you’re doing about a system while you folk theorize. Any level of theorization complexity can result in folk theories. Lower-level theorization tends to focus on what a system is doing, while higher level theories tend to focus on how a system is doing something. Higher levels of complexity tend to open up more options for a user.

Functional Theorization

Functional folk theorists have folk theories that reflect that they are focused on the presence and effects of algorithmically-driven systems, as opposed to the causes, or inner workings of the system. 

Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

Functional folk theorization is folk theorization which focuses on function, or what a technology does. If you’re a functional theorizer looking at the Facebook News Feed, your folk theory might be that you’re not seeing all possible content on the feed because the platform is somehow picking and choosing what to show you. This doesn’t leave the user a ton of room to maneuver, since there really isn’t much you can do with a functional theory – they tell you how things probably are, not what to do about them. Functional theorizers tend not to do that much experimentation to figure things out. When things change on a platform, functional theorizers try to adapt, but often have a hard time.

Structural Theorization

Structural theorists dive into the causes behind the algorithmic/computational effects they encounter. These users often confidently assert effects – in the present study, structural theorists universally took algorithmic curation as given and focused on the “how” of the curation. 

Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

Structural folk theorization is folk theorization which focuses on structure, or how a technology actually does what the user thinks it is doing. If you’re a structural theorizer looking at the Facebook News Feed, your folk theory might be that the platform decides what to show in your feed based on a combination of engagement with the post by people like you, closeness of your relationship with the person who made the post, and time of day the post was created. This highlights a lot of potential areas to try things out in one’s own posts – you could try to post at a different time to get a different audience, or try to build closer online relationships with folks who you always want to see in your feed. Essentially, structural theorization provides the user with direction on what they can do about the system – how to work within it to achieve their goals, or sometimes to counter it. Structural folk theorists tend to draw from many sources as they try to figure platforms out, and do a lot of testing, sometimes even enlisting friends to try out complex ideas and see how the system reacts. When systems change and structural theorists have to adapt, they can usually find a way – though they might not act on it if they’re no fan of the platform in question’s spirit.

Platform Spirit

the user’s perception of what a platform is and what it is for, as determined by the user’s understanding of the platform’s stated mission, its values and actions in practice over time, and the functionality which it allows as juxtaposed with the user’s understanding of the platform’s purpose

Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

Platform spirit can be boiled down to how the user feels about the platform, based on their assessment of the platform’s behavior (especially in comparison to any promises the platform has made) and the ability of the platform to help them in achieving their goals. Importantly, this isn’t just at one point in time – a user’s entire history with a platform factors into how they perceive its platform spirit. If a platform says they’re about creating community, a user wants to create community, and the platform provides great tools and tons of support for creating community, the user will likely see the platform as having a very positive spirit. If a platform says they’re about creating community and users are excited to create that community, and then the platform provides confusing tools and bans marginalized communities without warning, the user will likely see the platform as having a very negative spirit.

Platform spirit isn’t the only factor in how people perceive and react to platforms, but it is an important one. When it comes to folk theorization, even experienced structural theorizers may simply decide re-theorizing and adapting are not worth their time if a platform’s spirit is judged to be wanting. Bad enough platform spirit could even cause people to stop using a platform entirely.

Algorithmic Literacy

the capacity and opportunity to be aware of both the presence and impact of algorithmically-driven systems on self-or collaboratively-identified goals, and the capacity and opportunity to crystalize this understanding into a strategic use of these systems to accomplish said goals

Adaptive Folk Theorization as a Path to Algorithmic Literacy on Changing Platforms

Being algorithmically literate is essentially having the knowledge and skill you need to not just survive, but thrive and accomplish your goals in a platform environment which is driven AI-based mechanisms. It doesn’t mean being able to code or build a computer, though those skills can be important – rather, it means being able to interpret algorithmic systems on one’s own well enough to make reasonable, self-sustaining decisions.

Algorithmic literacy is one of the most important kinds of literacy to focus on in our education system, as AI-driven systems are only getting more commonplace. Eventually, being literate in how these systems work will be as crucial a life skill as knowing how to access the internet today.

My work strongly suggests that an important step towards algorithmic literacy is helping people get to a structural level of folk theorization. The knowledge of mechanism and the options that knowledge create directly enable algorithmic literacy.