This is a project website of "Yes: Affirmative Consent as a Theoretical Framework for Understanding and Imagining Social Platforms", a paper accepted to CHI 2021, a top-tier conference in Human-Computer Interaction. The paper won the Best Paper Honorable Mention 🏅, an award given to top 5% of the submitted papers. The link to the paper is here. You can also check out a 5-minute video of the work here. If you have any questions, please reach out to Jane Im. :)
This work could not have existed without and builds on Consentful Tech Project, which the incredible Una Lee founded.✨ Una, who is also a co-author of this work, introduced the term "consentful technology"—which inspired many people.
How can we design a social internet where people's consent boundaries are protected? Non-consensual interactions are pervasive in online spaces, such as online harassment and revenge porn. In this work, we use a theoretical framework of affirmative consent ("Yes means yes!") to understand such problematic phenomena and generate new design ideas to tackle them. This website highlights the 1) principles of affirmative consent and 2) the design insights generated from the principles for building consentful platforms.
Voluntary | Informed | Revertible | Specific | Unburdensome | |
---|---|---|---|---|---|
DM + group chat | Users are asked if they want to join when invited to group chat.
Periodic checks |
Platform visualizes topics discussed in group chat before a person decides to enter.
Topic inference |
Users can revert message read status to unread. | Different online status by group: would love to chat for friends; online, but busy for others.
Granular visibility Group-level policies |
Classify DMs from strangers using sender’s content and behavior.
Account summarization |
Profile | Users can control profile visibility by audience: only show selfies to friends & friends’ friends.
Granular visibility |
Platform shows how many people that viewed the profile are strangers.
Audience intel |
Users can query and delete, en masse, tags and comments from their profile related to account (e.g., ex-partner).
Efficient expressivity |
Some profile fields are only shown to accounts that have been friends for > t time.
Group-level policies |
Platform periodically reminds user how their profile looks to other people: “This is how your profile looks to Jake.”
Periodic checks |
Friend + follow | Users can accept a friend request but can isolate it, sending it to a separate queue. (e.g., if acceptance is coerced).
Request isolation |
Platform alerts if friend request comes from account with history of posting toxic content.
Account summarization |
Requests from people previously unfriended are sent to a queue. —ensuring revert.
Request isolation |
Assign people to “circles” at follow time with rules: no tags from this circle.
Social circles Group-level policies |
Periodic reviews of followers/friends with new risk scores (e.g. toxicity level).
Periodic checks Account summarization |
Post+ comment | *most platforms already support voluntary posting and commenting | Users receive reports of how many post viewers are strangers.
Audience intel |
Users can query and delete posts/comments at large scale.
Efficient expressivity |
Users can apply audience rules to hashtags: e.g, creator can restrict who can use it.
Group-level policies |
Users can rate limit comments per post.
Individual rate limit |
Feed | Feed asks what users want to see today (or this week).
Periodic checks |
Content feed makes algorithms visible and salient. | Users can bookmark feed settings to easily revert to prior settings. | Users can set different types of content feeds per social circle.
*similar to mastodon’s local timelines Group-level policies |
Users can annotate posts in feed, from which the system can learn what posts the person wants to see (or not see).
Annotation for system-learning |
Tag | By default, platform always asks user if they consent to being tagged when another user initiates tagging.
Periodic checks |
Platform provides high-level summary of audience, outside friends, that sees tagged post.
Audience intel |
If user unfriends, the system asks if they also want to delete tags of the person.
Efficient expressivity |
Users set tagging rules by content type: disallow tags in photos of people.
Topic inference |
Users can timebox tag frequency: Jake can only tag once a month.
Timeboxing |
Share + retweet | Users can limit how many hops shares are allowed to travel.
Sharing hops |
Users are notified if post is shared to a new network “neighborhood.”
Audience intel |
When user deactivates post’s sharing, or deletes the post, existing shares disappear.
*twitter partially implements this Cascading & normative revert |
Leveraging data of past interactions, users can decide who can share each post: Only people who I have messaged 5 times can share.
Social circles |
Platform alerts user if their post starts being shared rapidly by strangers.
Audience intel |