Allison Chan
Designer, artist, and sometimes cook from the Pacific Northwest, based in Northern California. Devoted to nurturing slowness, deep flavor, and communal abundance in all areas of life. 
Select Clients & Collaborators
Ayo Akingbade
Anti-Eviction Mapping Project
Recidiviz
Sandspiel
Permanent AgricultureIDEO CoLab
 NASA JPL
 Seattle Children’s Hospital
Seattle Design Festival
Internet.org Google
Cooking
State Bird Provisions, San Francisco
Kamaya, Kamiyama, Japan
Chez Panisse, Berkeley
Joodooboo, Oakland
Ramen Shop, Oakland
Contact
hi@allisonchan.info
@llisonchan
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2022–
Anti-Eviction Mapping Project

Designing maps, websites, and publications with the Anti-Eviction Mapping Project, a critical cartography and data visualization collective documenting the contours of gentrification and housing dispossession across the US.
Following the foreclosure crisis, corporate consolidation of real estate in the Bay Area has given rise to sweeping evictions and displacement. Real estate investors increasingly conceal their property portfolios behind vast webs of shell companies, further widening the power and information gap between tenants and their landlords.

Evictorbook is a mapping tool that allows San Francisco and Oakland tenants and housing organizers to research properties, evictions, and the corporate networks of ownership behind them. In addition to helping communities curb evictions through multi-building campaigns, this tool is used by local coalitions, advocacy groups, and policy analysts to publish reports and advance tenant protections in the Bay.
2024
Cooked

Product and brand design for Cooked, an AI-assisted, end-to-end cooking platform designed to help the pros share their best recipes, and to help anyone become a pro. (Stealth, please don’t share!)
 Prep like a pro. Mise en place is the first step to nailing a recipe. Make any dish your own with easy substitutions. Find what’s local and in season.  See the right info at the right time. A focused workflow helps you time and nail every step. Even the most complex recipes become easy to follow.  Import recipes like magic. Creators can instantly translate their existing recipe content (videos, media, text) into a rich and intelligent cooking experience.
2022–2023
Permanent

Product and brand design for Permanent, a small company working to transition our community’s food systems towards short-chain, regenerative agriculture and steward regional food sovereignty.
2022
Sandspiel

Sandspiel is a virtual sandbox that lets people of all ages play, paint, and experiment with real-world elements—sand, water, fire, plants, wind, and more. Inspired by sandplay therapy, the natural world, and the early web, Sandspiel offers a gentle sanctuary for expression and enrichment. 

I re-designed Sandspiel's core drawing experience, developed a fun visual identity, and ideated systems for people to program their own cellular automata.
  
2019–2021
Even

Even (acquired in 2022) aimed to get 25 million US workers "out of the red" by 2025 by ending the paycheck-to-paycheck cycle and helping employers disburse cash assistance. We serviced over a million low-wage, precarious workers in key frontline sectors—healthcare, retail, food service, manufacturing, and more. 

I conducted diary studies, interviews, surveys, and more to better understand the needs of workers most impacted by economic precarity, and designed services to deliver emergency relief and help them pay down debt. I also supported brand work and stewarded company-wide antiracism education following the 2020 George Floyd protests.



 


2017
Privacy & Civil Liberties at Palantir

Designed systems to forground data privacy, minimize dragnet surveillance, and heighten accountability across healthcare, nonprofits, and other critical organizations. Productized key requirements and principles from the (then) newly published GDPR into Foundry, Palantir’s enterprise data analysis platform.

For black-box organizations like Palantir that frequently handle sensitive, personal information at scale, perhaps the most impactful principle of the GDPR is data minimization—put simply, stop using and collecting data you don’t actually need, for reasons you won’t specify.

To support this, we built a layer of oversight into the data pipeline that empowers users and regulators to better manage the flow of sensitive information in and out of their organization’s scope of work. Foundry flags when someone wants to upload or access data that might be high-risk, and prompts them to submit a clear, proportionate use case for review. Data Protection Officers (in-house regulators appointed by the GDPR) can filter out data before it ever enters, set a retention policy, and attach policy documentation to steward safe usage.
As sensitive data about people flows downstream, its audience and scope of use is likely to change. A National Health Service doctor needs to share patient data with a clinical study at a partnering university; or a Polaris analyst wants to publish an annual report on trafficking survivors in DC. How can we help organizations share meaningful data while protecting the identities of individuals it describes?

In today’s ever-expanding social graph, true anonymity is nearly impossible to achieve. Even if you scrub away name, age, race, gender, or other direct PII (personally identifiable information), surrounding data can still provide context clues and proxies for re-identification.

We introduced typeclasses to help index these connections by classifying properties of data at the column- or object-level. This granularity helps us make smart inferences about when and how data might be sensitive, and support de-identification methods like obfuscation, generalization, and pseudonymization without requiring SQL.
K-anonymity is a method of measuring the risk of re-identification in an anonymized dataset. For example, a dataset with k-3 anonymity is generic enough such that any combination of attributes appears at least 3 times—in other words, any record could correspond to at least 3 individuals. The higher the k-value, the harder it is to discern who in particular the data describes.

This method has become industry-standard for data protection, but existing tools are complex and opaque. Here, a non-technical user can easily test for k-anonymity, understand how identifiable their data is, and take action to minimize it.