Writing about people

Here are some best practices to follow when writing about people. These apply to identity.

Best practices#

The goal of all UX content is to be understandable to all — not just to the people paying for something or for those in a certain industry — and to accommodate the many ways that people use products. When creating content for product experiences, think and write by centering the person you’re writing to or about in a way that’s compassionate, inclusive, and respectful. Work to grasp the perspective of underrepresented groups, and avoid writing in a way that may view or treat someone as intrinsically different from yourself. You can use methods like co-designing and UX research.

Keep the following best practices in mind when writing:

Use neutral, precise, relevant descriptions#

Only include personal qualities if they’re relevant and important. Write what you mean, then look back at what you wrote and think about whom you’re centering with your words. Doing this can reveal which people you’re leaving out. What’s the sentiment behind your words?

Choose words carefully and understand historical significance#

Be cautious of appropriating terms from marginalized communities. In this guide, we say “underrepresented groups.” You can also reference 3rd-party sources such as Wikipedia’s list of which words to use and which to avoid.

Be clear and avoid stereotypes#

Be on the lookout for proxy questions and statements, which appeal to generalizations and stereotypes. For example, saying, “just buy more storage” is a proxy statement on economic status, while “view additional storage options” doesn’t make those assumptions. Communicate from a place of equality, not condescension, and think about the worst-case interpretation of your words. Clear intent excludes fewer people and reduces bias.

Account for machine learning and AI#

When collecting user data in app or web experiences, first think about whether that information is actually needed, and then if it really is, communicate why. Allow for both common and custom responses, self-identification, multiple selections, and the option to opt out of responding. Artificial intelligence learns only from the information we provide to it, so our inherent biases can easily become included in training data. If content allows for variable and AI-provided information, consider the ways that may affect any copy.

Writing about disability#

Use neutral, precise, relevant descriptions#

Person-first language centers the person, not their qualities, by using those qualities as modifiers: “Design Adobe apps for people who use assistive technology.” But for identity-first language, which some communities and individuals prefer instead, language highlights the disability: “Design Adobe apps for deaf people.” No group unilaterally chooses one over the other, so when you’re writing about someone, ask them how they want to be identified. Avoid euphemisms like “differently abled,” which are regarded as condescending, and descriptors used as nouns, like “the disabled” or “the blind.” These tend to present a group of individuals as a monolith and suggests a lack of individual diversity within the group.

Disabled person or person with disabilitiesDifferently abled or the disabled
Blind person or person who is blindThe blind

Some phrases in common parlance that imply negativity are based on slurs against people with disabilities, such as “crazy” or “lame.” Never imply that a person is “suffering” from a disability or is a “victim” of a condition. Avoid appropriating terms from the disability community.

Ridiculous or unpredictableCrazy
Incompetent or badDumb or lame
Keyanna has autism.Keyanna is suffering from autism.
Placeholder variableDummy variable
Amir uses a wheelchair.Amir is confined to a wheelchair.

Be clear and avoid stereotypes#

With imagery and language, avoid implying that a person has to look a certain way, be a certain size, or have a certain cognitive ability to do something. Depict more types of people as typical.

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Avoid appropriating terms from underrepresented groups#

Be aware of how words that are often associated with physical and mental health are often used as metaphors to describe interactions and product functionality.

Unavailable or locked or turned off or deactivatedGrayed out
Coherence checkSanity check
Organize or organizedOCD

Account for machine learning and AI#

Enter metadata with caution. For example, don’t tag a photograph of a child with words like “crazy” or “weird.”

Key example showing how not to account for machine learning and AI. Incorrect usage, photograph with metadata tag field, label Tags. Inside tag field, tag label Crazy.

Writing about race and class#

Use neutral, precise, relevant descriptions#

Let’s say you’re writing a persona. When describing a person's country of origin or race, be as descriptive as possible as to not generalize any race or ethnicity. Race is only pertinent to biographical and announcement-related content that involves significant, groundbreaking, or historical events. For capitalization, Adobe follows AP Stylebook guidelines: capitalize nationalities, peoples, races (all except white), and tribes.

Choose words carefully and understand historical significance#

Adobe avoids using software terms such as “whitelist,” “blacklist,” “master,” and “slave.” Don’t use terms assigning value to racial characteristics, such as “dark pattern.” (Terms like "dark mode," "light theme," or "black screen" literally refer to color and brightness and don't assign good or bad values, so continue using them.)

Use this format to provide contextual clarity:
(Result in past participle form) (object)

Examples: Shared domains, approved people, targeted sites

For coding constructs:
Use this format to provide contextual clarity:
(Result in past participle form) (object)

For coding constructs:
LegacyGrandfather clause
Futile undertaking or a project destined to failDeath march
Primary or main or source (e.g., “main track”)Master” descriptors

Be clear and avoid stereotypes#

If you want to use a certain idiomatic or casual phrase, research its history before doing so. For example, imperfect spellings or pronunciations of words can imply pejorative associations with an accent. Be on the lookout for proxy questions, such as relating postal codes to ethnicity in rejecting job candidates, or making pricing or marketing decisions based on the average income of postal codes.

Use plain language#

Since English isn’t everyone’s first language, it’s best to write using clear, plain language — as well as avoid idioms and phrases that might be complicated for non-English speakers to understand. Plain language is more widely understood and, therefore, avoids alienating people. It especially avoids alienating people in ways that specifically belittle non-English speakers. For example, the conversational and casual phrases “long time no see” and “no can do” were originally used to belittle Native Americans.

Welcome backLong time no see
2-step processWax on, wax off
Sorry, something went wrongNo can do

Depict more types of people as typical#

We must focus on building successful experiences for all users. That means writing and designing in a way that depicts all skin types, names, and cultures as typical. We cannot keep centering white-skinned, Western cultures in our designs.

Dark brown or beige or tan or peach, etc.Skin or flesh or nude (referring to color swatch)
CriticsPeanut gallery
A broad range of name examples within a product experience (e.g., Ayesha, Ibrahim, Vignesh, Quynh)Only culturally white name examples within a product experience (e.g., John, Bill, Karen, Amy)
Key example of correct way to show more people as typical in a product. A coach marks shows a new feature, titled "Introducing coediting." There are 4 avatars showing a diverse group of coeditors: a Black woman, an Asian man, a white man, and an Indian woman. The featured name of one coeditor is Ayesha Wilson.
Key example of incorrect way to show people in a product. A coach marks shows a new feature, titled "Introducing coediting." There are 4 avatars showing non-diverse group of coeditors: 3 white men and 1 white woman. The featured name of one coeditor is John Smith.

Be cautious of appropriating terms from underrepresented groups#

Here’s a list of preferred words that are alternatives to common technology industry jargon.

Native to the operating system or built-in featureNative
MeetingPow wow or circle the wagons
Vision statement or strategic statement or value propositionZen statement or Zen garden
Role model or kindred spiritSpirit animal
Authority or expertGuru or ninja

Writing about gender and sexuality#

Be clear and avoid gendered language and stereotypes#

Rather than “he” or “she,” if you don’t know a person’s pronouns, make the phrase plural and use “they” instead. Use of “they” to describe one person is also accepted, although the syntax remains plural (e.g., “they are” = “that person is”). It’s also best to avoid using roles or stereotypes that have gendered roots (e.g., “businessman" or “waitress”).

Flight attendantStewardess
TheyHe/she or (S)he
A group of people or a group of womenGuys or girls or ladies

Choose words carefully and understand historical significance#

Use gender and sexuality descriptors as modifiers, not nouns (e.g., “transgender woman” rather than “a transgender,” “bisexual person” rather than “a bisexual”). A person’s pronouns are not opinion or preference, even if they may change over time (view Spectrum’s guidelines on pronouns). All of this helps us emphasize every person’s humanity, and keeps us from alienating people who aren’t cisgender and heterosexual.

Transgender people or trans peopleTransgendered people or transgenders or the transgendered or transexuals
Trans womenTrans-women
A transgender manA transman
Alejandra, a lesbian womanAlejandra is a lesbian
Jing, a non-binary personJing is a non-binary
Saadi is cisSaadi is CIS
What are your pronouns?What are your preferred pronouns?
Jamal’s pronouns are he/him/his.Jamal prefers he/him pronouns.
Wholehearted or impassionedHysterical

Be specific and kind#

Know the difference between sex (male/female) and gender (man/woman). When collecting personal data from users, consider if it is really necessary to ask for a person’s gender. Data collection and forms, while useful to product builders, can feel intrusive when asking about gender. When you really do need the information, allow for both common and custom responses, self-identification, multiple selections, and the option to opt out of responding. Avoid asking proxy questions, for example, asking for someone’s gender when the information that is actually needed is their bike size.

Prefer to self-describe and Prefer to not respondOther
A key example showing correct usage of being specific and kind when asking about gender in a form. A picker with label Gender, value text Woman. Picker menu options: Man, Woman, Prefer not to answer, Prefer to self-describe.
A key example showing incorrect usage of asking about gender in a form. A picker with label Gender, value text Female. Picker menu options: Male, Female, Other.

Account for machine learning and AI#

People globally identify with many genders and sexualities, so it's important to teach AI exactly that. It wasn’t until June 2018 that the World Health Organization (WHO) declassified being transgender as a mental illness, so even though humans have adjusted this perspective, machine learning and AI can still perpetuate these biases. Don’t use AI or machine learning to guess genders based on image recognition, text analysis, or anything else.