Tech industry language and terminology is rapidly changing to avoid being biased. We think this is a good thing and want to support the evolution. 🙌🏽

We know many common terms and phrases are used without malice. However, some of them either originate from inherent bias, or have the power to perpetuate continued bias.

Increasing the inclusivity of our language means striving to understand the ways language often unconsciously makes assumptions about people and unintentionally reinforces dominant norms around gender, sexual orientation, race, class, ability and disability, age, and more.

So when writing we try to ask ourselves: is the use of this term or phrase central to the meaning of the work? Can I convey the intended meaning using more inclusive language?

There are lots of examples of things we write that can be written using more inclusive reading language.

So, as a general rule we’ll try to:

👉🏻 Avoid terms that use black to mean something bad or negative and white to mean positive or less harmful

• Examples: blacklist/whitelist; blackmail; white lie

👉🏽 Avoid terms that perpetuate negative stereotypes or unequal power relationships

• Examples: master/slave; smart/dumb; right/left (in this example, avoid using right to mean “good” or “normal,” and left to mean “bad” or “abnormal.”)

👉 Avoid terms that assign a gender to inanimate objects

• Examples: male/female connectors; male bolt/female nut

👉🏾 Avoid colloquialisms, metaphors, similes, idioms, and other unnecessary jargon. Plain language is always preferable.

👉🏾 Not identify gender unless necessary for comprehension, or when referring to a specific individual or group.

👉🏼 Use people-first language when describing a specific individual or group.

• Examples: persons experiencing homelessness; persons with a disability; adult who uses a wheelchair

👉 Pay attention to the order in which we present groups of people (including authors, contributors, social groups, agencies, etc.). The order can imply that the first mentioned dominates the later mentioned.

👉🏻 When referring to persons in the text (i.e., in the acknowledgments), we’ll try to ask them for their names, prefixes, and personal pronouns.