Gender Bias+ for Confluence

Promote diversity and gender equality by identifying gender bias in your pages and blog posts



BYLINE Click {x} bias at the top of this page.

report Click Apps then Gender Bias+ in the top navbar (login required).


  1. Open

  2. Click the Try it free button for a free 30 day trial.

  3. Install to your Confluence instance.

Pricing: free up to 10 users or use the pricing calculator.

Note: an active payment method is required to install apps.

Getting Started

BYLINE When there is potential gender bias present click {x} bias at the top of any page.

report Click Apps then Gender Bias+ in the top navbar to see a report of all potential gender bias.


Click Next below to step through all of the features and configuration options...


Exceeds the highest of security and data privacy standards:

  • 100% Cloud Fortified!

  • Data stored only within your Confluence instance region using content properties.

  • No external database.

  • No token/secret storage.

  • Static app runs client-side.

  • Ongoing vulnerability testing.

data flow diagram

apps+ details

Transparent technical details of the secure data flows in/out of Apps+


Static build of the app.

Where running on Cloudflare.


Loads all the HTML, CSS and JS required to run the app.


By default the byline will only display to logged-in users. In the top navbar click Apps then Manage apps. Scroll down to Gender Bias+ for Confluence and click the Configure button. From there you can show/hide the byline or set specific visibility settings.

Yes the calculation is run automatically in the background when you visit, create or edit content.

  • Bias free: no gendered words found.

  • Neutral bias: total feminine words = total masculine words.

  • Some bias:

    • LESS than 10 total masculine/feminine words found.

    • or, difference between totals is LESS than 200%.

  • Strong bias:

    • one total is 0 while the other is GREATER than or equal to 10.

    • or, difference between totals is GREATER than 200%.

  1. The algorithm identifies words within your page or blog post from a list of 120+ gendered words.

  2. It then sums a total for both identified feminine words and masculine words.

  3. The raw score is the difference between these two word totals.

  4. And we also calculate a qualitative score: bias free, neutral bias, some bias, and strong bias.

No. The discovery of gendered words in content is not necessarily a bad thing. The goal of this app is to help you quickly identify if gender bias in your content leans strongly in any particular direction.

If content is tagged with “strong bias” you may want to read the content, identify the particular sentences and do some editing to bring the calculation score back into “some bias”.

Feminine Words: affectionate*, agree*, careful*, cheer*, child*, co-operat*, collab*, commit*, communal*, compassion*, connect*, conscientious*, considerate*, cooperat*, dedicated*, depend*, diligent*, emotiona*, empath*, enthusias*, feel*, flatterable*, gentle*, hardwork*, honest*, inclusive*, inter-dependen*, inter-persona*, inter-personal*, interdependen*, interpersona*, interpersonal*, kind*, kinship*, loyal*, meticulous*, modesty*, nag*, nurtur*, pleasant*, polite*, quiet*, respon*, sensitiv*, shar*, share*, sharin*, submissive*, support*, sympath*, tender*, thorough*, together*, trust*, understand*, warm*, whin*, yield*

Masculine Words: active*, adventurous*, aggress*, ambitio*, analy*, assert*, athlet*, autonom*, battle*, boast*, challeng*, champion*, compet*, confident*, courag*, decid*, decision*, decisive*, defend*, determin*, domina*, dominant*, driven*, fearless*, fight*, force*, greedy*, head-strong*, headstrong*, hierarch*, hostil*, impulsive*, independen*, individual*, intellect*, lead*, logic*, objective*, opinion*, outspoken*, persist*, principle*, reckless*, self-confiden*, self-relian*, self-sufficien*, selfconfiden*, selfrelian*, selfsufficien*, stubborn*, superior*, unreasonab*

  1. Gaucher, D., Friesen, J., & Kay, A. C. (2011). Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of Personality and Social Psychology, 101(1), 109-128. doi:10.1037/a0022530

  2. Schmader, T., Whitehead, J., & Wysocki, V. H. (2007). A Linguistic Comparison of Letters of Recommendation for Male and Female Chemistry and Biochemistry Job Applicants. Sex Roles, 57(7-8), 509-514. doi:10.1007/s11199-007-9291-4

  3. Trix, F., & Psenka, C. (2003). Exploring the Color of Glass: Letters of Recommendation for Female and Male Medical Faculty. Discourse & Society, 14(2), 191-220. doi:10.1177/0957926503014002277

  4. Isaac, C., Chertoff, J., Lee, B., & Carnes, M. (2011). Do Studentsʼ and Authorsʼ Genders Affect Evaluations? A Linguistic Analysis of Medical Student Performance Evaluations. Academic Medicine, 86(1), 59-66. doi:10.1097/acm.0b013e318200561d

  5. Dutt, K., Pfaff, D. L., Bernstein, A. F., Dillard, J. S., & Block, C. J. (2016). Gender differences in recommendation letters for postdoctoral fellowships in geoscience. Nature Geoscience, 9(11), 805-808. doi:10.1038/ngeo2819


Our team provide fast 24/7 support. We typically respond within minutes-hours and aim to resolve any problems in the same day.

✅ Any questions

✅ Technical support

✅ Feature requests


🕓 Mon-Sun 24hrs

🌏 Sydney, Australia


Similar Apps