Images are a powerful way to communicate a message. They can cause joy, inspire people to take action, and teach new concepts. But images can also cause distress, demotivate people, and perpetuate stereotypes. The ACS Inclusivity Style Guide—an American Chemical Society guide on inclusive language and images—aims to help people harness the power of images to send messages that reflect the diversity of our world and promote inclusion.

Inclusive images

The guide encourages varying the characteristics of people in images. Characteristics to consider include age, body size and shape, disability status, skin tone, gender expression, and hair style and texture. For example, an image accompanying a C&EN article about international students shows a wide range of people, which helps convey the diversity of students in the US educational system. But often, a single image can’t show the full breadth of human experience. So the guide recommends being thoughtful about who you choose to represent and avoiding showing the same combinations of characteristics across all your images.

Inclusive data visualizations

Related to images are data visualizations, such as graphs and charts. Many people consider data to be neutral—they’re just facts, after all. But as anyone who has adjusted the axis of a graph knows, the way data are presented can greatly skew the facts and create misconceptions. Each decision related to showing data affects the ultimate message that an audience gets. Which type of visualization will you use? What colors will you choose, and do those colors have connotations? What groups will you include? What will you call them, and how will you order them? What accompanying text is necessary to contextualize the visualization? How did you make the visualization accessible?

The ACS Inclusivity Style Guide aims to help take the guesswork out of inclusive data visualizations by providing guidance on these questions and more. A key piece of advice is to disaggregate data when possible so that more people are visible in the data. Aggregating groups—such as combining all Indigenous groups and multiracial people into an “Other” category—can contribute to their erasure in the data displayed. The guide also encourages communicators to ensure their data visualizations are accessible so that people with disabilities can access the information.

The top tips on making images and data visualizations inclusive are compiled in two tip sheets, shown here and at www.acs.org/inclusivityguide, where you can also find the full ACS Inclusivity Style Guide. We’re sharing all the guide’s tip sheets in a six-part series in C&EN. I invite you to share feedback on the guide and tip sheets at ISG@acs.org.