Finding Bias in Unexpected Places
It was after midnight. I was trying to finish a storyboard before going to bed and was looking for a picture of someone committing a crime. No problem, I thought. Except, it was.
I was looking for a neutral image because in a course about criminals, I didn't want to inadvertently attribute the negative characteristic of “criminal” to any particular group within my learner demographic. And then it happened. I caught myself tempted to “just pick something,“ and go to bed. Finger on the mouse, I stopped myself. What was I doing?!
Suddenly, doubting every image I'd previously used in completed segments of this 12-part video series, I took a step back. Was I accurately representing the learner demographic of this project? Were my own biases seeping through? Did I even know what my biases were? Why didn't I include pictures of older learners or do a better job of including people with disabilities? Was it really because I couldn't find good images, or was it me? Was I overthinking it? Was I just tired? I kept looking.
Finally, on page 15 of my image search, I saw someone wearing a balaclava and gloves. Phew, that was close!
Only, the next day, I was still thinking about it. Did I even come across any women during my search? Women commit crimes, too. Right?
What exactly is bias?
Bias is a problem for people across all industries. Many of us who work within learning industries and areas that focus on content creation already have a basic understanding of bias. There is no shortage of books, websites, and other sources that explain bias to us.
We know that bias varies based on the person and the environment, and that no single answer identifies each unique type of bias. Most of us agree that bias refers to a judgment or attitude that is influenced by our own personal considerations rather than objective facts. It's part of human nature to form snap judgments about others based on superficial characteristics, such as looks, gender, or level of education. We all do it.
Admittedly, my own biases are sometimes harder to spot than the biases I observe in the media and in other people. I've taken the classes, read the articles, and completed the training, but that was a long time ago, and this is not a one-and-done exercise of self-reflection. It is time for a refresher.
Learner-Centered Training and The Pygmalion Effect
What happens if the risk of bias during the learning experience design is not appropriately acknowledged and evaluated? Our decisions are so frequently influenced by our preconceptions that, as a result, we may make decisions that negatively affect our learners and the community we seek to serve. For example, we may create content that doesn’t reflect our intended audience, or we may make assumptions about the level of learner education or experience in a way that significantly reduces the effectiveness of our learning designs.
The Pygmalion Effect is a term that refers to the phenomenon in which people meet the expectations of their instructors. When it comes to instructional designers and eLearning developers, we are effectively those instructors, and we may inadvertently project our biased expectations onto our learners. I’m constantly asking myself, what message am I sending if a learner is not represented in the content I generate, or they are only negatively represented? Am I unintentionally decreasing a learner's receptivity to the content because my bias signals that the learning was not designed with them in mind?
On the other hand, can diversity and inclusivity for their own sake be equally harmful or distracting? We've all seen content that is overly calculated in its approach to combating bias, and it comes across as dishonest and insulting. 1, 2, 3, 1, 2, 3, 1, 2, 3. Ah, yes. Everyone is represented equally. Problem solved.
It is essential to interact with learners authentically and not merely be performative. For instance, representing all genders equally in a course whose primary objective is to help mothers mentally prepare for childbirth would be both distracting and unnecessary for the course's target audience. However, including women of varying financial, ethnic, and cultural backgrounds is not irrelevant to all target learners, even if it may be distracting to some.
Relevant visuals aimed at the learner demographic, pregnant women. Incorporating a variety of images facilitates successful learning by combating negative biases and fostering diversity and inclusiveness.
Strategies To Reduce Bias In The Design and Development Process
This month, I set aside time to conduct a self-audit. I developed a plan to be more intentional in my assessment of my own biases and become more proactive in the way I evaluate my design and development process. The goal: To ensure that my content is inclusive, disrupt harmful biases, and enhance learner outcomes. If you are interested in developing your own strategy, you can use my journey for inspiration.
Step 1: Conduct a self-audit here at Project Implicit. It’s been a few years since I’ve taken these tests, so it was definitely time to check again and see what has changed. There are quite a few tests, so I did them over several days.
Step 2: Accept that I am human and just as biased as everyone else.
Step 3: Educate myself by watching videos and reading articles specifically targeting areas of concern.
Step 4: Pay more attention to areas of concern when conducting a needs analysis and identifying learner demographics. Prioritize content that actively speaks to learner demographics, disrupts harmful biases, and promotes diversity and inclusion.
Step 5: Give myself more time to analyze content and establish methods for evaluating my choices based on the information gathered in steps 1 - 4.
My struggle with identifying and controlling my own biases is an ongoing process that requires time, motivation, and dedication. Realistically, sometimes I will do better than other times. However, what is important is that I have started my own journey, which hopefully inspires others to re-examine themselves and the learning environments that they create.
What are you doing to regularly monitor and control your own biases?
Edited to add a list of sites for more inclusive stock images. I will add to the list as I discover new sources.