In the wake of the protests last summer, many organizations have published statements in support of organizations and movements like Black Lives Matter. These events have also prompted organizations to launch or accelerate initiatives for diversity, equity, accessibility, and inclusion (DEAI). This is happening at for-profit companies and many of the cultural institutions with which Knology works.

Meaningful measures of change count among the many ingredients required for these efforts to bear fruit. I purposely skipped the word "test" or "assess" in the preceding sentence because measurement goes beyond data for testing. Measurement decisions imply a theory of change: the structure of the problem and the levers of change.

When working with our partners, whether on DEAI or any other initiative or intervention, we start with questions such as:

• What things (object or actions or beliefs, etc) constitute this activity?
• Where do those things intersect with other things?
• Why should we care about those things and their intersections? Only then do we ask...
• How might we capture something revealing about those things and their intersections?

At present, many organizations are measuring implicit attitudes, stereotypes, and biases. In other words, they have decided that they are concerned with the consequences of relatively automatic behaviors prompted by mostly unconscious and often undesired intuitions. This focus is often justified by group-level findings: for example, in US counties where one finds a high level of implicit bias in teachers, one also finds unequal educational outcomes for people assigned to different racial and ethnic groups.

Awareness is often the goal of DEAI programs that focus on implicit attitudes. Implicit attitudes can be overridden with awareness because awareness provides people the opportunity to catch themselves before acting on those attitudes. However, this assumes that the person or the environment in which the person is acting desires or requires that they catch themselves.

Measures of implicit attitudes, as in the example above, tend to reflect the environment in which the person is operating and implicitly (or explicitly) learning those attitudes. In-group favoritism might be a human trait, but how one identifies or treats the out-group depends on cultural learning and environmental reinforcements. However, DEAI programs that focus on implicit attitudes rarely address the environmental forces with which the person who wants to change must contend. Personal awareness is an inadequate lever for change in an environment that resists change.

Knology advises its partners to directly address cultural learning and environmental reinforcements that perpetuate discrimination. For example, Knology recently worked with a partner on an educational project intended to help teachers and their students build compassion across racial and ethnic groupings. Together, we identified the teacher as the lever of change for this program: the teachers are the models from which the children learn how to act and how to respond to the actions of others. We asked teachers to answer difficult questions about the racialized experiences, beliefs, and actions of their students, their colleagues, and their communities. In that way, we could gauge the resistance teachers needed to overcome using the knowledge, tools, and strategies provided by the program’s curriculum. In that way, the teachers could gauge how to fulfill their behavioral modeling role. In that way, the curriculum and its measures worked in tandem to advance the programmatic goals.

This kind of environmental awareness goes beyond attitudes (whether implicit or explicit). It reveals the landscape of change, within which attitudes are only a small piece. It reveals how little of that landscape we have explored. It reveals the uncertainties and risks of moving through that landscape of change.

In a subsequent post, I will explore the vital role of uncertainty and risk for change, especially for changing how we see and treat other persons.

Relevant Readings

If you are interested in digging deeper into the ideas shared in this article, please see the suggested readings below:

Cesario, J. (2021). What Can Experimental Studies of Bias Tell Us About Real-World Group Disparities? Behavioral and Brain Sciences, 1–80. https://doi.org/10.1017/S0140525X21000017

Chin, M. J., Quinn, D. M., Dhaliwal, T. K., & Lovison, V. S. (2020). Bias in the Air: A Nationwide Exploration of Teachers’ Implicit Racial Attitudes, Aggregate Bias, and Student Outcomes. Educational Researcher, 49(8), 566–578. https://doi.org/10.3102/0013189X20937240

Dovidio, J. F., Kawakami, K., & Beach, K. R. (2008). Implicit and Explicit Attitudes: Examination of the Relationship between Measures of Intergroup Bias. In Blackwell Handbook of Social Psychology: Intergroup Processes (pp. 175–197). John Wiley & Sons, Ltd. https://doi.org/10.1002/9780470693421.ch9

Hinton, P. (2017). Implicit stereotypes and the predictive brain: Cognition and culture in “biased” person perception. Palgrave Communications, 3(1), 1–9. https://doi.org/10.1057/palcomms.2017.86

Kunda, Z., & Thagard, P. (1996). Forming impressions from stereotypes, traits, and behaviors: A parallel-constraint-satisfaction theory. Psychological Review, 103(2), 284–308. https://doi.org/10.1037/0033-295X.103.2.284

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