Generally, my default practice in regression for nominal categorical variables, including race, is to use dummy coding, with the majority/plurality level as reference. Interpretation of the model coefficients using this scheme is straightforward.
Additionally, I typically view comparisons to the majority/plurality group as most relevant, and for this coding scheme those comparisons are simply evident in the estimated coefficients and it's straightforward to test only these comparisons. They are also the best-sampled pairwise comparisons (i.e., the tests with the most power for given effect size). For a sample in the US population, that usually means white race is the reference category.
Recently, a colleague of mine received some criticism for this approach, arguing that using "white" as a default category propagates bias that "white" is normal/typical, and that it's better to look at difference between each group to "all" as a reference, or perhaps to choose the measured maximum/minimum category as a reference (whichever is preferable with respect to the dependent variable).
I appreciate the sentiment behind this, but the interpretation seems flawed to me. For an outcome where disparities are expected due to racism or bias, a comparison of one race category to the mean across all categories (weighted or not) seems to dilute the size of any disparities that are present across more than one non-majority race. Planning contrasts only with the "best" point estimate could mean the comparison group is likely to be undersampled and introduces selection bias. Unfortunately, I wasn't present and was unable to follow up with the person raising an objection as to what specifically they are proposing.
Am I missing some alternative? I'd be interested in any supported proposals of best-practices for handling these types of variables. I understand the use of "race" as a variable is unfamiliar/unusual to many people outside the US and would prefer not to relitigate those issues here: from my perspective, perceived race is not useful as a biological variable, but is nonetheless important because it impacts how people are treated by others in society and therefore affects health and healthcare.
A colleague suggested the criticism may have been motivated by papers like https://journals.sagepub.com/doi/abs/10.1177/0081175020982632 that suggests use of mean contrasts or binary contrasts. That would help answer the alternative I'm missing, but I'm still a bit uncertain with these suggestions, as they still seem to bring other problems with interpretation.