  Despite rapid growth in the data science workforce, people of color, women,
those with disabilities, and others remain underrepresented in, underserved by,
and sometimes excluded from the field. This pattern prevents equal opportunity
for individuals, while also creating products and policies that perpetuate
inequality. Thus, for statistics and data science educators of the next
generation, accessibility and inclusion should be of utmost importance in our
programs and courses. In this paper, we discuss how we developed an
accessibility and inclusion framework, hence a structure for holding ourselves
accountable to these principles, for the writing of a statistics textbook. We
share our experiences in setting accessibility and inclusion goals, the tools
we used to achieve these goals, and recommendations for other educators. We
provide examples for instructors that can be implemented in their own courses.
