The research problem addressed in this study is that racial bias programmed into predictive algorithm recommendations negatively impacts students in historically underrepresented groups. The purpose of this qualitative descriptive study was to explore the perception of algorithm accuracy among data professionals in higher education and explore the potential application of a modified critical race theory framework to the design of predictive algorithms used in higher education to reduce instances of racial bias from negatively impacting students from historically underrepresented groups. Social justice theory guided this study and emphasized four principles: access, participation, equity, and human rights. Three research questions steered this study. RQ1 addressed how data professionals in higher education perceived the accuracy of predictive algorithm recommendations used at higher education institutions. RQ2 considered how institutions vet the accuracy of their recommendations to protect students in historically underrepresented groups. RQ3 examined the process higher education institutions use to address racially biased predictive algorithm recommendations to protect students in historically underrepresented groups. This study included eight higher education professionals who currently work or have recently worked with predictive algorithm recommendations. Data collection initially occurred through an online Qualtrics questionnaire, with six of these participants volunteering to participate in online one-on-one semi-structured interviews through Zoom. NVivo directed thematic analysis by identifying codes, categories, and themes. Themes centered on the prevalence of systemic and racial bias in algorithm inputs and outputs (recommendations). Four implications for practice include the need for social justice and data literacy training for stakeholders, increased student participation in an institution's data strategy, including adverse childhood experiences data, and incorporating critical race theory tenets into the predictive algorithm design process. Future research should center on data justice and predictive algorithm recommendations, integrating critical race theory tenets into predictive algorithm inputs and outputs, and interviewing college students in historically underrepresented groups on their perception of predictive algorithms used at higher education institutions. Increased research in these areas can support social justice and guide institutions in the ethical use of predictive algorithm recommendations. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]