Our Commitment to Diversity, Equity, Inclusion and Social Justice in Clinical and Translational Research

The CCTSI is committed to improving human health by accelerating scientific discoveries and their implementation and dissemination while building the research teams of the future. We strongly believe that teams of diverse people working together and capitalizing on their individuality foster progress and innovation -- and when we engage our communities as part of these teams, we advance our research in a way that improves health and increases health equity. We value principles of fairness, equity and social justice in relation to, and across, intersections of race, age, color, disability, faith, national origin, citizenship, sex, sexual orientation, ethnicity, gender identity and gender expression.

The CCTSI is fully committed to the ongoing examination of our organizational policies and practices to ensure that we are in alignment with these values and to work in partnership with our faculty, staff, trainees and community partners to create an inclusive culture of equity.

Specifically, we are investing in ongoing work as follows:

  • Proactively implementing anti-racist principles and practices
  • Evaluating our organizational policies and procedures regularly to ensure DEI in hiring, promotion and funding practice
  • Including community members in our executive leadership team
  • Convening a Diversity, Equity and Inclusion committee made up of core leadership and others
  • Monthly, mandatory training series on DEI issues for all staff
  • Revising marketing and recruitment strategies to reflect our value of DEI for all of our CCTSI workforce development programs
  • Reviewing and revising the selection process for all CCTSI workforce development programs, which includes training on holistic review, selections scoring/rating criteria and review/discussion of applicants
  • Integrating health equity and social determinants of health considerations into the planning, conduct and dissemination of research across the clinical and translational sciences spectrum
  • Detecting and mitigating algorithmic bias in artificial intelligence (AI) systems and machine learning