The AWT- Black Digital Research Lab (BDRL-ATL) at Emory University is dedicated to advancing the study of Black history, philosophy, theology, culture, and art in Atlanta through digital humanities and cutting-edge technology. The Lab emphasizes the importance of integrating "thick data"—rich, contextual information gathered from archives, oral histories, focus groups, and interviews—into the development and design of AI and machine learning systems. By creating Black-centered datasets, developing harm taxonomies, and establishing use case schemas, the Lab seeks to inform the design of fairer and more accurate algorithms that better serve Black communities.

Comprehensive Digital Archive

• Establish and maintain a digital repository that captures the full spectrum of Black intellectual, cultural, theological, and artistic contributions in Atlanta. This includes critical texts, oral histories, philosophical writings, theological works, visual art, literature, music, and other cultural artifacts that reflect the depth and diversity of Black life in the city.

Philosophical, Theological, and Artistic Research

• Support interdisciplinary research that explores how Black philosophical, theological, and artistic thought has shaped social justice movements and continues to influence contemporary debates. The Lab is committed to digitizing and preserving key works in philosophy, theology, and art, ensuring that visual art, music, literature, and other forms of cultural expression are accessible for future scholarship and public engagement. The Lab fosters inquiry into ethical, political, metaphysical questions, and aesthetic contributions that have emerged from Atlanta’s Black communities.

Educational Empowerment and Training

• Develop and deliver training programs focused on teaching Black students from the Atlanta metro area and HBCUs how to code, engage in digital humanities, and apply AI and LLMs to research. These programs aim to equip students with the technical and critical thinking skills necessary for leadership in academia, technology, and public history.

Extracting Thick Data for Fair AI Design

• Collect "thick data" through extensive fieldwork, including archival research, interviews, focus groups, and ethnographic studies, to gather deep contextual information about the lived experiences of Black communities in Atlanta. This data provides the foundation for understanding the specific harms and challenges that may arise from AI and machine learning technologies.

Risk-Informed Design Pipeline

Develop a Risk-Informed Design Pipeline that guides the AI development process by identifying potential risks or issues, categorizing possible harms, and suggesting responses or mitigations at each stage of the design. This pipeline includes:

• Categories of Harms: Group different types of social harms that could result from biased AI systems, such as discrimination in hiring, misrepresentation in media, or unfair policing practices.

• Scenarios and Use Cases: Organize specific instances or scenarios where AI might be used, such as in education, law enforcement, or healthcare, and detail the potential impacts on Black communities.

• Harm Taxonomies and Use Case Schemas: Develop taxonomies that categorize harms and logical schemas that outline possible outcomes and responses to identified risks. These tools help inform the design of AI systems, ensuring they are sensitive to the needs and contexts of Black communities.

Black-Centered Datasets and Algorithmic Fairness

• Create and curate Black-centered datasets that reflect the lived experiences of Black communities. These datasets are used to train AI models, ensuring they are more accurate and capable of addressing social harms. The Lab works to quantify this data into actionable insights that can be applied in the development of more robust and fair models.

Dr. Tolbert's research intersects the philosophy of science, social & political philosophy, and theoretical AI, translating theory into actionable policy. He earned his Ph.D. in Philosophy and a Masters in Statistics at the University of Pennsylvania. Guided by prominent scholars like Michael Kearns, his co-advisor, and Aaron Roth on his dissertation committee. Additionally, Anita Allen, Quayshawn Spencer, Samuel Freeman, and Scott Weinstein provided valuable insights during his doctoral journey. Before Penn, Dr. Tolbert acquired master’s degrees from Virginia Tech in Biochemistry and Philosophy and a B.S. in Biology from the University of Mobile.