One view of the aim of science is that it is a means to transform our existing social organization into one more compatible with the demands of justice, liberty, or human flourishing. This picture of scientific rationality arises from the tradition of African American sociologists who wielded the statistical techniques of their time in the service of social justice. Born into chattel slavery, Wells's data-driven journalism challenged the rationalization for acts of terror against Blacks. Du Bois, in particular, held that science should aim to change the nature and character of society. For Du Bois, scientific inquiry had to explicitly include universal social goals of progress and reform. He thought democratic social reform was the final (or “mediate”) aim of science: 

 “The object of these studies is primarily scientific – a careful search for truth conducted as thoroughly, broadly, and honestly as the material resources and mental equipment at command will allow; but this is not our sole object; we wish not only to make the Truth clear but to present it in such shape as will encourage and help social reform” (Du Bois, Dusk to Dawn, 1940). 
 
Wells and Du Bois were pioneers in socially conscious and ethical statistical analysis in the service of social good. They were "proto"-socially aware data scientists.
 
It is in this spirit Alexander Williams Tolbert has founded The Inaugural W. E. B. Du Bois- Ida B. Wells Symposium on Socially Aware Data Science and Ethical Issues in Modeling to bring together scholars and social activists working on issues that intersect and cross-cut data science, social science, ethics, philosophy, technology, and law.
 
This conference will be held by Penn Philosophy in Claudia Cohen 402 on Tuesday, June 7th. Please register here
 
 
Speakers: 
 
1. Shahin Jabbari-“The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective”
2. Anita Allen-Dismantling the “Black Opticon”: Privacy, Race Equity, and Online Data-Protection Reform
3. Konrad Kording-“Recommender systems: what does it mean to get good “recommendations?
4. Aaron Roth-"Robust and Equitable Uncertainty Estimation”
5. Hanming Fang -“Toward an economic theory of dysfunctional identity”
6. Richard Berk-“Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Optimal Transport and Conformal Prediction Sets”
7. Deborah Mayo ”Statistical Significance and its Critics: Practicing damaging science, or damaging scientific practice?”
8. Michael Kearns “An Algorithmic Framework for Bias Bounties”
9. Konstanin Genin "Causal Discovery, Randomization and Individualized Treatment"
 
This conference will also be accessible via Zoom here: https://upenn.zoom.us/j/92645349625