Can you trust AI? Here's why you shouldn't
An article for The Conversation about how to build AI that is more trustworthy.
Nathan E. Sanders
AI could shore up democracy – here’s one way
An article for The Conversation about how AI could be used in legislative engagement.
Build AI by the People, for the People
An article for Foreign Policy making the case for Public AI.
Can We Build Trustworthy AI?
An article for Gizmodo about how to live in a world with untrustworthy AI.
Just Wait Until Trump Is a Chatbot
An article for The Atlantic about how political campaigns may begin to use AI chatbots.
How AI could write our laws
An article for the Tech Review about how AI could be used to write legislation.
We Don’t Need to Reinvent our Democracy to Save it from AI
An article for Harvard's Belfer Center blog about how to address AI's coming impacts on democracy.
Estimating the Global Prevalence of Mass Public Shootings
Estimates the global prevalence of mass public shootings using Bayesian modeling.
Machine Learning Featurizations for AI Hacking of Political Systems
Investigates graph and sequence data representations for applying deep learning to predict outcomes in political and legislative systems, advancing the concept of AI hacking. [11, 17, 41]
The Contagion of Mass Shootings: The Interdependence of Large-Scale Massacres and Mass Media Coverage
Examines the interdependence of large-scale massacres and mass media coverage.
Forecasting the Severity of Mass Public Shootings in the United States
Forecasts the future severity of mass public shootings in the U.S. using statistical modeling.
Sewage: An Environmental Justice Tragedy
A blog post for the Mystic River Watershed Association about sewage overflows.
Does Media Coverage of Mass Public Shootings Create a Contagion Effect?
A short review article for Significance Magazine about the contagion effect in mass public shootings.
SuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae
A semi-supervised SN photometric classification pipeline using Pan-STARRS1 data.
Can the Coronavirus Prompt a Global Outbreak of 'Distributional Thinking' in Organizations?
Discusses the potential for the COVID-19 pandemic to foster 'distributional thinking' within organizations, drawing parallels with social justice movements.
Supernova Photometric Classification Pipelines Trained on Spectroscopically Classified Supernovae from the Pan-STARRS1 Medium-Deep Survey
SN photometric classification pipelines trained on Pan-STARRS1 MDS SNe.
AMEND: Open source, data-driven oversight of water quality in New England
Details the AMEND project, an open-source initiative for data-driven monitoring and oversight of water quality in New England.
A Balanced Perspective on Prediction and Inference for Data Science in Industry
Offers a balanced view on the roles of prediction and inference in data science within industrial applications, emphasizing their interconnectedness.
The Role of Prior Information in Inference on the Annualized Rates of Mass Shootings in the US
Examines how prior information affects statistical inferences regarding the yearly rates of mass shootings in the United States.
Clust-LDA: Joint Model for Text Mining and Author Group Inference
A joint model for text mining and inferring author groups.