Publications

This section of our site houses white papers and other publications completed with support from the Notre Dame-IBM Tech Ethics Lab by faculty, student research assistants, and Lab partners. The library is searchable, and we add to it regularly, so be sure to check back often for updates.

• Lab White Paper

  The Return on Investment in AI Ethics: A Holistic Framework

  By: Nicholas Berente, Marialena Bevilacqua, Heather Domin, Brian Goehring, Francesca Rossi

  The authors propose a Holistic Return on Ethics (HROE) framework for understanding the return on organizational investments in artificial intelligence (AI) ethics efforts.

• Lab White Paper

  Preserving Privacy when Learning Individualized Treatment Rules: Sensitive data can provide valuable insights while still remaining private

  By: Spencer Giddens

  This paper applies the differential privacy framework to outcome weighted learning as a method of developing individualized treatment rules while providing mathematically provable privacy guarantees.

• Lab White Paper

  Philosophy of Work: How Can We Promote Flourishing, Purpose, and Meaning In and Through Work?

  By: Paul Blaschko, Claire Murphy

  Using tools from the virtue ethics method, this paper identifies three constituents of "good work" and offers suggestions for using technology to facilitate good work.

• Lab White Paper

  Learning Symbolic Models for Interpretability in Healthcare Applications

  By: Jennifer Schnur

  Symbolic modeling is an approach to machine learning that prioritizes the interpretability of an algorithm’s output. This paper suggests a first step towards achieving symbolic modeling by focusing on structural relationships within datasets.

• Lab White Paper

  Addressing Bias Against the Poor in Artificial Intelligence by Observing Individual Bias in Twitter

  By: Imani Mathenge

  This paper provides an approach to evaluate negative attitudes towards the poor on Twitter. As Twitter data is used to train AI models, bias against the poor on Twitter may instill bias against poor people in AI.

• Lab White Paper

  Inference From the Data: Protect Privacy When Releasing Results

  By: Bingyue Su

  Su develops the privacy-preserving version of Metropolis-Hastings (MH), a widely used sampling algorithm in statistics, and finds that the privatized algorithm maintains good data utility in finance and simulated data sets with formal privacy guarantees.

• Lab White Paper

  Readability of Water Reports in the U.S.: Disclosing Information Biases Through Flesch Readability Ease Scores

  By: Renee Ana Aziz

  Using Python, 1,000+ U.S. water reports were analyzed using optical character recognition. A dataframe displayed each water report’s readability through multiple readability indices. All processes disclosed a grave information bias that needs resolution.

• Lab White Paper

  Crowdsourcing From the Worker’s Perspective

  By: Dylan Cole, Timothy Sullivan, Simone Zhang, Emma Zurek

  This paper investigates how companies and researchers can respect the dignity and needs of workers on crowdsourcing platforms while also improving the quality of the data and insights that they gain.