296 citations · 577 across the 12 of their papers we have counts for
14 papers
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
Maria De-Arteaga, Alexandra Chouldechova, Artur Dubrawski
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this pa…
The effect of differential victim crime reporting on predictive policing systems
Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova
Police departments around the world have been experimenting with forms of place-based data-driven proactive policing for over two decades. Modern incarnations of such systems are c…
Leveraging Expert Consistency to Improve Algorithmic Decision Support
Maria De-Arteaga, Vincent Jeanselme, Artur Dubrawski +1
Machine learning (ML) is increasingly being used to support high-stakes decisions. However, there is frequently a construct gap: a gap between the construct of interest to the deci…
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
Maria De-Arteaga, Riccardo Fogliato, Alexandra Chouldechova
The increased use of algorithmic predictions in sensitive domains has been accompanied by both enthusiasm and concern. To understand the opportunities and risks of these technologi…
Proceedings of NeurIPS 2019 Workshop on Machine Learning for the Developing World: Challenges and Risks of ML4D
Maria De-Arteaga, Tejumade Afonja, Amanda Coston
This is the proceedings of the 3rd ML4D workshop which was help in Vancouver, Canada on December 13, 2019 as part of the Neural Information Processing Systems conference.
Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism
Maria De-Arteaga, Benedikt Boecking
After the peace agreement of 2016 with FARC, the killings of social leaders have emerged as an important post-conflict challenge for Colombia. We present a data analysis based on o…
What's in a Name? Reducing Bias in Bios without Access to Protected Attributes
Alexey Romanov, Maria De-Arteaga, Hanna Wallach +7
There is a growing body of work that proposes methods for mitigating bias in machine learning systems. These methods typically rely on access to protected attributes such as race,…
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
Maria De-Arteaga, Alexey Romanov, Hanna Wallach +6
We present a large-scale study of gender bias in occupation classification, a task where the use of machine learning may lead to negative outcomes on peoples' lives. We analyze the…
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact
Maria De-Arteaga, Amanda Coston, William Herlands
This is the Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact, held in Montreal, Canada on December 8, 2018
Learning under selective labels in the presence of expert consistency
Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises w…
Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World
Maria De-Arteaga, William Herlands
This is the Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World, held in Long Beach, California, USA on December 8, 2017
Discovery of Complex Anomalous Patterns of Sexual Violence in El Salvador
Maria De-Arteaga, Artur Dubrawski
When sexual violence is a product of organized crime or social imaginary, the links between sexual violence episodes can be understood as a latent structure. With this assumption i…
Canonical Autocorrelation Analysis
Maria De-Arteaga, Artur Dubrawski, Peter Huggins
We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables. Unlike CCA, w…
Lass-0: sparse non-convex regression by local search
William Herlands, Maria De-Arteaga, Daniel Neill +1
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step. Our algorithm, the Lass-0 ("Lass-z…