Search Results for author: Maria De-Arteaga

Found 16 papers, 3 papers with code

Doubting AI Predictions: Influence-Driven Second Opinion Recommendation

no code implementations29 Apr 2022 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.

Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms

no code implementations28 Apr 2022 Terrence Neumann, Maria De-Arteaga, Sina Fazelpour

Faced with the scale and surge of misinformation on social media, many platforms and fact-checking organizations have turned to algorithms for automating key parts of misinformation detection pipelines.

Fact Checking Fairness +1

Social Norm Bias: Residual Harms of Fairness-Aware Algorithms

no code implementations25 Aug 2021 Myra Cheng, Maria De-Arteaga, Lester Mackey, Adam Tauman Kalai

We study this issue through the lens of gender bias in occupation classification from biographies.

Decision Making Fairness

The effect of differential victim crime reporting on predictive policing systems

1 code implementation30 Jan 2021 Nil-Jana Akpinar, Maria De-Arteaga, Alexandra Chouldechova

Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot\'a, Colombia.

Fairness

Leveraging Expert Consistency to Improve Algorithmic Decision Support

no code implementations24 Jan 2021 Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova

However, the nature of the labels available for training these models often hampers the usefulness of predictive models for decision support.

Killings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism

1 code implementation19 Jun 2019 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.

Applications Computers and Society

What's in a Name? Reducing Bias in Bios without Access to Protected Attributes

no code implementations NAACL 2019 Alexey Romanov, Maria De-Arteaga, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Anna Rumshisky, Adam Tauman Kalai

In the context of mitigating bias in occupation classification, we propose a method for discouraging correlation between the predicted probability of an individual's true occupation and a word embedding of their name.

Word Embeddings

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

2 code implementations27 Jan 2019 Maria De-Arteaga, Alexey Romanov, Hanna Wallach, Jennifer Chayes, Christian Borgs, Alexandra Chouldechova, Sahin Geyik, Krishnaram Kenthapadi, Adam Tauman Kalai

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.

Classification General Classification

Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact

no code implementations21 Dec 2018 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

no code implementations2 Jul 2018 Maria De-Arteaga, Artur Dubrawski, Alexandra Chouldechova

We explore the problem of learning under selective labels in the context of algorithm-assisted decision making.

Data Augmentation Decision Making +1

Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World

no code implementations27 Nov 2017 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

Canonical Autocorrelation Analysis

no code implementations19 Nov 2015 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.

Anomaly Detection

Lass-0: sparse non-convex regression by local search

no code implementations13 Nov 2015 William Herlands, Maria De-Arteaga, Daniel Neill, Artur Dubrawski

We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step.

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