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Found 16 papers, 3 papers with code
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
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
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.
On the Relationship Between Explanations, Fairness Perceptions, and Decisions
It is known that recommendations of AI-based systems can be incorrect or unfair.
Social Norm Bias: Residual Harms of Fairness-Aware Algorithms
We study this issue through the lens of gender bias in occupation classification from biographies.
The effect of differential victim crime reporting on predictive policing systems
Our analysis is based on a simulation patterned after district-level victimization and crime reporting survey data for Bogot\'a, Colombia.
Leveraging Expert Consistency to Improve Algorithmic Decision Support
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
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
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.
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting
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.
Proceedings of NeurIPS 2018 Workshop on Machine Learning for the Developing World: Achieving Sustainable Impact
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
What are the biases in my word embedding?
The inputs to our algorithm are a list of target tokens, e. g. names, and a word embedding.
Learning under selective labels in the presence of expert consistency
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making.
Proceedings of NIPS 2017 Workshop on Machine Learning for the Developing World
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
We present an extension of sparse Canonical Correlation Analysis (CCA) designed for finding multiple-to-multiple linear correlations within a single set of variables.
Lass-0: sparse non-convex regression by local search
We compute approximate solutions to L0 regularized linear regression using L1 regularization, also known as the Lasso, as an initialization step.
