31 research outputs found
Fairness in representation: quantifying stereotyping as a representational harm
While harms of allocation have been increasingly studied as part of the
subfield of algorithmic fairness, harms of representation have received
considerably less attention. In this paper, we formalize two notions of
stereotyping and show how they manifest in later allocative harms within the
machine learning pipeline. We also propose mitigation strategies and
demonstrate their effectiveness on synthetic datasets.Comment: 9 pages, 6 figures, Siam International Conference on Data Minin
FairCanary: Rapid Continuous Explainable Fairness
Machine Learning (ML) models are being used in all facets of today's society
to make high stake decisions like bail granting or credit lending, with very
minimal regulations. Such systems are extremely vulnerable to both propagating
and amplifying social biases, and have therefore been subject to growing
research interest. One of the main issues with conventional fairness metrics is
their narrow definitions which hide the complete extent of the bias by focusing
primarily on positive and/or negative outcomes, whilst not paying attention to
the overall distributional shape. Moreover, these metrics are often
contradictory to each other, are severely restrained by the contextual and
legal landscape of the problem, have technical constraints like poor support
for continuous outputs, the requirement of class labels, and are not
explainable.
In this paper, we present Quantile Demographic Drift, which addresses the
shortcomings mentioned above. This metric can also be used to measure
intra-group privilege. It is easily interpretable via existing attribution
techniques, and also extends naturally to individual fairness via the principle
of like-for-like comparison. We make this new fairness score the basis of a new
system that is designed to detect bias in production ML models without the need
for labels. We call the system FairCanary because of its capability to detect
bias in a live deployed model and narrow down the alert to the responsible set
of features, like the proverbial canary in a coal mine
Generating Interactive Worlds with Text
Procedurally generating cohesive and interesting game environments is
challenging and time-consuming. In order for the relationships between the game
elements to be natural, common-sense has to be encoded into arrangement of the
elements. In this work, we investigate a machine learning approach for world
creation using content from the multi-player text adventure game environment
LIGHT. We introduce neural network based models to compositionally arrange
locations, characters, and objects into a coherent whole. In addition to
creating worlds based on existing elements, our models can generate new game
content. Humans can also leverage our models to interactively aid in
worldbuilding. We show that the game environments created with our approach are
cohesive, diverse, and preferred by human evaluators compared to other machine
learning based world construction algorithms
A World Full of Stereotypes? Further Investigation on Origin and Gender Bias in Multi-Lingual Word Embeddings
Publicly available off-the-shelf word embeddings that are often used in productive applications for natural language processing have been proven to be biased. We have previously shown that this bias can come in a different form, depending on the language and the cultural context. In this work we extend our previous work and further investigate how bias varies in different languages. We examine Italian and Swedish word embeddings for gender and origin bias, and demonstrate how an origin bias concerning local migration groups in Switzerland is included in German word embeddings. We propose BiasWords, a method to automatically detect new forms of bias. Finally, we discuss how cultural and language aspects are relevant to the impact of bias on the application, and to potential mitigation measures