1,383 research outputs found
Gender Artifacts in Visual Datasets
Gender biases are known to exist within large-scale visual datasets and can
be reflected or even amplified in downstream models. Many prior works have
proposed methods for mitigating gender biases, often by attempting to remove
gender expression information from images. To understand the feasibility and
practicality of these approaches, we investigate what exist within large-scale visual datasets. We define a
as a visual cue that is correlated with gender,
focusing specifically on those cues that are learnable by a modern image
classifier and have an interpretable human corollary. Through our analyses, we
find that gender artifacts are ubiquitous in the COCO and OpenImages datasets,
occurring everywhere from low-level information (e.g., the mean value of the
color channels) to the higher-level composition of the image (e.g., pose and
location of people). Given the prevalence of gender artifacts, we claim that
attempts to remove gender artifacts from such datasets are largely infeasible.
Instead, the responsibility lies with researchers and practitioners to be aware
that the distribution of images within datasets is highly gendered and hence
develop methods which are robust to these distributional shifts across groups.Comment: ICCV 202
Balancing Biases and Preserving Privacy on Balanced Faces in the Wild
Demographic biases exist in current models used for facial recognition (FR).
Our Balanced Faces in the Wild (BFW) dataset is a proxy to measure bias across
ethnicity and gender subgroups, allowing one to characterize FR performances
per subgroup. We show that results are non-optimal when a single score
threshold determines whether sample pairs are genuine or imposters.
Furthermore, within subgroups, performance often varies significantly from the
global average. Thus, specific error rates only hold for populations matching
the validation data. We mitigate the imbalanced performances using a novel
domain adaptation learning scheme on the facial features extracted from
state-of-the-art neural networks, boosting the average performance. The
proposed method also preserves identity information while removing demographic
knowledge. The removal of demographic knowledge prevents potential biases from
being injected into decision-making and protects privacy since demographic
information is no longer available. We explore the proposed method and show
that subgroup classifiers can no longer learn from the features projected using
our domain adaptation scheme. For source code and data, see
https://github.com/visionjo/facerec-bias-bfw.Comment: arXiv admin note: text overlap with arXiv:2102.0894
AAAI Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD)
This book is a collection of the accepted papers presented at the Workshop on Artificial Intelligence with Biased or Scarce Data (AIBSD) in conjunction with the 36th AAAI Conference on Artificial Intelligence 2022. During AIBSD 2022, the attendees addressed the existing issues of data bias and scarcity in Artificial Intelligence and discussed potential solutions in real-world scenarios. A set of papers presented at AIBSD 2022 is selected for further publication and included in this book
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Unfair behaviors of Machine Learning (ML) software have garnered increasing
attention and concern among software engineers. To tackle this issue, extensive
research has been dedicated to conducting fairness testing of ML software, and
this paper offers a comprehensive survey of existing studies in this field. We
collect 100 papers and organize them based on the testing workflow (i.e., how
to test) and testing components (i.e., what to test). Furthermore, we analyze
the research focus, trends, and promising directions in the realm of fairness
testing. We also identify widely-adopted datasets and open-source tools for
fairness testing
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
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