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As machine learning methods become more powerful and capture more nuances of human behavior, biases in the dataset can shape what the model learns and is evaluated on. This paper explores and attempts to quantify the uncertainties and biases due to annotator demographics when creating sentiment analysis datasets. We ask >1000 crowdworkers to provide their demographic information and annotations for multimodal sentiment data and its component modalities. We show that demographic differences among annotators impute a significant effect on their ratings, and that these effects also occur in each component modality. We compare predictions of different state-of-the-art multimodal machine learning algorithms against annotations provided by different demographic groups, and find that changing annotator demographics can cause >4.5 in accuracy difference when determining positive versus negative sentiment. Our findings underscore the importance of accounting for crowdworker attributes, such as demographics, when building datasets, evaluating algorithms, and interpreting results for sentiment analysis.
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Interpreting Expert Annotation Differences in Animal Behavior
Hand-annotated data can vary due to factors such as subjective differences, intra-rater variability, and differing annotator expertise. We study annotations from different ex- perts who labelled the same behavior classes on a set of an- imal behavior videos, and observe a variation in annotation styles. We propose a new method using program synthesis to help interpret annotation differences for behavior analysis. Our model selects relevant trajectory features and learns a temporal filter as part of a program, which corresponds to estimated importance an annotator places on that feature at each timestamp. Our experiments on a dataset from behav- ioral neuroscience demonstrate that compared to baseline approaches, our method is more accurate at capturing an- notator labels and learns interpretable temporal filters. We believe that our method can lead to greater reproducibility of behavior annotations used in scientific studies. We plan to release our code.
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- Award ID(s):
- 1918865
- PAR ID:
- 10325786
- Date Published:
- Journal Name:
- CVPR 2021 Workshop on CV4Animation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
