Summary of High Risk Of Political Bias in Black Box Emotion Inference Models, by Hubert Plisiecki et al.
High Risk of Political Bias in Black Box Emotion Inference Models
by Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus, Artur Pokropek
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study investigates political bias in emotion inference models used for sentiment analysis (SA) in social science research, highlighting a pervasive issue that can skew text data interpretation. Machine learning models often reflect biases in their training data, impacting outcome validity. The paper conducts a bias audit on a Polish sentiment analysis model and finds systematic differences influenced by political affiliations. To mitigate this, the study prunes the training dataset of texts mentioning politicians, reducing bias but not eliminating it completely. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how machine learning models can be biased towards certain political views, which can affect the accuracy of text analysis results in social science studies. The study looks at a specific model developed in the lab and finds that it is more likely to predict certain emotions when talking about politicians from one party versus another. To fix this problem, the researchers try removing some of the training data that mentions these politicians and find that this helps reduce the bias. |
Keywords
» Artificial intelligence » Inference » Machine learning
