Participants are protected, especially

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This is especially important because videos may contain personal identifiers and other sensitive information. Anonymity and Confidentiality: Researchers must ensure that the identities of if the video is shared across the research team. This may involve blurring faces or using voice changes in some cases. 7.2 Bias in Video Analysis While video provides deeper insights, it can also introduce new types of bias. Human researchers analyzing video may bring their own assumptions and interpretations to the data djibouti b2b leads which can skew the results. Additionally, AI-powered video analysis tools can exhibit biases if they are not properly trained on diverse data sets.


7.2.1 Strategies to Mitigate Bias AI Diversity Training: AI tools used to analyze video data should be trained on a wide range of facial expressions, gestures, and vocal tones to reduce cultural or demographic bias. Inter-rater Reliability: When human researchers analyze video data, using multiple raters can help ensure consistency and reduce the impact of individual biases. Automated Tools with Caution: While AI-driven tools for emotion detection and sentiment analysis are powerful, they should be used as a supplement rather than a replacement for human judgment. 7.3 Participant Comfort and Ethical Concerns Some participants may be uncomfortable with the use of video in research, especially if they feel self-conscious about being on camera.