Realtime Limitations of Azure Content Safety
In the last segment, you implemented the moderation system for your Fooder app. A question was raised about whether integrating an automation moderation solution like Azure AI Safety Content will become the one-stop solution for all moderation-related issues. Unfortunately, this is not always the case.
Understanding Limitations of Azure Content Safety
Automated moderation solutions in today’s era, where user-generated and AI-generated data is exploding, offer a great solution for moderation. They provide various benefits:
- Ability to provide almost real-time moderation results for every request at any given time.
- Ability to scale and handle large amounts of moderation requests without impacting the moderation performance and latency in moderation responses.
- Ability to better understand the context and handle text and image data nuances.
Despite their advanced capabilities, AI moderation tools also face several limitations in the real world:
- Potential Improper Flagging: Although AI moderation tools are great at detecting harmful content, they are not entirely fail-proof. The AI might flag content as harmful due to misinterpretation. Conversely, it is possible that harmful content can slip past the AI model’s detection if it’s presented in a way that AI is not good enough to recognize.
- Cultural and Linguistic Nuances: In today’s globalized era, the content hosted by platforms comes from people with diverse cultures and languages. A phrase considered harmless in one culture might be harmful to people from different cultures. AI models may not fully grasp regional dialects, slang, or cultural references since they were not trained enough on diverse languages and cultural content data. They are ultimately leading to inappropriate moderation decisions.
- Evolving Harmful Content: Harmful content often develops over time, making it harder for AI models to detect. New slang, coded language, or emerging harmful trends can render existing AI models less effective unless they are retrained.
Introducing Human-in-the-loop to Overcome the AI-based Moderator’s Limitation
Most of the challenges faced by the AI-based moderation system — and any automated moderation system in general — can be dealt with by introducing humans in the reviewing process where these moderation systems are weak and may not perform well.
Introducing human moderators can help strengthen this AI-enabled system in many ways:
- Handling Edge Cases: They can pick a few moderation tasks for the content where these models have low scores or confidence. Since, humans excel at understanding context, sarcasm, and cultural nuances, they can make informed decisions that AI might miss, particularly in complex or ambiguous situations. Humans will also be able to identify and respond appropriately to new kinds of harmful content quickly and will be able to amend the community guidelines if required.
- Continuous Improvement of AI-based Moderators: Moderation response from handling edge cases and newly detected harm content can be used to retrain the AI-based moderators to retrain them. This loop will enable the moderator’s models to evolve and perform even better.
- Handling Appeals: Allowing users to appeal on already rejected or flagged content that they consider not harmful can further help understand any edge cases or scenarios where AI-based models fail to perform well. Human moderators can reassess these contents upon appeal, help correct any mistakes made by the AI, and even improve the AI-based moderator’s response by retraining them to handle such cases in the future.