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Understand the unsaid
Humans remain the best in reading, interpreting and understanding content. Often times AI powered moderation fails to decode hidden meanings. On the other hand, humans are intuitive by nature, they are able to read between the lines and understand straight away. This helps to avoid the wrong flagging of content.
Don’t we all want to wow our customers with an exceptional customer experience? And the best way to do that is by creating real conversations with the audience. While AIs are programmed to be more conversational and interactive with customers, they aren’t humans and don’t have feels. They lack the humanity needed to connect with the customers on a personalized and engaging level.
Grasping the context
Taking English as an example, the same word can have different meanings depending on how it is used. Correspondingly, the same image can also have different meanings depending on the context it is used. It would be difficult for AI to determine the motive of a picture even if it detects it. For example, when giving reviews about a weight loss program, a customer may post a partially nude picture. Deciding whether the picture is appropriate or not, would be a challenge for an AI powered system. Contrary to that, a human moderator is able to immediately recognize the improperness of the image and conclude if it is acceptable or not.
Upholding a good brand reputation is imperative for a company’s continued success. And because we live in an online world, the first place frustrated customers go to vent their disappointment is online. And the last thing such a customer would want is to receive a generic AI canned response. During such instances, humans are the best alternatives as they have the intelligence and know-how to solve such conflicts by even flipping a negative experience to a positive one and living the customer happy and satisfied.
Thanks to technology advancement, AI deep-learning and neural networks have enabled the automation of numerous tasks, such as image classification, speech recognition and natural language processing. In spite of that, AI content moderation is hampered with frequent errors. Even with the training of numerous examples, neural networks are still unreliable to make accurate judgements of cases that appear different from their training data.
Ultimately, effective Content Moderation requires a good mix between a robust AI powered system to instantaneously and correctly filter content without exposing the moderators to sensitive material, handle a massive content volume and also a very adaptive team of empathetic moderatos with local cultural knowledge to accurately screen borderline user-generated content.
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