AI looks to be the ideal response to the rising difficulties of content moderation on social media platforms, given the vast amount of data, the frequency of violations, and the need for human judgments without requiring people to make them.
We sometimes portray AI content moderation as a necessary response: the enormous size of social media platforms like Facebook and YouTube explains why AI approaches are desirable, if not imperative.
Interactive online platforms have grown more important in our daily lives. By eliminating traditional editorial constraints, user-generated content has stimulated dynamic online dialogues, improved business processes, and increased access to information. However, it has also created substantial issues in terms of how to regulate harmful online content.
As the volume of user-generated content rises, it becomes more difficult for internet and social media companies to keep up with the need to monitor what they post on their platforms. AI-based content moderation systems have emerged as key tools for solving this issue.
Automation in Content Moderation and Limitations
Automation in content moderation is a broad concept: in certain aspects, AI content moderation systems use very little intelligence. AI in content moderation can refer to the use of a variety of automated approaches at various phases of content moderation. These techniques might range from simple keyword filters to machine learning and a wide range of tools and methodologies.
Because of their design, different systems for automatic identification and analysis of user-generated content will have limitations — for example, a tool designed to recognize “poison” statements in a single language may struggle to interpret a multilingual text.
The significance of context
The context of a post often determines whether it violates the law or a content guideline, which the machine learning system ignores. Some information, such as the speaker’s identity or the sender and receiver of a message, might be included in a machine learning tool’s analysis, but this has significant privacy consequences. Other sorts of context, such as historical, political, and cultural context, are far more difficult to uncover using technology.
Lack of representative and well-annotated training datasets
Machine learning systems develop their ability to detect and categorize different content based on the datasets we trained them on. Many systems are trained using publicly available labelled datasets; if these datasets do not include examples of speech in a variety of languages and from a variety of groups or communities, the resulting algorithms cannot comprehend these groups’ communication.
An annotation may introduce bias into supervised learning
In labelling a dataset for supervised learning, reviewing samples and picking the proper label, or evaluating an automatically applied label, many human beings are usually involved. Intercoder reliability is an important indicator for understanding how well different people who are labelling a dataset perform consistently. Low intercoder reliability shows that the humans who apply the label differ on whether the content is considered “hate speech” or “spam.”
Models that are flexible and dynamic are necessary.
Because human communication patterns change rapidly, flexible, dynamic models are required, and speakers who are blocked by automated filters have an added incentive to figure out how to get around the filter. Static machine learning algorithms will quickly become outmoded, unable to accurately categorize users’ communications.
The uniqueness of the domain
Natural language processing software performs best in conditions that are comparable to those in which we taught it. It’s difficult to build tools that work in a variety of settings, languages, cultures, interest groups, and topic areas. For example, facebook’s AI cannot determine the underlying meaning or intentions behind a piece of information. It is for this reason that human-in-the-loop facebook content moderation services are essential.
Technology and Human Moderators
While artificial intelligence (AI) has come a long way, and companies are continually refining their AI algorithms, we still require human moderators to maintain brand online and ensuring your content is of high quality.
Humans are still the greatest at reading, comprehending, interpreting, and filtering information. As a result, for building an online presence and curating content, elite companies will combine AI and human expertise.
Humans can hold discussions.
If you want to engage your audience in actual online conversations, you’ll need human moderators.
No one is fooled just yet, even though AI is being trained to be more communicative. Chatbots, for example, may be helpful in communicating with customers and giving simple information. They lack the compassion needed to properly engage with clients in a meaningful and personalized conversation.
Human-in-the-loop moderation is ideal for connecting with customers. They can respond to comments and messages quickly, allowing for a two-way conversation.
Humans can decipher hidden meanings in sentences.
Human moderators are better at reading between the lines, which is one of the most essential reasons for utilizing them. Hidden meanings will occasionally elude an AI, even though a human could usually grasp the meaning in a fraction of a second.
Humans have a better chance of learning about business.
Human-in-the-loop moderation also gives moderators a greater understanding of what their clients are thinking. They’ll be able to see any notable trends that arise. Human moderators understand the value of social listening and are skilled at posing questions to customers and requesting feedback on products and services.
Human moderators may help your company go forward by engaging customers, reading between the lines, and taking suggestions seriously. The information they receive may be put to good use in your organization and used to guide future marketing strategies and activities.
Humans are still a long way from being entirely replaced by AI. On the other side, using AI as a preliminary screening for human moderators can offer you the best of both worlds. If you want your company to prosper online, you need to keep humans involved. If you’re not sure if human-in-the-loop moderation services are right for your online business, look into all the following considerations.