There are a lot of discussions these days about what's wrong with social media in general. The topic of self-esteem, disinformation, and privacy has received a lot of attention. People suggest broad-brush solutions, such as "split it up" or "get rid of engagement algorithms," that is unlikely to address the core concerns.
Every second, tens of thousands of pieces of content are posted on social media in the form of video, text, picture, or audio. Cyber harassment or bullying, misinformation, hate speech, child sexual abuse, suicide, self-harm, violent extremism, violent and explicit content, or fraud are just a few of the issues.
Social media can be a chaotic place if it isn't used correctly. The large volume of material and the possibility of virality make the process hard to handle manually; thus, social media businesses are increasingly relying on human-empowered AI-based technology to discover and remove such hazardous information.
With the massive quantity of the data, the relentlessness of the infractions, and the necessity for human judgments without wanting people to make them, AI appears to be the correct answer to the mounting issues of content moderation on social media platforms.
The trend toward automatic content moderation is frequently described as a necessary response to scale: the massive size of social media platforms like Facebook and YouTube explains why AI techniques are desirable, if not required. However, in order to achieve very precise findings, a human-in-the-loop system is needed, as automatic processes cannot provide highly accurate results on their own.
Problems with AI Content Moderation Services
The right to freedom of expression and opinion, the right to privacy, the right to personal data protection, the right to non-discrimination and several other crucial things may all be impacted by AI-based content moderation and curation. When algorithms fail to recognize sensational violence-inciting or misleading material and instead give it a boost because of the significant amount of user engagement it receives, it can aid in the mobilization of people for genocide. Failure to ban online harassment, slander, and hate speech directed against female politicians are some other reasons.
Automated content curation may jeopardize economic and social rights too, particularly when targeted ad algorithms select which financial possibilities and adverts should be given to consumers based on demographic profiling.
The limitations of techno-solutionism may be seen in these problematic features of AI-based content control. Legislators and governments should avoid putting their expectations for the avoidance of online harms on technological advancements and content monitoring by internet corporations because technology cannot address issues that we haven't solved in the real world.
Most online damages, from hate speech to cyber harassment, from disinformation to violent extremism, are founded in or interwoven with complex themes including sexism, racism, ultranationalism, homophobia, and transphobia, as well as poverty and is hard to detect by AI-powered solutions.
Why human-in-the-loop is required
The necessity for human contact with machine learning systems in order to enhance their performance is referred to as "human-in-the-loop." Indeed, algorithmic moderation systems are unable to work without the assistance of people. The machine must be conceived, built, maintained, and updated with fresh training data on a regular basis, necessitating a complicated human labor supply chain. Training data is made by applying several annotation and labeling techniques like bounding boxes, polygon image, 3D cuboids, semantic segmentation and among others.
Human moderators' primary responsibility is to assess user appeals against specific machine-made judgments and to make judgments in circumstances when machine learning algorithm confidence is low. This might result in an edge case. With our automated API integration approach, Cogito delivers high-quality Edge Cases last-mile automation and solutions to reputable AI firms.
Other most crucial reasons for using human moderators is that they are better at reading between the lines. Hidden meanings will occasionally escape an AI, even if a person could often comprehend the meaning in a second.
Another advantage of human moderation is that it engages customers. The knowledge they gain may be put to good use in your company and can also drive future marketing approaches and initiatives.
Human-in-the-Loop Data Labeling with Cogito
Artificial intelligence can only be as accurate as the data it is fed. Machine learning still requires highly qualified data annotation professionals to provide an adequate performance model. It's simply a matter of finding the proper mix of people and technology.
Cogito provides data collecting, annotation, content moderation and labeling services to fuel the most creative AI solutions. We can manage challenging, large-scale data labeling projects, whether you're constructing computer vision or natural language processing (NLP) applications.