Driving Child Safety Using Large Language Models

With the rise of the internet, more and more users are connected. It has become difficult to identify the intended users to serve a content. In the past various methods like masking sensitive contents before presenting to a user have been employed but this also does not solve all the pressing issues.

As per UNICEF research, one child in every three users is an internet user and below 18 years of age. This is fueled by the rise of smartphone devices. As more and more children are connected, security becomes an important aspect of the internet. According to a recent report from Common Sense Media, kids between 8-12 years of age average nearly five hours of screen time a day. For teens, that number jumps to well over seven hours. The majority of time is spent either on web surfing or social media.

These venues hold tons of information and information security is required in place to ensure proper content is served to these children.

Challenges

There are various challenges associated with this group of users. Few of them are:

1. Identification: The users can be identified by entering their age or birth information. However, there is no authority to validate this information, users can enter any arbitrary date to identify and pose themselves as an older user than the actual age.
2. Content Moderation: There are various firewalls placed across certain organization policies to ensure some websites cannot be reached. But there are ways to bypass these limitations to access the internet without any guard or control.
3. Cyberbullying: As more and more users are connected online, cyberbullying can impact kids in a fraction of time. With more connected users online, the information travels at a lightning speed. Cyberbullying could be defamation, publicly exposing private information, harassment, identity theft etc.
4. Cybersecurity Issues: These issues are compromised devices, compromised networks, lack of security measures etc. This could lead to severe damage to the kids as well as users of the device and network.

Mitigation Using Large Language Models

Large Language Models (LLM) are trained on a wide source of data and hold vast information on the internet. There are various uses of these LLM due to its vast training data. One such usage can be in mitigating the security threat imposed to children using the internet. A large language model, can be used to help ensure child safety online in several ways:

1. Content moderation: Large language models can analyze text and identify potentially harmful or explicit content that may pose a risk to children. By analyzing user-generated content on social media platforms, forums, and other online communities, the model can flag potential threats and alert moderators or parents. These models can be programmed to remove any unwanted content to be presented to these children.
2. Filtering inappropriate content: Parents and guardians can use large language models to filter out inappropriate or harmful content from search results, websites, and apps. For example, they can input keywords related to child exploitation or cyberbullying, and the model can provide only safe and appropriate responses. Also, the large language models can be architected in a way to filter these data at the network layer itself.
3. Chatbots and virtual assistants: Large language models can power chatbots and virtual assistants designed specifically for child safety. These AI assistants can engage with children in safe and age-appropriate interactions while also monitoring their behavior for signs of harm. They can answer questions about topics like online privacy, sexting, cyberbullying, and healthy relationships. These chatbots can be used for educational purposes and act as a central place of information.
4. Creativity for educational resources: Large language models can generate creative and engaging educational resources related to digital citizenship, online safety, and healthy internet usage. This could include interactive stories, games, quizzes, videos, and infographics that teach kids essential skills for staying safe online.
5. Support for parents and caregivers: Large language models can offer guidance and support to parents and caregivers in helping keep their children safe online. They can suggest resources, provide information on best practices, and recommend strategies for having open and honest conversations about online safety with their kids.
6. Collaborating with experts: Large language models can work alongside human experts, such as psychologists, law enforcement officers, educators, and technology professionals, to develop effective and evidence-based interventions for promoting child safety online. By understanding the nuances of human communication patterns, intent, and context, these systems can complement human expertise and contribute valuable insights into complex issues related to child safety online.
7. Identification of Users: Large language models can analyze user behavior online and identify their age group. Based on this, the application can moderate content instead the user identified themself as an old user

Conclusion

In conclusion, LLM in conjunction with various different software architectures, can be used to analyze user behavior to identify the age group based on various parameters. Once identified, these models can help in moderating the content for intended users. These models can be a baseline for applications intended for child safety and parenting for the digital era. A chatbot based on these large language models can act as a single point of information for any sort of cybersecurity issues faced by children and their parents. Leveraging LLM can help social media as well as content producers to shield children from consuming inappropriate contents.

Please note that the views expressed in this post are author’s personal opinions.