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TikTok has quickly become one of the most popular social media platforms, with an estimated 800 million active users worldwide. While the app is beloved by many for its creative content and lighthearted videos, there is a dark side to TikTok that parents need to be aware of. The app has been criticized for its lack of privacy controls and for exposing children to inappropriate content. There have also been several reports of child predators using the app to groom and target minors.While TikTok does have some age-appropriate content filters in place, they are not foolproof. And because the app is so popular with kids, it’s important for parents to be aware of the potential dangers and talk to their kids about responsible social media use. Here are some of the potential dangers associated with children using TikTok.

Exposure to Inappropriate Content. 
There is a lot of user-generated content on TikTok, and not all of it is suitable for young eyes. Because the app is so popular with kids, there is a growing number of videos that feature inappropriate language, sexual references, and violence (Worley et al, 2019). There are also a lot of “challenges” that encourage risky or dangerous behavior, such as the Tide Pod Challenge, which encouraged kids to eat laundry detergent pods (Bever, 2018). And because TikTok videos are often set to music, there is also the risk of kids hearing explicit lyrics that they might not be able to understand.

Lack of Privacy Controls
TikTok does not have the same privacy controls as other social media platforms (Huddleston, 2022). For example, there is no way to make your account private or to approve who can follow you. This means that anyone can see your child’s videos and information, including their location if they have geotagging enabled. This lack of privacy can put kids at risk of being targeted by predators or cyberbullies. It also means that kids could accidentally share personal information or images that they wouldn’t want to be seen by the public. In addition, TikTok’s suggestion algorithm can expose users to more inappropriate content the more they use the app. This can create a dangerous feedback loop for kids who are already struggling with mental health issues.

Encourages Risky Behavior
As mentioned before, there are a lot of “challenges” on TikTok that can encourage kids to engage in risky behavior. These challenges often involve daring each other to do things that could be physically harmful, such as the Hot Water Challenge, which resulted in several hospitalizations (Dockrill, 2019). There are also challenges that encourage kids to break the law, such as the Bird Box Challenge, which involved blindfolding oneself and driving or walking around (Stevens, 2019). These types of challenges can normalize dangerous behavior and put kids in harm’s way.

Cyberbullying
Unfortunately, TikTok has also become a breeding ground for cyberbullying (Na, 2020). Because of the lack of privacy controls, it’s easy for bullies to target kids on the app. In addition, the comments section on TikTok videos is often filled with hateful and hurtful comments. This can be particularly difficult for kids to deal with because they might not have the same support system on TikTok as they do in real life. And because TikTok is so popular, the bullying can feel inescapable.

References

Bever, L. (2018, January 17). Teens are daring each other to eat Tide pods. We don’t need to tell you that’s a bad idea. The Washington Post. Retrieved on 17 October 2022, from https://www.washingtonpost.com/news/to-your-health/wp/2018/01/13/teens-are-daring-each-other-to-eat-tide-pods-we-dont-need-to-tell-you-thats-a-bad-idea/

Dockrill, P. (2019, February 11). That Viral ‘Boiling Water Challenge’ Is Landing People in Hospital, Just Stop, Please. Science Alert. Retrieved on 17 October 2022, from https://www.sciencealert.com/dangerous-viral-boiling-water-challenge-is-sending-people-to-hospital-doctors-warn

Huddleston, T. (2022, February 8). TikTok shares your data more than any other social media app — and it’s unclear where it goes, study says. CNBC. Retrieved on 17 October 2022, from https://www.cnbc.com/2022/02/08/tiktok-shares-your-data-more-than-any-other-social-media-app-study.html

Na, J. (2020, October 22). Cyberbullying on TikTok is a major issue. Youthopia. Retrieved on 17 October 2022, from https://youthopia.sg/read/cyberbullying-on-tiktok-is-a-major-issue/

Stevens, C. (2019, January 3). Netflix wil niet dat je de ‘Bird Box Challenge’ doet. IGN Benelux. Retrieved on 17 October 2022, from https://nl.ign.com/bird-box/110745/news/netflix-wil-niet-dat-je-de-bird-box-challenge-doet

Worley, B., Temko, S., & Bernabe, A.J. (2019, October 18). Young kids could be seeing mature content on TikTok. Here’s how to keep them safe. Good Morning America. Retrieved on 17 October 2022, from https://www.goodmorningamerica.com/living/story/young-kids-mature-content-tiktok-heres-safe-66366182

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A popular topic in the FinTech industry is Machine Learning (ML) and its numerous innovative applications. Andreas Braun, Director of Artificial Intelligence (AI) and Data Science at PwC, explained in a recent interview that in the last decade many AI, and more specifically ML, innovations have had a significant influence in the financial sector (England, 2022). But what exactly is machine learning? And what are its implications and applications in the FinTech industry?

Machine learning is a subdivision of artificial intelligence that uses data and algorithms to learn in the same way humans do, while progressively increasing its accuracy as it is learning (Sharbek, 2022). There are two main approaches to machine learning; supervised learning and unsupervised learning. Supervised learning is an approach where training materials with the correct output are fed to the algorithm (Azubi et al, 2018). The algorithm then learns to respond more accurately and quickly by comparing its output to the training materials.By learning from historical data the algorithm is able to predict future outputs. Unsupervised learning happens when no training materials are available and the algorithm is set out to find unidentified existing patterns from the data to derive rules from it (Azubi et al, 2018). 

As you can imagine, an algorithm that can be trained to find patterns or predict future outputs is very valuable in the financial industry. This is why machine learning has been applied in many different ways. A few examples are fraud prevention, automated customer service, cyber security, and transactions and payment confirmations (Sharbek, 2022). In fraud prevention for example, the algorithm assesses a client’s purchasing patterns to identify abnormal activity (Sharbek, 2022). However, machine learning also brings some implications. Most prominently, because machine learning is relatively new in the financial industry its ethical standards have only been set recently (Rizinski et al, 2022). This means that until not too long ago, it is possible that certain organizations have been operating their algorithms with different ethical standards. Andreas Braun solidifies this concern in his interview by stating that the European AI Act is still under discussion and foresees a risk-based approach towards the use of AI (England, 2022). Overall, machine learning is proving to be a valuable asset in the financial world opening up doors to automating onerous tasks and solving complex issues. 

References

Alzubi, J. et al. (2018) Machine Learning from Theory to Algorithms: An Overview. Journal of physics. Conference series. [Online] 1142 (1), 12012.

England, J. (2022). Why AI and ML is reshaping the fintech industry, Fintechmagazine.com. [Online]. Available at:  https://fintechmagazine.com/financial-services-finserv/why-ai-and-ml-is-reshaping-the-fintech-industry (Accessed: 13 October 2022)

Sharbek, N. (2022) How Traditional Financial Institutions have adapted to Artificial Intelligence, Machine Learning and FinTech? Proceedings of the International Conference on Business Excellence. [Online] 16 (1), 837–848.

Rizinski, M. et al. (2022) Ethically Responsible Machine Learning in Fintech. IEEE access. [Online] 97531–97557.