As electronic devices such as smartphones advance in technology, the number of people using them to watch videos or view photos is increasing. Along with this, interest is growing in the principle of how content related to past searches appears in the recommendation tab. It comes down to the algorithms. Here are two Sookmyungians who will share their opinion about the positive and negative impact these algorithms have had and will continue to have on people.
- Debate Topic -
Are algorithms beneficial or harmful to people?
1. <Algorithms are beneficial to people>
(Yang Eun-ji, Department of Mechanical Systems Engineering '23)
In Over-the-top (OTT) services such as YouTube or Netflix, the technology that recommends content to people's tastes automatically without them having to search for it is called an "algorithm." But what exactly is an algorithm? To define it simply, an algorithm is a set of rules that perform specific operations to solve a problem. An algorithm within an OTT service can be defined as the process of comparing content that users have watched with content that they have not watched to determine similarities and recommend content to users based on this calculation. Not only these OTT services, but also the operation of various technologies we use in our daily lives, such as the Internet, computer programs, and electronic devices, all run according to a blueprint called an algorithm. In daily life, algorithms are used in almost every field, from everyday electronic devices such as kiosks and smartphones to programs for corporate work. This allows people to perform tasks and work more efficiently with less effort and time. Therefore, algorithms provide many benefits to people's lives.
2. <Algorithms are harmful to people>
(Kim Eo-jin, English Language & Literature '22)
In the past, if we were watching a video on a certain topic and wanted to see another video with similar content or the same characters, we had to do our own search. Since algorithms have become commonplace, people can receive recommendations for videos they want without having to search for them and then watch them with just one click. These recommendations show similar yet new content. However, these algorithms also have negative aspects. As mentioned earlier, since similar content is recommended based on people's viewing habits, subscriptions, likes, and following history, unless they make a separate effort to do their own searching, the possibility of being exposed to new topics or perspectives is low. Nevertheless, I believe that any technology has its drawbacks, and that these can be overcome with effort. Technically, it might be possible to develop new algorithmic features that could recommend new topics unrelated to our viewing history. Personally, I think we could incorporate a variety of topics into our algorithm through our own research efforts rather than relying solely on the algorithm's recommendations. I believe that by complementing the shortcomings of algorithms and making the most of their strengths, we can develop algorithms into the kind of technology that benefits people.