I recently became interested in how do universities in the higher education world uses AI for learning purposes. Some universities forbid the use of AI altogether. Others, in my view more progressive, give the students guidelines. The guidelines are about ethical use and how to create prompts to serve the purpose of assignment writing. I think this is more of a progressive approach because it enhances the student's ability to guide their own learning as well as empowering the person. Whether we are ready or not, AI is here to stay.
What's the difference between an algorithm and AI? An algorithm is a set of instructions. A group of algorithm that is able to modify itself is referred to as Artificial Intelligence (Helm et al., 2020). In my opinion, there is no such a thing as artificial intelligence, but this is a debate for another time.
Algorithms are taking over not just learning, but also our self development. It can be a major turning point in our development as a society, so we need to make sure this is conducted in an ethical way. As we become more aware of the risks and biases in machine-learning, we need to introduce boundaties to improve the process.
Algorithms are already impacting the way we consume content on social media, and therefore it directly affects our behaviour, and in particular our consumer-behaviour. The algorithm decides what we see in a “for you” page, effectively driving entertainment and social connections.
Certain engines, like Google or Amazon display things that are more searched for at the top, thus making them more appealing and easily accessible. This has been widely researched in terms of marketing (Chen et al., 2020). So the items searched for the most are the most likely to be bought and consumed again. This creates a self-feeding loop with a direct impact on food consumption and prices.
Another example is how people use Spotify and how it automatically generates music recommendations according to what the user is more likely to listen. This has already had some significant impact on people’s taste in music. Since Spotify exists, users are more likely to listen to pop, rather than other genres (Zhang et al., 2013; Anderson et al., 2020). What does this mean for smaller producers? How will this affect music produced in the future?
This also affects the way we consume news. Certain events are not talked about much in mainstream media because they produce less engagement. At the moment, people's interest and engagement drive the news. This process creates polarisation in political choices (Feezel et al., 2021), effectively impacting how countries are ruled and how policies are decided.
Overall algorithms push people to mainstream consumption, cutting away diversified use. What is the implication of this change on our well-being and mental health? This is still to be investigated on a larger scale, but we can already see how the self-feeding machine-learning impacts a lot of factors in our lives. The next challenge is to find a way to fight this conformity, sameness and polarisation. At the same time we want to keep the benefits of AI and algorithms. We need to support the varied and diverse experience of life, so we can all be unique individuals. How we are going to use machine-learning as a tool to support our individuality is the next big debate.
References
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020, April). Algorithmic effects on the diversity of consumption on spotify. In Proceedings of the web conference 2020 (pp. 2155-2165).
Chen, R., Yang, B., Li, S., & Wang, S. (2020). A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem. Computers & industrial engineering, 149, 106778.
Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., ... & Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.
Feezell, J. T., Wagner, J. K., & Conroy, M. (2021). Exploring the effects of algorithm-driven news sources on political behavior and polarization. Computers in human behavior, 116, 106626.
Zhang, B., Kreitz, G., Isaksson, M., Ubillos, J., Urdaneta, G., Pouwelse, J. A., & Epema, D. (2013, April). Understanding user behavior in spotify. In 2013 Proceedings IEEE INFOCOM (pp. 220-224). IEEE.