AI alignment: Assessing the global impact of recommender systems

The recent growing concerns surrounding the pervasive adoption of generative AI can be traced back to the long-standing influence of AI algorithms that have predominantly served as content curators on large online platforms. These algorithms are used by online services and platforms to decide what content to show and in what order, and they can have a negative impact, including the spread of misinformation, social polarization, and echo chambers around important topics. Frances Haugen, a former Facebook employee turned whistleblower, has drawn significant public attention to this issue by revealing the company's alleged knowledge about the negative impacts of their own algorithms. Additionally, a recent initiative to ban TikTok as a threat to US national security indicates the influence of recommender systems. The objective of this study is threefold. The first goal is to provide an exhaustive evaluation of the profound worldwide influence exerted by algorithm-based recommendations. The second goal is to determine the degree of priority accorded by the scientific community to pivotal subjects in recommender systems discussions, such as misinformation, polarization, addiction, emotional contagion, privacy, and bias. Finally, the third goal is to assess whether the level of scientific research and discourse is commensurate with the significant impact these recommendation systems have globally. The research concludes the impact of recommender systems on society has been largely neglected by the scientific community, despite the fact that more than half of the world's population interacts with them on a daily basis. This becomes especially apparent when considering that algorithms exert influence not just on major societal issues but on every aspect of a user's online experience. The potential consequences for humanity are discussed, such as addiction to technology, weakening relations between humans, and the homogenizing effects on human minds. One possible direction to address the challenges posed by these algorithms is the application of algorithmic regulation to promote content diversity and facilitate democratic engagement, such as the tripartite solution which is elaborated upon in the conclusion. Therefore, future research should not only be centered around further evaluating influence of this technology, but also the analysis of how such systems can be regulated. A broader conversation among all stakeholders should be evoked on these potential approaches, aiming to align AI with societal values and enhance human well-being.