Recently, researchers have been rushing to publish their research on misinformation and algorithmic bias before the arrival of new corporate management. This is an increasingly relevant topic considering the proliferation of news outlets placing their focus on short-term profits and sensationalism.
The problem of misinformation and algorithmic bias has cause a significant amount of distrust among the public, especially because of how easily it can spread within digital networks. Because it is often hard to recognize, it is possible for people to spread and even believe false information. This becomes especially problematic when it begins to be used to manipulate populations and affect public opinion on political and social issues.
Algorithmic bias is a type of issue related to computers not being able to process nonverbal cues and words in the same way that humans can. Because of this, algorithms can have an inherent bias and measure anything from elections and populations, to emotions and behaviors. Algorithms may also be based on incorrect information or use outdated information, leading to skewed and harmful results.
The researchers who are rushing to get their research published seem to be doing so to try and stay ahead of the curve, and to provide much-needed warnings of these issues. They are trying to provide a scientific foundation so that the public can be appropriately informed about biases and potential harms in the algorithmic environment.
The new management team might be intimidating, and it is understandable why researchers would want to get their findings out as soon as possible, but can also be a good thing. It is important to continue research in this field, but if management is more open to the idea of ethical and responsible algorithmic decisions, then it will give the researchers a platform on which their findings can be built upon.
Overall, research on misinformation and algorithmic bias continues to be an important issue and likely one that will become more prevalent as technology evolves. Research is needed to help guide decision makers on how to best reduce bias and misinformation in algorithms, and hopefully the research that has already been published and the ones to come will help ensure a more responsible and equitable algorithmic environment.