\section*{Ethical Considerations}
\label{sec:ethics}

The study of automatic detection of disinformation can be a complicated task.
There is always the risk of the models being misused due to maliciousness, lack of information, or misinterpreting the purpose of the model.

An example of the first case could be a government or company looking to censor news articles that show them in an unfavourable light.
%In this case, it would be a matter of changing the way that they label the data to train the models.
Even though some of the assumptions we made in this paper might not hold true in this case, models that classify news articles could potentially be repurposed for other tasks.

Another issue could be blindly trusting the outputs of the model.
Given that our model statistically selects the class that an article is most likely to belong to, there is always the risk of it being wrong.
Because of this, it is important to always keep a human-in-the-loop approach when using these kinds of models.

People may also mistakenly use these kinds of models as a ``truth detector'' with other kinds of media.
We have discussed this issue in the \hyperref[sec:limitation]{Limitations} Section.

On top of that, there are the issues of where we get the data from and how it is annotated.
Even though the datasets we used obtain their annotations from independent fact-checking organizations, there is always the risk of conflicts of interest or unstated agendas.

Even though we take steps to mitigate these issues, we are aware that some of them might still linger, especially those regarding possible misuse of the model.