3 Ways to Avoid Biased Language in Essays

"Sticks and stones may break my bones, but words never hurt me" There is an old phrase. Although this may be correct physically, I ignore the power of prejudice.

The words we use are important. We previously wrote about biased languages ​​and gender in academic papers, but it may also be related to factors such as race, age, sex, class, obstacle and so on.

Unfortunately, we can not cover all of them in a blog post. However, we can provide some general instructions to avoid prejudice of written assignment.

If you are writing articles about individuals or groups, please try to think about their viewpoint. Most of them use the correct terminology. That means avoiding words insulting the person you are writing.

In most cases, this is obvious. However, some older books use old-fashioned terminology that is currently considered aggressive. For example, calling a Native American "Red Indian" is not acceptable.

Please check online instructions if you need to quote where you are using the word that you feel bad. You can edit citations at any time as needed and use more confidential terms.

Equally important is not reductive. In other words, please do not simplify people to one thing like skin color and libido. For example, the following can be considered a restore.

In this case, the problem is that "Albino patients" make a group of people into a medical condition. A more sensitive way to express this sentence is:

Finally, it is important to avoid generalization when discussing groups of people. Even if generalization seems to be "positive," this is true. For example,

Efficiency is a good thing. But unless you are investigating everyone from Germany, you can not know that all Germans are 'effective'.

On the contrary, the idea of ​​efficiency by the German Order is a fixed idea. Even positive stereotypes have problems. This is partly because they may use other negative stereotypes. However, this is also because they are comprehensively summarized, and these generalizations are usually simplified and inaccurate.

Another way to not distinguish between gender distinctions is to avoid using male pronouns, unless you point to people. You can use plural form to avoid male pronouns. For example, it is no exaggeration to say that any child going to school by bicycle is late today. But you can correct it and say: the child riding a bicycle to school is late today. Or you can say that all children going to school are late today without using pronouns at all. You can use him or her or her as long as you do not overuse them. For example, the sentence is as follows. If there are problems with the students, please stay in the hallway, not in front of the class.

Cultural prejudice is usually a way to observe and talk about negative groups. Prejudice usually has a way to sneak into our daily languages ​​under our consciousness. Culturally biased languages ​​may refer to one or more cultural identities, including race, gender, age, sexual orientation, and ability. There are other socio-cultural identities that may be the subject of biased words, but we focus on these five languages. Many biased languages ​​are based on stereotypes and myths that influence the words we use. Prejudice is neither intentional nor deliberate, but as we have already explained, we need to take responsibility for what we say, even if we do not "intend" a particular meaning. With thoughts and speech. I will explain the specific ways in which cultural prejudice appears in our language and how to be more conscious of prejudice.

The concept of "prejudice" is understood in several ways. In statistics, researchers think that datasets or samples are skewed if the dataset or sample is systematically different from the population it represents. As in everyday language, in ethics, decisions are usually considered biased if decisions do not treat people fairly. In either case, because prejudice includes partial or unilateral insight, people will make misunderstandings. Due to bias of the algorithm, for various reasons. First, the data used to train the machine learning model is usually incomplete or distorted. By representing or excluding certain marginalized groups or groups of societies, this "sampling error" leads to poorly calibrated products that are exacerbated rather than opposed to what is left behind.