Markovian discrimination

Okay, so have you ever played the game where you take turns guessing what the next letter will be in a word? Well, imagine you are playing this game with a friend, but instead of guessing letters, you are trying to guess what word your friend will say next.

Now, let’s say your friend only says words that start with the letter ‘B’. That means you can predict with pretty good accuracy that the next word will also start with ‘B’. This is called a Markov Chain, where the probability of each outcome (the next word) depends only on the previous outcome (the current word).

But what if your friend only says words that start with ‘B’ when they are talking to girls, and when they talk to boys, they only say words that start with ‘G’? That’s where Markovian discrimination comes in.

Markovian discrimination is when a system uses a Markov Chain to predict an outcome (like what word will be said next), but the probability of that outcome depends on the attributes of the person making the prediction (like their gender in our example). This can lead to unfair or discriminatory outcomes, because the predictions are based on stereotypes or bias.

So, even though you might be able to guess with pretty good accuracy what word your friend will say next based on their gender, it’s not fair to assume that all girls or all boys will act in a certain way, just like it’s not fair to assume that all people of a certain race or ethnicity will act in a certain way.

Instead, it’s important to treat each person as an individual and avoid using stereotypes or bias when making predictions or decisions.