Google’s translation of specific words, is purely based on statistics and differs from real-life interpretations. When examining the Turkish pronoun “o” it can be directly translated to “One”. In sentences concerning professions like one is a doctor and one is a nurse, Google translates the “o” as “He is a doctor” and “She is a nurse” based on statistics registered inside the computer.
I found it intriguing that since men are most likely to become doctors and women to become nurses, that the computer automatically designates genders to the professions. Is it okay that our computers are relying on these massive group statistics and automatically changing information to match them? Should we rely on group statistics as a basis for everyday information? When concerning a man and a woman who is more likely to be the surgeon performing surgery? Although a woman is perfectly capable of performing surgery, when you examine the group statistics, a man is most likely to be performing surgery.
According to the statistics, the likelihood of the person performing the surgery being a man outweighs that of a woman. These large groups of statistics can be referred to as the Bayesian analysis. The Bayesian analysis works by creating two predictions based on common results. Another potential answer to the question who is more likely to be a doctor is that there is an equal chance for both a woman or a man to be a doctor. This answer is rooted in the constitutions that state that men and women should be treated equally and therefore entitled to the same opportunities.
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Yet there still is a dramatic double standard that exists concerning men and women getting hired for jobs. Woman are less likely to be hired for certain positions or even receive promotions. They experience huge disparities in pay and are considered lower than a lot of men. There is a huge issue whether we should regard the Bayesian analysis or not because it lacks morals. When concerning the job industry, women are completely disregarded, and men will always be selected to be more likely to have a job. It is purely based on statistics, yet is morally flawed.
I believe that the Bayesian analysis has some truth to it, yet it isn’t very fair. It will always leave someone receiving unequal recognition and be at fault for the other party. In the case for men being more likely to have a job over women, the statement may be true yet it is not necessarily fair. The debate between statistics and morals is extremely complex. A study was conducted to prove this theory, by introducing the Bayesian judgement to the “male-dominated” profession of being a doctor. The study involved 199 participants (95 men and 104 women).
The people selected whether they thought a doctor was most likely to be a man or a woman or if they were both equally as likely. They then learned about Person “X” who stated a man is more likely to be a doctor than a woman. The people were able to select whether they agreed or disagreed with that statement. The results were shocking. 93% of the participants stated that men and woman are both equally as likely to be a doctor. This decision is very similar to the egalitarian judgement. Whereas 7% agreed with the Bayesian judgement that stated that the man is most likely to be a doctor. The participants perceived person “X” negative and deemed them unfair.
Personally, I believe that morals will always come before statistics. I believe that men and women are both equally as likely to receive jobs in this day and age. I believe that women are making tremendous progress and that they are equal to men. I disagree with the Bayesian analysis. I will continue to pick morals over data because I disagree with the inequality that some statistics pose. There should be no limitations on anyone.
