عنوان

Taraflılığın tespiti için bir dil modeli yaklaşımı

شماره

TLpq2522841701

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Taraflılığın tespiti için bir dil modeli yaklaşımı

نام عام مواد

[Thesis]

نام نخستين پديدآور

Atik, Ceren

نام ساير پديدآوران

Tekir, Selma

نام ناشر، پخش کننده و غيره

Izmir Institute of Technology (Turkey)

تاریخ نشرو بخش و غیره

2020

نام خاص و کميت اثر

55

جزئيات پايان نامه و نوع درجه آن

Master's

کسي که مدرک را اعطا کرده

Izmir Institute of Technology (Turkey)

امتياز متن

2020

متن يادداشت

Technology is developing day by day and is involved in every area of our lives. Technological innovations such as artificial intelligence can strengthen social biases that already exist in society, regardless of the developers' intentions. Therefore, researchers should be aware of this ethical issue. In this thesis, the effect of gender bias, which is one of the social biases, on occupation classification is investigated. For this, a new dataset was created by collecting obituaries from the New York Times website and they were handled in two different versions, with and without gender indicators. Since occupation and gender are independent variables, gender indicators should not have an impact on the occupation prediction of models. In this context, in order to investigate gender bias on occupation estimation, a model in which occupation and gender are learned together is evaluated as well as models that make only occupation classification are evaluated. The results obtained from models state that gender bias has a role in classification occupation.

موضوع مستند نشده

Artificial intelligence

موضوع مستند نشده

Bias

موضوع مستند نشده

Computer engineering

مستند نام اشخاص تاييد نشده

Atik, Ceren

مستند نام اشخاص تاييد نشده

Tekir, Selma

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y