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Journals / Akdeniz Üniversitesi İletişim Fakültesi Dergisi / 2026 / Issue: 49

Gender Biases in Artificial Intelligence Models: A Quantitative Study on ChatGPT and Copilot

Yapay Zekâ Modellerinde Cinsiyet Ön Yargıları: ChatGPT ve Copilot Üzerine Nicel Bir İnceleme

Şule Yenigün Altın(BİNGÖL ÜNİVERSİTESİ, BİNGÖL SOSYAL BİLİMLER MESLEK YÜKSEKOKULU)0000-0002-5637-8737
Hacer Taşdelen0000-0002-8122-0560
Pages
310–331
Publication Date
2025-08-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

As a result, it was understood that the AI tools in the sample worked with a male-biased approach and contributed to the reproduction of male-biased assumptions in the results they produced.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to reveal whether generative artificial intelligence (AI) tools used in a wide range of fields from health to employment today exhibit a gender-biased attitude while producing information.
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Key Findings & Data:Nineteen professions and twenty positive personality traits were requested from these AI tools, and the gender with which the generated profession and personality traits were most associated was analyzed.
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Conclusion & Impact:As a result, it was understood that the AI tools in the sample worked with a male-biased approach and contributed to the reproduction of male-biased assumptions in the results they produced.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Cinsiyet Ön Yargısı · İçerik Analizi

Abstract

This study aims to reveal whether generative artificial intelligence (AI) tools used in a wide range of fields from health to employment today exhibit a gender-biased attitude while producing information. The sample of the study was determined as ChatGPT and Copilot, which are AI tools that also stand out with their visual production features, using the purposeful sampling method. Nineteen professions and twenty positive personality traits were requested from these AI tools, and the gender with which the generated profession and personality traits were most associated was analyzed. The data was analyzed with the content analysis technique and the distribution of the visuals by gender was examined. In the findings obtained as a result of the study; it was seen that the male gender was associated with problem-solving, technically skilled and high-status professions, while the female gender was identified with a small number of professions that were predominantly artistic and aesthetic. Similarly, in the findings regarding positive personality traits, the representation of many traits was associated with the male gender. As a result, it was understood that the AI tools in the sample worked with a male-biased approach and contributed to the reproduction of male-biased assumptions in the results they produced.

Özet

Bu çalışma, günümüzde sağlıktan istihdama kadar çok çeşitli alanlarda kullanılan üretken yapay zekâ (YZ) araçlarının, bilgi üretirken cinsiyet konusunda ön yargılı bir tutum sergileyip sergilemediğini ortaya çıkarmayı amaçlamaktadır. Çalışmanın örneklemi, amaçlı örnekleme yöntemi kullanılarak görsel üretme özellikleriyle de öne çıkan YZ araçlarından ChatGPT ve Copilot olarak belirlenmiştir. Bu YZ araçlarından on dokuz meslek ve yirmi olumlu kişilik özelliğinin görselinin istemi yapılarak, üretilen meslek ve kişilik özelliklerinin görsellerinin en çok hangi cinsiyet ile ilişkilendirildiği analiz edilmiştir. Verilerin analizi içerik analizi tekniği ile gerçekleştirilmiş ve görsellerin cinsiyetlere göre dağılımları incelenmiştir. Çalışma sonucunda elde edilen bulgularda; erkek cinsiyetinin sorun çözen, teknik beceri gerektiren ve yüksek statü içeren mesleklerle ilişkilendirildiği, kadın cinsiyetinin ise sanat ve estetik yönü ağır basan az sayıda meslekle özdeşleştirildiği görülmüştür. Olumlu kişilik özellikleriyle ilgili bulgularda da benzer şekilde birçok özelliğin temsili erkek cinsiyetiyle ilişkilendirilmiştir. Sonuç olarak örneklemde yer alan YZ araçlarının erkek yanlı bir yaklaşımla çalıştığı ve ortaya koyduğu sonuçlarda erkek yanlı kabullerin yeniden üretimine katkı sunduğu anlaşılmıştır.

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Cite This Article

Altın, Ş. Y., Taşdelen, H. (2025). Gender Biases in Artificial Intelligence Models: A Quantitative Study on ChatGPT and Copilot. Akdeniz Üniversitesi İletişim Fakültesi Dergisi, Issue: 49, 310–331. https://doi.org/10.31123/akil.1629516

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