“The World AI Creator Awards”were held last month, attracting entries from some 1,500 AI programmers around the world. After judges of the world’s first AI beauty pageant unveiled 10 finalists, Kenza Layli has been crowned the world’s first-ever Miss AI., a hijab-wearing avatar from Morocco.
“世界人工智慧創造者獎”在上個月舉行，吸引來自世界各地約 1,500 名人工智慧程式設計師的參賽作品。在全球首屆人工智慧選美大賽的評審公佈了 10 名決賽入圍者後， Kenza Layli被加冕為有史以來首位世界人工智慧小姐，她是一個來自摩洛哥並戴著頭巾的虛擬形象。
Competition organisers said that entrants would be judged on more than just their beauty. They would earn points for their creators’use of AI tools ,as well as their social media influence and have to answer questions like“if you could have one dream to make the world a better place what would it be?”
比賽主辦單位表示，評審參賽者的標準將不僅僅是她們的美貌。他們將因其創作者對人工智慧工具的使用以及社交媒體影響力而獲得積分，並且必須回答諸如「如果你有一個讓世界變得更美好的夢想，你會夢想什麼？」之類的問題。
“Winning Miss AI motivates me even more to continue my work in advancing AI technology,”Layli said in a video of the speech. As we move forward, I am committed to promoting diversity and inclusivity within the field, ensuring that everyone has a place in technological advancement.”
「贏得人工智慧小姐大賽更激勵我繼續推動人工智慧技術的工作，」Layli 在演講影片中說道。在我們前進的過程中，我致力於促進該領域的多樣性和包容性，確保每個人都在技術進步中佔有一席之地。
Most of the models on the“Miss AI”shortlist, Dr. Kerry McInerney said, are “very very light-skinned and most of them are still white women, still thin, still really not diverging very much from that norm.”
Kerry McInerney博士說，“人工智能小姐”入圍名單上的大多數模特“皮膚非常非常淺，絕大多數仍然是白人女性，仍然很瘦，仍然與標準沒有太大差異。”
Research has found that racial and gender biases ingrained within beauty standards also seep into programmes that use AI to generate images. Experts have also expressed concern about the implications of an AI beauty pageant, as stylized AI-generated images may further homogenize beauty standards.
研究發現，在美容標準中，根深蒂固的種族和性別偏見也滲透到使用人工智慧生成圖像的程式中，專家們也對人工智慧選美比賽的影響表示擔憂，因為人工智慧生成的風格化圖像可能會進一步使選美標準同質化。
💡重點單字片語
Unveil公佈、揭露
Crown(v.)為…加冠、加冕
Hijab頭巾
Avatar虛擬化身
Entrant新成員、參賽者、考生
Motivate激勵
Diversity多樣性、差異性
Inclusivity具有包容性
Norm 準則、規範
Ingrained根深蒂固的
Racial種族的
Gender bias性別偏見
Homogenize(v.)使均質
💡參考內容
