不可取代代幣（NFT）市場中發現的性別和種族差異

在一項新研究中，卡內基美隆大學的研究人員研究了虛擬化身的性別和種族對不可替代代幣（NFT）市場交易價格的影響。

Researchers from Carnegie Mellon University, New York University, and Harvard University have conducted a new study examining the impact of avatars' gender and race on transaction prices in the non-fungible token (NFT) marketplace. Their findings, published in the International Journal of Research in Marketing, reveal pronounced gender and racial disparities in NFT prices, challenging the expectation that these disparities would be diminished in the emerging digital marketplace.

來自卡內基美隆大學、紐約大學和哈佛大學的研究人員進行了一項新研究，探討虛擬人物的性別和種族對不可替代代幣（NFT）市場交易價格的影響。他們的研究結果發表在《國際行銷研究期刊》上，揭示了 NFT 價格中明顯的性別和種族差異，挑戰了這些差異將在新興數位市場中縮小的預期。

The study, led by Yuan Yuan, a Ph.D. student in marketing at CMU, sought to investigate the presence of gender and racial biases in the NFT marketplace, given the younger, wealthier, and more politically progressive demographic of NFT investors. As the NFT market continues to grow and digital communities are increasingly leveraged as brand assets, understanding such biases becomes crucial.

這項研究由博士袁媛領導。鑑於 NFT 投資者更年輕、更富有、政治上更進步，卡內基美隆大學行銷專業的學生試圖調查 NFT 市場中是否存在性別和種族偏見。隨著 NFT 市場不斷增長，數位社群越來越多地被用作品牌資產，理解這種偏見變得至關重要。

In the NFT marketplace, avatar headshots represent individuals' digital identities. The researchers aimed to identify the effects of avatars' gender and race on NFT prices, how these effects varied over time, and what NFT providers could do to mitigate disparities.

在 NFT 市場中，頭像頭像代表了個人的數位身分。研究人員旨在確定化身的性別和種族對 NFT 價格的影響、這些影響如何隨時間變化，以及 NFT 提供者可以採取哪些措施來縮小差異。

Their analysis of CryptoPunks avatars, a popular NFT collection, revealed that female CryptoPunks were sold at a 37% lower price than their male counterparts (i.e., avatars with similar image attributes), while Black CryptoPunks were sold at a 31% lower price than their white counterparts. Furthermore, a controlled lab experiment causally confirmed both gender and race price premiums, as well as the presence of racial bias in these purchasing behaviors.

他們對流行的 NFT 系列 CryptoPunks 頭像的分析顯示，女性 CryptoPunk 的售價比男性同行（即具有相似圖像屬性的頭像）低 37%，而黑色 CryptoPunk 的售價比男性同行低 31%。此外，一項受控實驗室實驗證實了性別和種族價格溢價的因果關係，以及這些購買行為中存在種族偏見。

Interestingly, the study also found that certain attributes of the avatars, such as those linked to high-tech or higher education (e.g., 3D virtual reality headsets or nerd glasses), were associated with countering the disparities. Incorporating these attributes into avatars could potentially counteract bias in the NFT marketplace.

有趣的是，研究還發現化身的某些屬性，例如與高科技或高等教育相關的屬性（例如 3D 虛擬實境耳機或書呆子眼鏡），與消除差異有關。將這些屬性納入化身可能會抵消 NFT 市場中的偏見。

These findings challenge the notion that gender and racial biases are diminished in the emerging, technologically advanced marketplace. Instead, the study highlights deep-seated and persistent societal stereotypes and prejudices in the digital marketplace, particularly in the devaluation of Black avatars across demographics, showcasing the complexity of biases in digital marketplaces.

這些發現挑戰了性別和種族偏見在新興的、技術先進的市場中減少的觀念。相反，該研究強調了數位市場中根深蒂固且持續存在的社會刻板印象和偏見，特別是在人口統計中黑人化身的貶值，顯示了數位市場中偏見的複雜性。

"NFT creators and platforms can promote a more balanced marketplace by introducing attributes that counteract bias," said Kannan Srinivasan, professor of management, marketing, and business technology at CMU, who co-authored the study. "NFT platforms that want to harmonize efficiency with fairness can also adopt policies that discourage bias, providing more equitable guidelines for the creation and transaction of NFTs."

該研究的合著者、卡內基梅隆大學管理、營銷和商業技術教授 Kannan Srinivasan 表示：“NFT 創作者和平台可以通過引入抵消偏見的屬性來促進更加平衡的市場。” “想要協調效率與公平的 NFT 平台也可以採取消除偏見的政策，為 NFT 的創建和交易提供更公平的指導方針。”

The authors also acknowledged some limitations of their study, such as the difficulty in distinguishing between factors perceived as more valuable and those that are personally preferred, and the exclusive focus on human avatars. Additionally, they noted that the diversity of their database might not fully represent the general population.

作者也承認他們的研究存在一些局限性，例如難以區分被認為更有價值的因素和個人偏好的因素，以及僅關注人類化身。此外，他們指出，他們的資料庫的多樣性可能無法完全代表一般人群。