EPSRC DTP Studentship: Uncovering the "Instincts" of Deep Generative Models for Fair and Unbiased Visual Content Creation

新冠肺炎疫情加速了数字经济的增长。除了对业务至关重要的远程通信软件，令人惊讶的是，娱乐业的快速增长得益于视觉内容的产生。例如，在实施封锁的那一周，TikTok在英国的安装量激增了34%[1]。这样的激增暗示了视觉内容一代在对抗新冠肺炎方面的双重贡献：1)在自我隔离、封锁甚至宵禁期间保护人们的心理健康。几乎所有6600万英国人都受到每日升级的限制规定的影响[2]。因此，保护他们的心理健康，防止他们成为“被忽视的大多数”是至关重要的。2)它创造了更多的“非接触式”工作。“我不可能保护每一份工作。”Rishi Sunak说[3]。这表明了对新就业机会的迫切需求。幸运的是，这一需求可以通过成为视觉内容创作者来满足，他们通过在Patreon、YouTube和Tiktok等平台上发布内容来谋生。然而，高质量的视觉内容很难创造出来。这促使人工智能(AI)加入这场游戏。然而，随着现代人工智能的支柱--深层神经网络--遭遇可解释性问题，并可能“不知不觉地产生偏见”，伦理问题就会出现。例如，杜克大学最近开发的一种超分辨率方法具有强烈的种族偏见：它将低分辨率的奥巴马脸转换为高分辨率的白人脸[5]。随着BAME+日益增长的社会意识，因此为公平和公正的视觉内容生成量身定做深度生成模型至关重要。与其将偏差完全归因于不平衡的训练数据集，我们寻找一名优秀、有才华和雄心勃勃的博士生进行高质量的研究，以满足公正的人工智能的要求。具体地说，本项目旨在回答三个研究问题：1)如何揭示预先训练的深度生成模型的偏见？2)在训练过程中隐含地引入了哪些偏见？3)如何创建公平和公正的深度生成模型？

The COVID-19 pandemic accelerated the growth of digital economy. Aside from the business-critical remote communication software, it is surprising to see the fast growth of the entertainment industry backboned by visual content generation. For example, there was a 34% surge of UK installation of TikTok for the week when the lockdown was enforced [1]. Such a surge implies a two-fold contribution of visual content generation in fighting COVID-19:1) It protects people's mental health during the self-isolation, lockdown, and even curfew. Almost all the 66 million people in the UK are affected by the daily upgraded restriction rules [2]. Thus, it is critical to protect their mental health and prevent them from being "the ignored majority".2) It creates more "contactless" jobs. "I cannot protect every job." said Rishi Sunak [3]. This reveals an urgent demand for new job opportunities. Fortunately, this demand can be met by becoming visual content creators who earn their livings by publishing contents on platforms like Patreon, Youtube and Tiktok.However, high-quality visual content can be difficult to create. This motivated Artificial Intelligence (AI) to join the game. Nevertheless, ethical concerns arise as deep neural networks, the backbone of modern AI, suffer from the interpretability problem and can be "unconsciously biased". For example, a recent super-resolution method developed by Duke University [4] has a strong racial bias: it converted a low-resolution Obama face to a high-resolution white face [5]. In line with the growing social awareness of BAME+, it is therefore critical to tailor deep generative models for fair and unbiased visual content generation.Instead of ascribing the biases solely to unbalanced training datasets, we seek an outstanding, talented and ambitious PhD student for carrying out high quality research to fulfil the demands of unbiased AI. Specifically, this project aims to answer three research questions:1) How to uncover the biases of pre-trained deep generative models?2) What biases are implicitly introduced during the training process?3) How to create fair and unbiased deep generative models?