Successful deployment of artificial intelligence (AI) in various settings has led to numerous positive outcomes for individuals and society. However, AI systems have also been shown to harm parts of the population due to biased predictions. AI fairness focuses on mitigating such biases to ensure AI decision making is not discriminatory towards certain groups. We take a closer look at AI fairness and analyze how lack of AI fairness can lead to deepening of biases over time and act as a social stressor. More specifically, we discuss how biased models can lead to more negative real-world outcomes for certain groups, which may then become more prevalent by deploying new AI models trained on increasingly biased data, resulting in a feedback loop. If the issues persist, they could be reinforced by interactions with other risks and have severe implications on society in the form of social unrest. We examine current strategies for improving AI fairness, assess their limitations in terms of real-world deployment, and explore potential paths forward to ensure we reap AI's benefits without causing society's collapse.

翻译：人工智能（AI）在各领域的成功部署为个人和社会带来了诸多积极成果。然而，AI系统也因其存在偏见性预测而对部分人群造成损害。AI公平性研究致力于缓解此类偏见，确保AI决策不会对特定群体产生歧视性影响。我们深入剖析AI公平性问题，分析缺乏AI公平性如何导致偏见随时间加剧并成为社会压力源。具体而言，我们探讨了有偏模型如何对特定群体造成更严重的现实负面后果，而基于日益偏倚数据训练的新AI模型会加剧此类后果，形成反馈循环。若这些问题持续存在，其可能通过与其他风险的交互作用被强化，最终以社会动荡的形式对社会产生深远影响。我们审视了当前提升AI公平性的策略，评估其在实际部署中的局限性，并探索确保我们既能享受AI红利又不会引发社会崩溃的可行路径。