美国联邦贸易委员会正在制定关于"个性化定价"的执法政策，以应对企业利用人工智能算法根据个人数据预测消费者和员工支付意愿的风险1。这一举措反映出监管部门对企业通过算法获得信息优势、同时对消费者保持不透明的担忧1。
企业对消费者和员工的差异化定价已有实际案例。打车平台Lyft已有文件证实了其向不同司机提供个性化收入挑战的系统1，英国研究发现Uber的动态定价与司机时薪下降和收入不平等相关1。监管机构的调查发现，定价中介可以访问位置、人口统计、浏览历史、购物车活动甚至鼠标移动等详细用户信息1。在零售领域，新西兰消费者权益组织Consumer NZ警告，超市忠诚度计划收集的大量数据可能被用于推断个人支付意愿1。
The U.S. Federal Trade Commission is developing enforcement policies on "personalized pricing," addressing concerns about companies using artificial intelligence algorithms to predict what individual consumers and employees are willing to pay based on personal data.1 This approach enables businesses to learn consumers' and workers' financial constraints while keeping their own pricing strategies opaque, potentially creating asymmetrical information advantages reminiscent of "digital feudalism."1
Ride-sharing platforms have already implemented such systems at scale. Lyft has documented evidence of a system that offers personalized income challenges to different drivers,1 while research in the United Kingdom found that Uber's dynamic pricing correlates with declining hourly wages and increased income inequality among drivers.1 The FTC's investigation revealed that pricing intermediaries have access to extensive data including location, demographics, browsing history, shopping cart activity, and even mouse movements.1 Beyond transportation, supermarket loyalty programs are accumulating large datasets that could be used to infer individual payment willingness, according to warnings from Consumer NZ.1
评论
还没有评论，欢迎留下第一条。