论文标题
人工智能偏见在乘车经济的价格歧视算法中的不同影响
Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms
论文作者
论文摘要
从个人收集移动性数据以告知智能城市规划的乘车应用程序可以通过依赖人工智能（AI）的自动化算法来预测每次旅行的票价定价。这种类型的AI算法，即价格歧视算法，被广泛用于行业的黑匣子系统中，用于动态的个性化定价。缺乏透明度，在没有使用用于生成价格歧视算法结果的数据的情况下，研究这种AI系统以实现公平性和不同的影响是不可能的。最近，为了提高城市规划的透明度，芝加哥市法规规定，运输提供商发布了有关乘车的匿名数据。结果，我们介绍了乘车应用程序使用的价格歧视算法的不同影响的第一个大规模测量。 荟萃分析文献中随机效应模型的应用结合了AI偏见对来自美国社区调查的人口普查属性票价定价的票价定价的效果。对来自芝加哥市的1亿个乘车样本的分析表明，由于与人口统计学属性相关的乘车利用率模式中学到的AI偏见，社区的票价价格产生了显着不同的影响。非白人人口较大的社区，较高的贫困水平，年轻居民和高等教育水平与较高的票价价格显着相关，效果大小，在科恩的D中衡量，分别为-0.32，-0.28、0.69和0.24，分别为每个人群。此外，我们的方法有望从包含美国地理位置的数据集中识别和解决AI算法学习中不同影响的来源。
Ridehailing applications that collect mobility data from individuals to inform smart city planning predict each trip's fare pricing with automated algorithms that rely on artificial intelligence (AI). This type of AI algorithm, namely a price discrimination algorithm, is widely used in the industry's black box systems for dynamic individualized pricing. Lacking transparency, studying such AI systems for fairness and disparate impact has not been possible without access to data used in generating the outcomes of price discrimination algorithms. Recently, in an effort to enhance transparency in city planning, the city of Chicago regulation mandated that transportation providers publish anonymized data on ridehailing. As a result, we present the first large-scale measurement of the disparate impact of price discrimination algorithms used by ridehailing applications. The application of random effects models from the meta-analysis literature combines the city-level effects of AI bias on fare pricing from census tract attributes, aggregated from the American Community Survey. An analysis of 100 million ridehailing samples from the city of Chicago indicates a significant disparate impact in fare pricing of neighborhoods due to AI bias learned from ridehailing utilization patterns associated with demographic attributes. Neighborhoods with larger non-white populations, higher poverty levels, younger residents, and high education levels are significantly associated with higher fare prices, with combined effect sizes, measured in Cohen's d, of -0.32, -0.28, 0.69, and 0.24 for each demographic, respectively. Further, our methods hold promise for identifying and addressing the sources of disparate impact in AI algorithms learning from datasets that contain U.S. geolocations.
