Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscrimination

Organizations cannot address demographic disparities that they cannot see. Recent research on machine learning and fairness has emphasized that awareness of sensitive attributes, such as race and sex, is critical to the development of interventions. However, on the ground, the existence of these data cannot be taken for granted. This paper uses the domains of employment, credit, and healthcare in the United States to surface conditions that have shaped the availability of sensitive attribute data. For each domain, we describe how and when private companies collect or infer sensitive attribute data for antidiscrimination purposes. An inconsistent story emerges: Some companies are required by law to collect sensitive attribute data, while others are prohibited from doing so. Still others, in the absence of legal mandates, have determined that collection and imputation of these data are appropriate to address disparities. This story has important implications for fairness research and its future applications. If companies that mediate access to life opportunities are unable or hesitant to collect or infer sensitive attribute data, then proposed techniques to detect and mitigate bias in machine learning models might never be implemented outside the lab. We conclude that today's legal requirements and corporate practices, while highly inconsistent across domains, offer lessons for how to approach the collection and inference of sensitive data in appropriate circumstances. We urge stakeholders, including machine learning practitioners, to actively help chart a path forward that takes both policy goals and technical needs into account

Spatial Data Analytics of Mobility with Consumer Data

Consumer data arising from the interaction between customers and service providers are becoming ubiquitous. These data are appealing for research because they are frequently collected and quickly released; they cover a wide variety of attitudes, lifestyles and behavioural characteristics; and they are often dynamically replenished and longitudinal. It is demonstrated that consumer data can make important contributions to understanding problems in transport geography and in solving applied problems ranging from migration, infrastructure investment and retail service provision to commuting and individual mobility. However more effective exploitation of these data depends on the construction of bridges to allow greater freedom in the transfer of data from the commercial to the academic sector; it requires development of frameworks for privacy and ethics in the secondary use of personal data; and it is contingent on the emergence of effective strategies for the amelioration of selection bias which impairs the quality of many consumer data sources

Interactively Building Agents

Integrating and mining data from different web sources can make end-users well-informed when they make decisions. One of many limitations that bars end-users from taking advantages of such process is the complexity in each of the steps required to gather, integrate, monitor, and mine data from different websites. We present the idea of combining the data integration, monitoring, and mining as one single process in the form of an intelligent assistant that guides end-users to specify their mining tasks by just answering questions. This easy-to-use approach, which trades off complexity in terms of available operations with the ease of use, has the ability to provide interesting insight into the data that would requires days of human effort to gather, combine, and mine manually from the web