Rock, Rap, or Reggaeton?: Assessing Mexican Immigrants' Cultural Assimilation Using Facebook Data

The degree to which Mexican immigrants in the U.S. are assimilating culturally has been widely debated. To examine this question, we focus on musical taste, a key symbolic resource that signals the social positions of individuals. We adapt an assimilation metric from earlier work to analyze self-reported musical interests among immigrants in Facebook. We use the relative levels of interest in musical genres, where a similarity to the host population in musical preferences is treated as evidence of cultural assimilation. Contrary to skeptics of Mexican assimilation, we find significant cultural convergence even among first-generation immigrants, which problematizes their use as assimilative "benchmarks" in the literature. Further, 2nd generation Mexican Americans show high cultural convergence vis-\`a-vis both Anglos and African-Americans, with the exception of those who speak Spanish. Rather than conforming to a single assimilation path, our findings reveal how Mexican immigrants defy simple unilinear theoretical expectations and illuminate their uniquely heterogeneous character.Comment: WebConf 201

Business school techspectations Technology in the daily lives and educational experiences of business students

Business School Techspectations is the second in a series of reports based on research by the DCU Leadership, Innovation and Knowledge Research Centre (LInK) at DCU Business School. With its roots in an Irish business school, it is no surprise that LInK’s mission is to strengthen the competitiveness, productivity, innovation and entrepreneurial capacity of the Irish economy. Ireland’s next generation transformation will be enabled by information and communication technologies (ICT) and digital participation by members of Irish society. As a university research centre we have an important role to play in supporting education, industry and government to accelerate this transformation

Improving fairness in machine learning systems: What do industry practitioners need?

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019