The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

(Excerpt) Recently, leading technology companies such as Google and IBM have started experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the phenomenon of big data, in which analyses of huge sets of quantitative information are used to guide a variety of decisions. Applying big data to workplace situations could lead to more effective work outcomes, as in Moneyball, where the Oakland A\u27s baseball franchise used statistics to assemble a winning team on a shoestring budget. People analytics is the name given to this new approach to personnel management on a wider scale. Although people analytics is a nascent field, its implementation could transform the ways that employers approach HR decisions. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. In addition, people analytics could provide insights on more quotidian issues like location of the employee offices and use of break times. The data that drives these decisions may be collected in new ways: through the use of innovative computer games, software that monitors employee electronic communications and activities, and devices such as ID badges that record worker locations and the tone of conversations. Data may also be collected from sources outside the employer which have been gathered for different purposes, like real estate records, or for undefined purposes, like Google searches. While people analytics has great potential, no one has yet comprehensively analyzed the employment law or business ethics implications of these new technologies or practices. To date, most of the discussion centers on the uses for the data, not on its effects or its interactions with the law of the workplace. This Article seeks to survey these effects and interactions. Part I provides an overview, reviewing the history of employment testing, defining data mining, and describing the most current trends in people analytics. Part II describes the use of computer games and other technology to gather information. Part III examines the implications of people analytics on workplace privacy norms and laws. Part IV discusses the impact on equal-opportunity norms; while more and better information should lead to more merit-based decisions, disparate impact or unconscious bias could still operate to harm already-marginalized workers. Part V concludes with normative observations and preliminary policy notes. As the field of people analytics continues to develop, we must keep the values of employee voice, transparency, and autonomy as guiding principles

Applicant Selection to a Regional Medical Training Program: A Structural Analysis of Interviewer Assessments

Introduction: For regional campuses with specific program foci, assessing applicant fit necessarily extends beyond academic and professional factors. Based on assessments of applicants to a regional Rural Physician Leadership Program (RPLP), this study explores the relationship of academic and socio-demographic factors with interviewers’ ratings of: (1) likelihood of eventually practicing in a rural area of the state; and (2) overall acceptability to medical school. Methods: The study population consisted of 163 first-time RPLP applicants interviewed independently from 2009-2016 by two faculty members at both main and regional medical campuses. Path analysis was used to calculate direct, indirect, and total effects of applicants’ socio-demographic and academic characteristics on interviewers’ composite ratings. This study protocol (#17-0198-X3B) was approved as exempt by the governing Institutional Review Board; the authors report no conflicts of interest. Results: The combined influence of being an in-state resident with rural Appalachian origins, combined with undergraduate GPA, explained 40.7% of the variance in applicants’ predicted likelihood of practicing in rural Kentucky. In terms of applicant acceptability, the strongest direct effects were exerted by academic factors, GPA and total MCAT score, and the sole preceding endogenous variable: likelihood of rural in-state practice. However, two other background factors were modestly but significantly directly associated with overall acceptability: (1) age; and (2) residence. Specifying likelihood of rural practice as an intervening variable explained 42.5% of the variance in applicant acceptability and provided a good fit to the sample data (X2 = 3.19, df = 4, p = .526, CFI = 1.000, RLI = 1.018, RMSEA = .000). Conclusions: Interviewers appear to be assessing programmatic, mission-specific “fit” within the broader context of applicants’ abilities to navigate a demanding professional training curriculum. Future research should examine graduates’ eventual practice locations and intermediate academic performance as empirical validity of faculty interviewers’ assessments. Similarly, pre-professional pipeline efforts should better coordinate with training programs to provide consistent opportunities to nurture interest in mission-specific outcomes

Listeria Occurrence In Poultry Flocks: Detection and Potential Implications

Foodborne pathogens such as Salmonella, Campylobacter, Escherichia coli, and Listeria are a major concern within the food industry due to their pathogenic potential to cause infection. Of these, Listeria monocytogenes, possesses a high mortality rate (approximately 20%) and is considered one of the most dangerous foodborne pathogens. Although the usual reservoirs for Listeria transmission have been extensively studied, little is known about the relationship between Listeria and live poultry production. Sporadic and isolated cases of listeriosis have been attributed to poultry production and Listeria spp. have been isolated from all stages of poultry production and processing. Farm studies suggest that live birds may be an important vector and contributor to contamination of the processing environment and transmission of Listeria to consumers. Therefore, the purpose of this review is to highlight the occurrence, incidence, and potential systemic interactions of Listeria spp. with poultry

The potential of unmanned aerial systems for sea turtle research and conservation: A review and future directions

This is the final version. Available on open access from Inter Research via the DOI in this recordThe use of satellite systems and manned aircraft surveys for remote data collection has been shown to be transformative for sea turtle conservation and research by enabling the collection of data on turtles and their habitats over larger areas than can be achieved by surveys on foot or by boat. Unmanned aerial vehicles (UAVs) or drones are increasingly being adopted to gather data, at previously unprecedented spatial and temporal resolutions in diverse geographic locations. This easily accessible, low-cost tool is improving existing research methods and enabling novel approaches in marine turtle ecology and conservation. Here we review the diverse ways in which incorporating inexpensive UAVs may reduce costs and field time while improving safety and data quality and quantity over existing methods for studies on turtle nesting, at-sea distribution and behaviour surveys, as well as expanding into new avenues such as surveillance against illegal take. Furthermore, we highlight the impact that high-quality aerial imagery captured by UAVs can have for public outreach and engagement. This technology does not come without challenges. We discuss the potential constraints of these systems within the ethical and legal frameworks which researchers must operate and the difficulties that can result with regard to storage and analysis of large amounts of imagery. We then suggest areas where technological development could further expand the utility of UAVs as data-gathering tools; for example, functioning as downloading nodes for data collected by sensors placed on turtles. Development of methods for the use of UAVs in sea turtle research will serve as case studies for use with other marine and terrestrial taxa