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This is a Portuguese translation of the July 8, 2022 version of UVA-OB-1404. This case set is part of the Giving Voice to Values (GVV) curriculum. To see other material in the GVV curriculum, please visit http://store.darden.virginia.edu/giving-voice-to-values. In the A case, Tasha, leader of a data analytics team at Gotham Children’s Hospital, is confronted with an ethical dilemma when her supervisor, Beatriz, suggests a facial recognition system at the hospital. The proposed project would draw on images in the hospital’s database, as well publicly available images and information about arrest records, to identify visitors and trigger alarms when flagged individuals enter certain areas of the hospital. While her team is interested in the project, Tasha is concerned about privacy, bias in automated systems, public relations, and opportunity costs. In this B case, Tasha brings her concerns to Beatriz and outlines the ways in which the project would conflict with the core mission of the organization. This case set is intended to be used with students from a wide range of backgrounds, including especially team leads (who may have an MBA or other graduate-level degree) and data scientists. It would be particularly useful in courses on data ethics, technological innovation, technology management, and data law or policy.

This is a Portuguese translation of the July 8, 2022 version of UVA-OB-1403. This case set is part of the Giving Voice to Values (GVV) curriculum. To see other material in the GVV curriculum, please visit http://store.darden.virginia.edu/giving-voice-to-values. In this A case, Tasha, leader of a data analytics team at Gotham Children’s Hospital, is confronted with an ethical dilemma when her supervisor, Beatriz, suggests a facial recognition system at the hospital. The proposed project would draw on images in the hospital’s database, as well publicly available images and information about arrest records, to identify visitors and trigger alarms when flagged individuals enter certain areas of the hospital. While her team is interested in the project, Tasha is concerned about privacy, bias in automated systems, public relations, and opportunity costs. This case set is intended to be used with students from a wide range of backgrounds, including especially team leads (who may have an MBA or other graduate-level degree) and data scientists. It would be particularly useful in courses on data ethics, technological innovation, technology management, and data law or policy.

This is a Portuguese translation of the December 9, 2020 version of UVA-M-0987. Arredondar was a nongovernmental organization (NGO) whose objective was encouraging the culture of donation in Brazil through acts of micro-donations. Any shopper could donate at the retail stores that partnered with Arredondar. The process was simple: customers could round up the total value of their purchase to the next whole value, and the difference in cents would be donated to carefully selected NGOs. Arredondar offered a simple, accessible, and transparent way to donate, which seemed compatible with the characteristics of a country in which the donation culture was not yet strong, and where there was distrust of NGOs. However, there were indications that despite awards and success, the project might not be able to achieve financial sustainability. The case focuses on a common dilemma of social enterprises: how to be financially sustainable while creating a strong social impact. Although Arredondar had already partnered with large retailers, it needed to rethink its operation in order to break even. Arredondar’s leadership considered the following strategies: (1) focus on the core through existing retail partners; (2) expand toward new retailers; (3) implement new ways for consumers to donate the money other than through cashiers at partnering retailers; or (4) increase the administrative fee. Would one of these four options help Arredondar be financially sustainable?

This is a Portuguese translation of the August 11, 2021 version of UVA-F-1988. This case explores the dynamic allocation approach to investing, an approach that would time the market based on factors’ relative valuations. Research Affiliates created a RAFI Dynamic Multifactor US index that dynamically allocated five factor strategies: value, quality, momentum, size, and low volatility. In this course, students will utilize various information presented in the case to discuss why Research Affiliates decided to launch the strategy, how Research Affiliates times it, and what has been its performance relative to the Russell 1000 Index and other competitors. Students will also assess the strategy’s performance and some of the limitations of its dynamic strategy.