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Harvard Case - Ubiquitous Surveillance (A)

"Ubiquitous Surveillance (A)" Harvard business case study is written by Mary Gentile, David Danks, Maralee Harrell. It deals with the challenges in the field of Organizational Behavior. The case study is 4 page(s) long and it was first published on : Jul 7, 2022

At Fern Fort University, we recommend a multi-pronged approach to address the ethical concerns and potential risks associated with the widespread implementation of facial recognition technology on campus. This approach prioritizes transparency, student consent, and data privacy while balancing security needs and fostering a positive and inclusive campus environment.

2. Background

Fern Fort University, a prestigious institution with a strong commitment to academic excellence and student well-being, is grappling with the ethical implications of implementing facial recognition technology for campus security. While the technology promises enhanced safety and efficiency, concerns about privacy violations, potential bias, and the impact on student trust and freedom have emerged. The case study highlights the complex interplay between security concerns, ethical considerations, and the need to maintain a welcoming and inclusive campus environment.

The main protagonists in this case study are the university administration, led by President Emily Carter, who champions the use of facial recognition technology for enhanced security, and the student body, represented by the Student Government Association (SGA), who express concerns about potential privacy violations and the impact on individual freedoms.

3. Analysis of the Case Study

This case study can be analyzed through the lens of Organizational Behavior, focusing on the interplay of Leadership styles, Organizational culture, Team dynamics, and Change management.

Leadership Styles: President Carter's leadership style leans towards a directive approach, prioritizing security and efficiency. However, this approach lacks emotional intelligence and fails to consider the broader implications of the technology on the campus community. The SGA, on the other hand, exhibits transformational leadership, advocating for student voices and promoting ethical considerations.

Organizational Culture: The university's existing organizational culture emphasizes academic excellence and a strong sense of community. However, the implementation of facial recognition technology has the potential to erode trust and create a sense of surveillance, potentially impacting employee engagement and job satisfaction.

Team Dynamics: The case study highlights the lack of effective communication patterns and collaboration between the administration and the student body. This lack of communication leads to misunderstandings and exacerbates the conflict.

Change Management: The university's approach to implementing the technology lacks a comprehensive change management strategy. This failure to address potential resistance and engage stakeholders has created a sense of unease and mistrust.

4. Recommendations

  1. Establish a Transparent and Inclusive Decision-Making Process: The university should create a task force comprised of representatives from the administration, faculty, staff, and student body to engage in a transparent and inclusive dialogue about the use of facial recognition technology. This task force should conduct thorough research on the ethical implications, potential biases, and legal considerations associated with the technology.

  2. Prioritize Student Consent and Data Privacy: The university should prioritize student consent and data privacy by implementing strict protocols for data collection, storage, and usage. This includes clearly communicating the purpose of the technology, obtaining informed consent from students, and limiting data retention periods.

  3. Implement Robust Security Measures: The university should implement robust security measures to protect student data from unauthorized access, breaches, and misuse. This includes encryption, access control, and regular security audits.

  4. Develop a Comprehensive Communication Strategy: The university should develop a comprehensive communication strategy to inform the campus community about the technology, address concerns, and foster open dialogue. This strategy should involve regular updates, Q&A sessions, and opportunities for feedback.

  5. Explore Alternative Technologies: The university should explore alternative technologies that offer similar security benefits without compromising privacy. This includes access control systems, video surveillance, and behavioral analytics that do not rely on facial recognition.

  6. Invest in Training and Education: The university should invest in training and education for faculty, staff, and students on the ethical implications, potential biases, and responsible use of facial recognition technology. This will help foster a greater understanding of the technology and its potential impact.

5. Basis of Recommendations

These recommendations are based on the following considerations:

  1. Core competencies and consistency with mission: The recommendations align with the university's commitment to academic excellence, student well-being, and fostering a welcoming and inclusive environment.

  2. External customers and internal clients: The recommendations prioritize the needs and concerns of both external stakeholders, such as prospective students and parents, and internal stakeholders, including faculty, staff, and students.

  3. Competitors: The recommendations consider the ethical implications of facial recognition technology in higher education and aim to position the university as a leader in responsible technology adoption.

  4. Attractiveness - quantitative measures: While the recommendations do not directly address quantitative measures, they aim to create a positive and secure environment that enhances student satisfaction, retention, and overall campus well-being.

  5. Assumptions: The recommendations assume a willingness from the university administration to engage in transparent and inclusive dialogue, prioritize student consent and data privacy, and invest in the necessary resources to implement these recommendations.

6. Conclusion

By adopting a transparent, inclusive, and data-driven approach to the implementation of facial recognition technology, Fern Fort University can balance security needs with ethical considerations, fostering a welcoming and inclusive campus environment that respects student privacy and promotes trust.

7. Discussion

Alternatives not selected:

  • Complete rejection of facial recognition technology: This option would address privacy concerns but might compromise security.
  • Unilateral implementation of facial recognition technology without student input: This option would prioritize security but could erode trust and create a hostile environment.

Risks and key assumptions:

  • Resistance from the administration: Implementing these recommendations requires a shift in leadership style and a commitment to transparency and inclusivity.
  • Cost of implementation: Implementing robust security measures and alternative technologies requires significant financial investment.
  • Technological advancements: The rapid evolution of facial recognition technology might necessitate ongoing reevaluation and adjustments to the university's policies.

8. Next Steps

  1. Form a task force: Within one month, establish a task force to engage in a comprehensive review of facial recognition technology.
  2. Conduct research and gather feedback: Within three months, conduct research on the ethical implications, potential biases, and legal considerations of facial recognition technology and gather feedback from stakeholders.
  3. Develop a communication strategy: Within six months, develop and implement a comprehensive communication strategy to inform the campus community about the technology and address concerns.
  4. Pilot program: Within nine months, implement a pilot program to test alternative technologies and gather data on effectiveness and impact.
  5. Final decision: Within twelve months, based on the findings of the task force, research, feedback, and pilot program, make a final decision on the use of facial recognition technology.

By following these steps, Fern Fort University can navigate the complex ethical and practical considerations surrounding facial recognition technology, ensuring a safe, inclusive, and respectful environment for all members of the campus community.

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Case Description

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.

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