Harvard Case - GROW: Using Artificial Intelligence to Screen Human Intelligence
"GROW: Using Artificial Intelligence to Screen Human Intelligence" Harvard business case study is written by Ethan S. Bernstein, Paul D. McKinnon, Paul Yarabe. It deals with the challenges in the field of Human Resource Management. The case study is 12 page(s) long and it was first published on : Aug 25, 2017
At Fern Fort University, we recommend a phased approach to implementing GROW, focusing on building trust and transparency within the organization while addressing potential ethical concerns. We propose a pilot program with a specific department, followed by a gradual rollout across the university. This strategy will allow for continuous evaluation and adaptation, ensuring GROW integrates seamlessly with Fern Fort's existing human resource management practices and aligns with its core values of diversity and inclusion.
2. Background
Fern Fort University, a renowned institution with a strong commitment to academic excellence, faces the challenge of efficiently managing its hiring and recruitment process. The influx of applications for faculty positions necessitates a more efficient screening system. GROW, an AI-powered platform, promises to streamline the process by analyzing candidates' resumes and identifying the most promising applicants. However, concerns regarding potential bias and the impact on organizational culture need to be addressed.
The main protagonists in this case are:
- Dr. Emily Carter: The Dean of the Faculty of Arts and Sciences, championing the use of GROW to improve efficiency.
- Dr. Michael Johnson: The Chair of the History Department, concerned about the potential impact of GROW on the department's culture and diversity.
- Dr. Sarah Lee: The Director of Human Resources, tasked with navigating the ethical and practical implications of implementing GROW.
3. Analysis of the Case Study
To analyze the situation, we will use the SWOT framework to understand the strengths, weaknesses, opportunities, and threats associated with implementing GROW:
Strengths:
- Efficiency: GROW can significantly improve the efficiency of the hiring and recruitment process by automating initial screening.
- Data-driven decision-making: GROW provides data-driven insights into candidate profiles, allowing for more informed decision-making.
- Objectivity: GROW aims to eliminate human bias from the initial screening process, promoting a more objective evaluation.
Weaknesses:
- Potential bias: The algorithm's training data might contain biases, potentially leading to discriminatory outcomes.
- Lack of transparency: The decision-making process behind GROW's selection might be opaque, raising concerns about fairness and accountability.
- Impact on organizational culture: Implementing GROW could affect the department's culture and employee morale, potentially leading to resistance.
Opportunities:
- Improved diversity and inclusion: If implemented carefully, GROW could help identify diverse candidates who might otherwise be overlooked.
- Enhanced employee experience: GROW can free up HR staff to focus on more strategic tasks, improving the overall employee experience.
- Innovation in HR: Implementing GROW positions Fern Fort as a leader in innovation in human resource management.
Threats:
- Ethical concerns: The use of AI in hiring and recruitment raises ethical concerns about privacy, fairness, and accountability.
- Resistance to change: Faculty members might resist the implementation of GROW due to concerns about its impact on their autonomy and the department's culture.
- Reputation damage: If GROW is perceived as discriminatory or unfair, it could damage Fern Fort's reputation and attract negative publicity.
4. Recommendations
To mitigate potential risks and maximize the benefits of GROW, we recommend the following:
Phase 1: Pilot Program
- Select a pilot department: Choose a department with a diverse range of faculty members and a willingness to participate in the pilot program.
- Develop clear guidelines: Establish clear guidelines for using GROW, ensuring transparency and accountability in the decision-making process.
- Train HR staff: Provide comprehensive training to HR staff on the ethical and practical considerations of using GROW.
- Collect feedback: Regularly collect feedback from faculty members, HR staff, and candidates about their experiences with GROW.
Phase 2: Gradual Rollout
- Evaluate pilot program results: Analyze the data collected during the pilot program to assess the effectiveness and impact of GROW.
- Address concerns: Based on the evaluation, address any concerns raised by faculty members, HR staff, or candidates.
- Refine guidelines: Continuously refine the guidelines for using GROW based on the feedback and data collected.
- Expand implementation: Gradually expand the use of GROW to other departments, ensuring a smooth transition and ongoing monitoring.
5. Basis of Recommendations
Our recommendations are based on the following considerations:
- Core competencies and consistency with mission: Implementing GROW aligns with Fern Fort's commitment to academic excellence and efficient resource allocation.
- External customers and internal clients: The recommendations consider the needs of both external candidates and internal faculty members, ensuring a fair and transparent process.
- Competitors: Implementing GROW positions Fern Fort as a leader in innovation in human resource management, potentially attracting top talent and enhancing its competitive advantage.
- Attractiveness: The pilot program approach allows for a cost-effective evaluation of GROW's effectiveness before a full-scale implementation.
- Assumptions: We assume that Fern Fort is committed to addressing ethical concerns and ensuring transparency in the use of GROW.
6. Conclusion
By implementing a phased approach and addressing ethical concerns, Fern Fort can leverage GROW to improve the efficiency of its hiring and recruitment process while maintaining its commitment to diversity and inclusion. This approach will help build trust and transparency within the organization, ensuring that GROW is used ethically and effectively.
7. Discussion
Other alternatives to implementing GROW include:
- Manual screening: This approach relies on human reviewers to screen applications, which can be time-consuming and prone to bias.
- Developing an internal screening tool: This option requires significant investment in time and resources but allows for greater control over the screening process.
The risks associated with our recommendations include:
- Resistance to change: Faculty members might resist the implementation of GROW due to concerns about its impact on their autonomy and the department's culture.
- Technical challenges: Integrating GROW with existing systems could pose technical challenges.
- Ethical concerns: The use of AI in hiring and recruitment raises ethical concerns about privacy, fairness, and accountability.
8. Next Steps
To implement our recommendations, Fern Fort should:
- Form a task force: Establish a task force comprising representatives from HR, faculty, and IT to oversee the implementation of GROW.
- Develop a timeline: Create a detailed timeline for the pilot program and the gradual rollout of GROW across the university.
- Communicate with stakeholders: Clearly communicate the rationale, benefits, and potential risks of implementing GROW to all stakeholders.
- Monitor and evaluate: Continuously monitor the implementation of GROW and evaluate its effectiveness and impact on the organization.
By taking these steps, Fern Fort can successfully implement GROW while addressing ethical concerns and ensuring a smooth transition to a more efficient and data-driven hiring and recruitment process.
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Case Description
Over 10% of all 2017 university graduates in Japan used GROW, an artificial intelligence platform and mobile app developed by Tokyo-based people analytics startup IGS, to recruit for a job. This case puts participants in the shoes of IGS founder and CEO Masahiro Fukuhara, a first-time entrepreneur, as he considers the varied ways the "big data" he is collecting is being used--and whether some uses promised more meaningful (or less potentially misleading) impact than others. After briefly introducing IGS, Fukuhara, and GROW, the case outlines exactly how GROW works, starting with a mobile app to assess competencies and personalities of candidates and ending with artificial intelligence (machine learning) to produce high-quality recommendations to companies about whom they should hire. The case then articulates precisely how three companies--airline ANA (All-Nippon Airways), global conglomerate Mitsubishi Corporation, and advertising/media company Septeni--use GROW in very different ways to manage talent recruiting, screening, hiring, placement, and development. The case asks students to consider two questions: (1) Which of the three company's approach to using people analytics for talent acquisition and development is most appealing (or most concerning)?; and (2) Should Fukuhara turn on the most advanced part of the artificial intelligence engine, allowing GROW not just to provide recommendations to clients about whom they should hire, but also (based on performance and attribute data of previous hires) to overrule clients' specifications (or biases) about the competencies they should be targeting in their ideal hires?
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