Incorporating AI into the selection process requires a thoughtful balance between technological advancements and the irreplaceable value of human judgment, insights, and ethical standards. Emphasizing human oversight as a foundational practice allows invested parties to harness AI's capabilities effectively, while ensuring that decisions are informed and aligned with the core values and objectives of the institution.
From principle to practice:
- Involve humans from end-to-end. To ensure effective implementation, integrate nontechnical subject matter experts at every stage, including problem formulation, data curation and relevance, feature engineering, error analysis, model evaluation, and user interface design.1 Leveraging domain expertise will help increase accurate and actionable outcomes, with experts in the loop to enhance understanding, align with goals, and manage risk.
- Use AI as decision support. Emphasize and safeguard the pivotal role of human judgment in the evaluation process. Ensure that AI and ML systems are deployed to complement and enhance, not replace, human decision-making capabilities. This approach helps maintain essential human qualities that are vital for nuanced and well-informed decision-making (e.g., empathy, teamwork, situational awareness).
- Develop understanding of AI. Provide training for administrators to expand their understanding of AI and ML. Focus training on basic understanding of the AI techniques used, as well as the critical integration of human judgment in interpreting outcomes, to enhance the effectiveness and sensitivity of the process. Explore the references below as a starting point for content and as a bridge to more technical collaborators such as data scientists or software engineers.
Source Cited
- Huyen C. The human side of machine learning. In: Designing Machine Learning Systems. O’Reilly Media; 2022:chap 11. Back to text ↑