Applying NACE’s Principles for Ethical Professional Practice to AI

By the Principles for Ethical Professional Practice Committee

Introduction

Artificial intelligence (AI) and data-driven technologies have been integral to career services and recruitment for years. However, since the introduction of highly accessible AI tools in November 2022, their adoption has surged, generating both excitement and concern about their implications for the profession, the job market, and society at large. As AI evolves, it presents new opportunities and challenges spanning technical, legal, social, and economic dimensions. This resource focuses on ethical dimensions.

This resource is built on the premise that career services and recruiting professionals, alongside students and service providers, will continue to expand their use of AI-powered tools, even as some call for a pause in their development. NACE does not endorse specific AI tools or prescribe when they should be used in the profession. Rather, this document assumes that members have the agency to decide whether to use AI, have considered the inherent biases of these tools, and have ultimately chosen to proceed with their use. With a forward-thinking approach, the objective is to create a resource that provides an ethical framework for assessing AI’s role in our field rather than focusing on specific tools that may evolve or become obsolete.

Given the increasing integration of AI in career services and recruiting, it is essential to approach its use thoughtfully and responsibly. NACE’s Principles for Ethical Professional Practice offer a comprehensive foundation for ethical AI adoption, emphasizing transparency, fairness, security, and compliance. This guide applies these principles to AI use in career services and recruitment, providing actionable recommendations to ensure responsible implementation.

Ethical Application of AI in Career Services and Recruiting

Each of NACE’s five principles applies to the ethical use of AI in career services and recruitment. Below, we provide examples of how these principles can be effectively used. While this is not an exhaustive list, we encourage institutions and organizations to adopt these guidelines as appropriate.

1. Practice Reasonable, Responsible, and Transparent Behavior

• Inform students and candidates about AI usage: Clearly communicate how AI is used in career services and recruitment. Provide details on the tools used, their purpose, and their impact on decision-making.
• Publish an AI policy document: Develop a policy outlining AI tools' effectiveness, best practices, and appropriate use cases.
• Encourage student and candidate feedback: Create channels for students and candidates to share their experiences with AI tools. Use this feedback to refine practices, address concerns, and improve fairness.

2. Act Without Bias

• Select vendors committed to using diverse and representative data sets: Verify that they have processes to minimize bias. Perform regular bias audits: Conduct systematic reviews of AI systems and data sets to detect and mitigate biases. Monitor candidate pools and AI-generated feedback to identify disparities across groups.
• Implement feedback mechanisms: Establish structured feedback loops to capture and address bias-related issues. Continuously improve AI-driven recommendations based on user input.
• Consult external experts: Periodically engage with diversity, equity, and inclusion specialists to evaluate AI systems and recommend improvements, as resources are available or as appropriate.

3. Ensure Equitable Access

• Promote AI tools widely: Ensure all students and candidates are aware of available AI tools. If tools have premium versions, consider collaborating with campus offices like IT or libraries to provide access regardless of financial constraints.
• Select accessible AI tools: Choose AI solutions that meet accessibility standards to support students with disabilities and ensure inclusivity.

4. Comply With Laws

• Adhere to student privacy laws: Ensure AI tools comply with FERPA and other relevant regulations to safeguard student data.
• Require vendor compliance documentation: Obtain documentation from AI vendors certifying their adherence to local, state, and federal laws regarding data protection and employment practices.

5. Protect Confidentiality

• Ensure proper data handling: Implement data security measures, particularly when using cloud-based or third-party AI solutions.
• Require vendor security measures: Verify that vendors employ robust encryption and security protocols. Consider prohibiting unauthorized data sharing with third parties.
• Inform students and candidates: Clearly communicate data usage policies. For instance, consider providing a disclaimer such as: "Your data will only be shared with employers upon your explicit request during job application processes."
• Develop incident response plans: Create protocols for addressing potential data breaches involving AI systems, including notification procedures and remediation steps
• Refer to NACE resources: Leverage materials such as the Principles Committee’s Advisory Opinion: Managing Data Security with Technology Providers for best practices in AI-driven data security.

Career Centers' and Employers' Responsibilities

While AI is becoming increasingly integrated into career services operations and recruitment processes, individual career centers and employers should establish policies aligned with their institutional protocols regarding AI usage in career development and talent acquisition initiatives. In partnership with appropriate institutional stakeholders, clear guidelines should be developed to ensure AI is leveraged responsibly while maintaining ethical standards and effectiveness.

When incorporating AI into career services and recruitment, careful consideration is essential to balance technological advantages with ethical implications. Career services professionals should guide students and candidates to understand the limitations of AI tools and to fact-check AI-generated information and be aware of potential biases in AI systems. Additionally, implementing clear security protocols regarding personal data on AI platforms is crucial. Consider developing standardized language to remind users about best practices for data privacy.

While AI-powered tools can enhance efficiency in career services and recruitment processes, adoption should be strategic and aligned with organizational needs and leadership guidance. AI may be more suitable for certain applications than others. If permitted, specific use cases should be clearly communicated through official channels and marketing materials. For organizations with multiple career or recruitment offices, maintaining consistent AI policies will help set clear expectations.

Finally, it is encouraged that AI users engage in continuous training to remain current on relevant updates and how they might impact their work.

Developed by the 2024-25 Principles for Ethical Professional Practice Committee.