1. Introduction

1.1 Purpose 

The purpose of this Guideline is to establish a clear and consistent framework for the responsible and ethical use of Artificial Intelligence (AI) tools – including generative, agentic, and any future iterations – by University staff in their professional capacity. It aims to empower staff to harness the potential benefits of AI for enhancing productivity, innovation, and efficiency in their work, while ensuring that potential risks – including but not limited to data privacy, intellectual property, and bias – are mitigated.  

By outlining acceptable use cases, providing practical guidance on data handling and transparency, and fostering a culture of informed adoption, this Guideline seeks to ensure that if used, AI is integrated into University operations in a manner that aligns with our institutional values for an AI-ready university and promotes a trustworthy and equitable environment for all members of our community. 

Core principles include: 

• Encouraging staff members to effectively harness Artificial Intelligence tools and technologies in a responsible way to enhance their efficiency, boost overall productivity, and foster a culture of innovation. 
• Mitigating potential risks associated with the implementation and use of Artificial Intelligence, with a specific focus on addressing concerns related to data privacy, algorithmic bias, and the security of sensitive information. 
• Ensuring that all applications and deployments of Artificial Intelligence are consistently aligned with the University’s overarching mission, guiding vision for the future, and principles set out in the recommendations from the Task Force on Artificial Intelligence. 
• Promoting transparency in how Artificial Intelligence is being used and establish clear lines of accountability for its development and deployment. 

1.2 Application/Scope 

This Guideline applies to all administrative staff members employed by the University, including employees represented and not represented by a union, and both casual and staff-appointed employees (i.e., all University employees, save and except faculty members and librarians. 

1.3 Definitions  

• Artificial Intelligence (AI): “…the capability of a computer to do things that are normally associated with human cognition, such as reasoning, learning, and self-improvement…”. This includes, but is not limited to: 
  • Machine Learning: The ability of computers to learn from data without explicit programming. 
  • Natural Language Processing (NLP): Technology that enables computers to understand, interpret, and generate human language. 
  • Generative AI: AI models that can create new content, such as text, images, and audio. 
  • Agentic AI: AI systems and models that are capable of carrying out tasks fully or semi-autonomously.  
• AI Tool: Any software, application, or system that uses AI technology. 
• University Data: Any recorded information that is created, received, maintained, or transmitted in support of University operations, research, teaching, or other activities. 
• Sensitive Data: A subset of University Data that requires protection due to its confidential nature. This includes, but is not limited to, personally identifiable information, health information, and financial account details. 
• Bias: Systematic errors or distortions in the output of an AI system which can result in outcomes that are discriminatory or reinforce existing inequities; this can occur due to human biases that skew the original training data or AI algorithms.
   

2. Principles for Responsible AI Use

The following principles guide the use of AI by administrative staff: 

• Responsibilities: Staff are accountable for the appropriate and ethical use of AI tools. This includes ensuring the accuracy and reliability of AI outputs and addressing any issues or errors that may arise. 
• Augmentation, Not Replacement: AI is a tool for augmentation rather than replacement, which can be used to leverage the strengths of both AI and employees. This approach fosters a collaborative environment where technology supports and enhances human capabilities, leading to improved outcomes and innovation. 
• Fairness and Non-Discrimination: AI systems should be used in a manner that promotes fairness, equity, and inclusivity. Staff must be aware of the potential for bias in AI algorithms and take steps to mitigate it. 
• Transparency and Documentation: The use of AI in decision-making processes should be transparent, ensuring that all applications and their purposes are well-documented. Note, effective January 1, 2026, the Working for Workers Four Act requires a statement disclosing the use of artificial intelligence on every publicly advertised job posting using artificial intelligence to screen, assess or select applicants for the position. 
• Privacy and Data Security: All use of AI must comply with relevant data privacy laws, the University’s Data Classification Standards, and the recommendations from the University’s Task Force on Artificial Intelligence. Staff must protect the confidentiality and security of University Data used in AI systems in alignment with Guidelines on using AI responsibly.  
• Legal and Ethical Compliance: All AI use must comply with applicable laws, regulations, and ethical standards. 
• Continuous Evaluation: The University will continuously evaluate the use of AI to ensure it aligns with the stated principles and goals. 
• Professional Development: Staff should seek out training and development opportunities to improve their understanding of AI and its appropriate use in their roles. 

Additional information can be found in the recommendation sections of the People Strategy & Administration Working Group Report and the AI Task Force Report.

 

3. Acceptable Use Cases

With appropriate vetting and verification of the results, AI tools can be used to support a variety of administrative functions. The following are some examples of acceptable use cases: 

• Automating Routine Tasks: 
  • Automating data entry, document processing, and other repetitive tasks to improve efficiency and reduce errors. Use of automation should be done in parallel to or by leveraging institutional platforms to avoid duplication of efforts to implement automated tools, workflows, and processes. 
  • Using chatbots that are designed for information retrieval to find basic information. 
• Improving Data Analysis and Reporting: 
  • Analyzing large datasets to identify trends, patterns, and insights that can inform decision-making (while ensuring compliance with institutional, provincial, and federal data security and privacy requirements)
  • Generating reports and visualizations using AI tools. 
• Enhancing Communication and Outreach: 
  • Creating preliminary draft communications. 
  • Editing communications and adjusting language for a specific audience 
  • Summarizing documents. 
  • Using AI to review the accessibility of written materials. 
• Supporting Decision-Making and Compliance: 
  • Reviewing form submissions such as applications for enrolment, benefits, and awards for completion and identifying gaps or incomplete submissions. 
• Streamlining HR Processes: 
  • Creating job descriptions that fit the University structure and format 
  • Automating employee onboarding tasks such as answering policy and guideline questions, generating onboarding portals and scenario analyses.

 

4. Prohibited Use Cases

The following uses of AI are prohibited: 

• Decisions Affecting Employment: Using AI as the sole basis for decisions related to employment such as hiring, promotions, or performance assessments. 
• Unauthorized Data Use: Using AI tools with University data in a way that violates data privacy laws or University policies. 
• Misrepresentation: Using AI-generated content and presenting it as original work without proper attribution. 

Responsible Use of Generative AI: 

• DO think about bias and ethical considerations. 
• DO check for hallucinations. 
• DO identify on a document or report when AI-generated content has been used and in what capacity. 
• DO NOT use in place of critical thinking. 
• DO NOT forget to check the source references and links as AI can produce and reference incorrect information. 
• DO NOT overlook the importance of documenting how AI-generated content has been applied – this can compromise transparency and trust.

 

Examples of When AI Can Be Used: 

• DO use to support the automation of repetitive or high–volume simple tasks. 
• DO use to summarize meeting notes. 
• DO use to collect information and analyze large data sets while protecting institutional data.
  
  
• DO NOT forget that producing the task is ultimately a human accountability and the work must be validated. 
• DO NOT forget to get consent from participants in a meeting if using an AI tool to record a meeting to create meeting notes. 
• DO NOT use for decision making. Use judgement to review and validate information.

 

5. Data Handling and Security

• Data Minimization: Only collect and use the minimum amount of University Data necessary for the specific AI application.

• Data Security: Implement appropriate security measures to protect University Data used in AI systems from unauthorized access, use, or disclosure.
   
• Data Privacy: Comply with all applicable data privacy laws and University policies, including those related to the collection, use, and disclosure of personal information. 
• Data Retention: Retain University Data used in AI systems only for as long as necessary to fulfill the intended purpose, in accordance with University record retention policies. 
• Data Sharing: Do not share University Data with third-party AI vendors or other external parties without proper divisional or institutional authorization and a written agreement that addresses data privacy and security. 
• Data Quality: Ensure the accuracy and reliability of University Data used in AI systems. 
• Employees should critically assess the information produced by AI systems, especially in tasks involving reports, data analysis, or customer service. AI can process and generate data quickly, but it may occasionally produce errors or biased results.  
• AI-generated information should be cross-referenced with trusted sources to help verify its accuracy. This practice ensures that the data and insights provided by AI are consistent with established facts and reliable information.

 

6. Transparency and Explainability

• Transparency in AI Use: Be transparent about when and how AI is being used in administrative processes. 
• Explainability of AI Outputs: To the extent possible, be able to explain how an AI system arrived at a particular output or recommendation, especially when the output affects individuals. 
• Documentation: Maintain clear documentation of the AI systems used, the data used to train them, and the processes involved in their development and deployment. 
• Communication: Communicate clearly with stakeholders, including students, faculty, and staff, about the use of AI and its potential impact on them.
  

 

7. Bias Mitigation

• Bias Awareness: Be aware of the potential for bias in AI algorithms and the data used to train them. 
• Data Diversity: Use diverse and representative datasets to train AI systems in order to minimize bias. 
• Bias Detection: Implement mechanisms to detect and monitor bias in AI systems. 
• Bias Correction: Take steps to correct or mitigate bias when it is detected. This may involve modifying the data, the algorithm, or the decision-making process. 
• Ongoing Monitoring: Continuously monitor AI systems for bias and evaluate their performance on different subgroups.

 

8. Procurement and Vendor Management

• Vendor Due Diligence: Require vendors to declare where AI is used in their solutions or services, and by assessing their compliance with regulations, ethical standards in AI design and use, and the integrity of any subcontractors before acquisition. 
• Contractual Agreements: Establish contractual safeguards by securing terms around data ownership, and intellectual property, permitted uses for AI, audit rights, and accountability for errors or breaches. 
• Data Management: Ensure that vendor practices on data collection, retention, storage, and cross-border transfers fully comply with university policies and legal requirements. 
• Vendor Oversight: Maintain ongoing monitoring and review by regularly evaluating AI system outcomes for accuracy, fairness, and unintended impacts, and by reviewing new features or updates to ensure continued compliance and alignment with organizational standards.

 

9. Implementation and Enforcement

• Training and Education: Provide training and education to administrative staff on AI literacy, these guidelines and the responsible use of AI. 
• Oversight and Monitoring: Establish a process for oversight and monitoring of AI use to ensure compliance with these guidelines. 
• Reporting Violations: Provide a mechanism for reporting suspected violations of these guidelines. 
• Enforcement: Enforce these guidelines through appropriate disciplinary measures, up to and including termination of employment.

 

10. Resources and Support

• Toward an AI-ready University 
• Vetted and supported tools & training –  
  • Microsoft Copilot Chat – Centre for Teaching Support & Innovation 
  • ChatGPT EDU – available through the Information Commons 
• Guidelines – 
  • Guidelines on using AI responsibly  
  • AI chatbot Microsoft Copilot Guidelines 
  • Guidance on the use of Generative AI for marketing and communications staff 
• Collected protocols –  
  • Using artificial intelligence to help manage your meeting minutes 
  • Generative AI tools and Copyright Considerations 
  • Report: Toward an AI-Ready University  

 

This guideline will be reviewed regularly to reflect evolving AI practices and standards.

This Guideline was last updated October 27, 2025.