Categories: AI
As generative AI (Gen AI) becomes increasingly embedded in project management processes, organizations must develop a clear and responsible framework for its use. Gen AI, such as ChatGPT, can create schedules, draft procurement strategies, assess risks, and even suggest team configurations. However, employees require guidelines to reduce risks that may undermine both project and organizational integrity. Adhering to ethical considerations when interacting with Gen AI requires a framework that aligns with the organization's ethical policies, reflecting its values, regulatory requirements, and fairness. Here are three components to a good framework.
- Governance. Governance is the foundation of an ethical Gen AI framework. It ensures that organizational use aligns with internal values, external regulations, and changing technology landscapes. Without strong governance, ethical breaches may occur without accountability or oversight.
Organization Responsibility:
- Develop formal policies on AI usage
- Monitor evolving legal and ethical standards
- Establish audits and regular reviews
- Communicate responsibly and clearly
- Training. Employees engaging with Gen AI must be equipped to use it responsibly. This includes understanding how prompts influence outcomes and ensuring proper validation and attribution of AI-generated content. Potential issues include unclear or manipulative prompts, failure to fact-check AI-generated output, ignoring attribution norms, and making poor decisions based on unverified content. Employees require training on ethical challenges to ensure they are accountable for their interactions with Gen AI.
Training Objectives:
- Use transparent and honest prompts. Avoid prompts that intentionally mislead the system to generate biased or overly persuasive content
- Verify and validate outputs. Don’t assume Gen AI responses are accurate. Always ask for the sources and cross-check facts, numbers, and claims with trusted sources
- Respect attribution. If Gen AI uses or summarizes identifiable content, cite or acknowledge it appropriately
- Recognize limitations. Understand what Gen AI can and cannot do. Avoid delegating critical decisions solely to the tool
- Seek feedback. Share your Gen AI-generated work with peers or supervisors before taking action based on it
- Data Management. Gen AI results are based on the quality of data it can access. Datasets may be incomplete, inaccurate, or biased. Access may be given to confidential information or contain personal data without informed consent. Data ownership may be unclear, and sharing it could violate ethics policies.
Data Imperatives:
- Consistently evaluate data for credibility and completeness
- Differentiate between access to internal data and external data, identifying risk for each
- Determine access restrictions for privacy, security, and the potential for misuse
The consequences of ethical violations when using Gen AI can be significant (Hagerty & Rubinov, 2019). Poor decisions and breaches of ethical policy can not only have serious repercussions but may go unnoticed for some time. Accountability is necessary from all parts of the organization involved. Employees must acknowledge their use of Gen AI and understand the associated level of risk. Ethical lapses in the use of Gen AI can also have cascading effects, from poor project outcomes to reputational damage. Accountability must be built into every layer: policy, practice, and personnel. Organizations should not only empower employees to utilize Gen AI but also equip them to be stewards of its ethical application.
The technology offers incredible opportunities for organizational efficiency, which must be balanced by defining and implementing a framework that upholds ethical standards.
Is your organization ready to integrate Gen AI responsibly?
Some references to check:
Dignum, V. (2019). Responsible artificial intelligence: How to develop and use AI in a responsible way. Springer.
Hagerty, A., & Rubinov, I. (2019). Global AI ethics: A review of the social impacts and ethical implications of artificial intelligence. AI & Society, 36(1), 55–66
