When implementing AI in project management, it is important to consider the ethical and security implications. According to a Harvard Business Review article, some of the best practices for building ethical AI include:
Identifying existing infrastructure that a data and AI ethics program can leverage.
Creating a data and AI ethical risk framework that is tailored to your industry.
Changing how you think about ethics by taking cues from the successes in health care.
Optimizing guidance and tools for product managers.
Building organizational awareness.
Formally and informally incentivizing employees to play a role in identifying AI ethical risks.
Monitoring impacts and engaging stakeholders 1.
Additionally, Microsoft has released an AI security risk assessment framework as a step to empower organizations to reliably audit, track, and improve the security of their AI systems 2. The framework involves participation from each stakeholder involved in building and red teaming models, including AI researchers, machine learning engineers, security architects, and security analysts 2.
It is important to note that these are just some of the considerations that must be taken into account when implementing AI in project management. The specifics will depend on the context of the project and the industry it is being implemented in. Saving Changes...
