Ethical & Data Related Concerns
Privacy
Data left insecure can lead to breaches, which raises privacy concerns. This data can hold private personal data, which can be very sensitive. Strong security features, such as encryption, can reduce privacy concerns. Offering informed consent to those whose data is being used can also reduce concerns.
Accountability
Using AI to make decisions can blur the lines of accountability. This decrease in accountability can lead to project failures, without knowing who to blame. To increase accountability, humans need to be involved. To use AI effectively in project management, it must be used as an assistant, and not as a supervisor or decision maker.
Bias & Quality
There can be high or low quality data within an AI's dataset. Low quality data can introduce or increase bias within a data set. Bias can include factors such as race, gender, socioeconomic status, and even previous project successes and/or failures (Schmelzer & Walch, 2025). To reduce bias and increase quality of the data used by AI, it should be reviewed and audited frequently. This ensures it remains high-quality and contains limited biases.
Transparency
Transparency in AI is understanding how it operates and comes to its conclusions. Reduced transparency can lead to decreased trust in the AI and leadership. Reduced transparency can also lead to poor decision making. An AI that explains its conclusions, and contains an open database, can help increase transparency.
Impact on Project Management
Poor data can lead to inaccurate results, which can lead to poor project outcomes. Reduced ethical considerations can lead to potential data breaches, which can end up causing financial and reputational loses. It is critical that a human is involved in all aspects of project management, and to not allow AI to make important decisions.
Brandon Witham