Title: Consider | A 7-Step Approach for Ethical AI Decision-Making
Course: AI Literacy > Section: Module 2: Ethical Considerations in AI: Impact & Perspectives

Module 2 · Consider

A 7-Step Approach for Ethical AI Decision-Making

Navigating AI's ethical complexities means weighing each tool, system, or situation on its own terms, rather than applying one blanket response to all AI. This structured approach keeps ethical decision-making contextual, deliberate, and responsive to the specific challenges a given AI use raises.

Why Use an Ethical AI Decision-Making Framework?

Without a framework

- AI decisions may be rushed, inconsistent, or biased.
- Users may lack clear guidance on ethical dilemmas involving AI.
- AI tools may be adopted without weighing broader social and professional effects.

With a framework

- AI decisions are structured, transparent, and thoughtful.
- Users can critically assess AI tools for fairness, accuracy, and impact.
- AI integration balances innovation with ethical responsibility across settings.

Key Considerations: The 7 Steps

The following seven steps give you key considerations for evaluating AI's ethical implications:

- Identify the ethical dilemma. Clearly define the issue and its potential impact. Dilemmas often arise because AI affects multiple stakeholders in different ways.
- Gather information. Collect relevant data, stakeholder perspectives, and policies. What is known about the system's capabilities and limitations?
- Apply an ethics lens. Analyze the situation through key principles: privacy, fairness, transparency, accountability, and sustainability. Are there ethical trade-offs, and what are they?
- Evaluate options and trade-offs. Weigh the benefits and risks of different choices. Some prioritize efficiency over fairness, or accuracy over privacy. What balance makes the most sense?
- Make a decision and document it. Choose the most responsible course of action and record the rationale for accountability. Documentation supports transparency and helps others learn from past decisions.
- Implement and monitor. Deploy carefully, track the impact, and gather feedback from users and stakeholders. AI behavior can shift over time, so it needs ongoing oversight.
- Follow the AI ethics review cycle. Ethical AI is not a one-time decision. Regular audits, stakeholder input, and policy updates keep AI aligned with ethical standards as technology and expectations evolve.

See it in action: imagine your campus is considering AI proctoring software that watches students through their webcams during online exams. The framework would push you to name the dilemma (privacy and fairness versus exam security), gather evidence (does it flag some students unfairly, such as students of color or those whose disability affects how they move or look at the screen?), weigh trade-offs, document the decision, and keep reviewing it, rather than adopting the tool simply because it is available.

Check Your Understanding

Apply the framework

A campus team is deciding whether to adopt an AI tool that flags "at-risk" students from their data. According to this framework, what should they do first?

A) Roll it out quickly to see whether it works B) Clearly define the ethical issue and who it affects, then gather information C) Pick whichever option is most efficient D) Assume the AI is neutral and skip the ethics review

The next discussion board activity will have you apply this approach to an AI-related ethical dilemma.

Attribution: this 7-Step Ethical AI Decision-Making Framework is adapted from the work of Leo S. Lo. Used with permission.