Let's explore the benefits and risks of AI, like its potential to solve problems but also create bias.

To provide an example of how AI can impact everyday life, let's see the following scenario:

The Biased Hiring Algorithm

Imagine a large corporation develops an AI-powered hiring algorithm to streamline their recruitment process. The AI is trained on historical hiring data, including resumes, interviews, and performance reviews of past employees. The goal is to predict the best candidates for future job openings based on patterns identified in this past data.

However, the historical data reflects existing societal biases and prejudices, even the standards, degrees and priorities for employees back then. For instance, if the company has historically hired more men for technical roles, the AI might learn to associate male candidates with technical competence. As a result, the algorithm might unintentionally favor male applicants for technical positions, even if equally qualified female candidates apply.

Impact:

• Perpetuating Gender Inequality: This scenario demonstrates how AI can perpetuate existing gender biases in the workplace, making it harder for women to break into certain fields.
• Unintentional Discrimination: The company might not have intended to discriminate against female candidates, but the AI, trained on biased data, inadvertently reproduces those biases.
• Lack of Fairness: The AI's decisions are not fair because they do not provide equal opportunities to all candidates based on their qualifications.

Reflection

This example opens the door for a fruitful discussion about the ethical implications of AI. You could explore:

Data Bias: How historical data can embed societal biases.
Algorithmic Transparency: The importance of understanding how AI systems make decisions.
Fairness and Accountability: Strategies for ensuring AI systems treat all individuals fairly and avoid discrimination.
Human Oversight: The role of human judgment in overseeing AI systems and mitigating their potential biases.

AI Hiring Bias Simulation:

To further engage your students into this example, there are some activities you can propose for a class group the following activity

Goal: To understand the potential biases in AI-powered hiring systems and explore strategies to mitigate them.

Materials:

• A large whiteboard or flip chart
• Markers or pens
• Post-it notes
• A timer

Procedure:

• Divide into teams: Create groups of 3-4 students.
• Assign roles:
  • Each team will need a:
  • Data Scientist: Responsible for creating the AI algorithm.
  • HR Manager: Responsible for reviewing and approving candidates.
  • Social Justice Advocate: Responsible for identifying and addressing potential biases.
• Provide historical data: Give each team a set of hypothetical resumes and interview transcripts (download 5 examples here) that reflect existing societal biases. For example, the resumes might highlight certain experiences or degrees that are traditionally associated with men, while the interviews might contain gendered language or stereotypes.
• Develop the AI algorithm: The data scientist on each team should create a simple algorithm (e.g., a decision tree or rule-based system -download example here-) to predict job suitability based on the provided data.
• Conduct hiring simulations: Teams should use their AI algorithms to evaluate a new set of resumes and interviews. The HR manager should then review the algorithm's recommendations and decide whether to hire the candidates.
• Reflect on biases: The social justice advocate should observe the hiring process and identify any potential biases in the algorithm's recommendations. They should discuss these biases with the team and brainstorm ways to mitigate them.
• Debrief and discuss: After the simulation, the entire class should discuss the following questions:
  • What biases were identified in the AI algorithms?
  • How did these biases affect the hiring decisions?
  • What strategies could be used to prevent AI from perpetuating biases?
  • How can we ensure that AI-powered hiring systems are fair and equitable?

Additional Notes:

- Consider providing teams with different sets of historical data to explore the impact of different biases (e.g., racial, age, disability).
- Encourage teams to be creative in their approaches to mitigating biases. They might consider using techniques like data augmentation, bias detection, or human-in-the-loop decision-making.
- Facilitate a discussion about the ethical implications of using AI in hiring decisions.