Ethics and bias in AI in Intro to Computing - Flowchart & Logic Diagram

Process Overview

This flowchart explains how ethics and bias can affect AI systems and the steps taken to identify and reduce bias to ensure fair and responsible AI use.

Flowchart

Collect Data

Yes No↓

Train AI Model

↓

Evaluate Model

↓

Adjust Data or Model

↓

Retrain AI Model

↓

Evaluate Model

↓

Repeat Adjustment

This flowchart shows the step-by-step process of creating an AI system while checking for ethical concerns and bias. It starts with data collection, checks if the data is diverse, trains the AI, evaluates it for bias, and if bias is found, adjusts and retrains until bias is minimized before deployment.

Step-by-Step Trace - 10 Steps

Step 1: Collect data from various sources.

Step 2: Check if the data is diverse.

Step 3: Since data is not diverse, decide to improve it.

Step 4: Adjust data to include missing groups.

Step 5: Train AI model using improved data.

Step 6: Evaluate AI model for bias.

Step 7: Adjust model or data to reduce bias.

Step 8: Retrain AI model with adjustments.

Step 9: Re-evaluate AI model for bias.

Step 10: Deploy AI system for real-world use.

Diagram

 +-----------------+      +-----------------+      +-----------------+
 |                 |      |                 |      |                 |
 |   Raw Data      | ---> |  Data Cleaning  | ---> |  Balanced Data  |
 |                 |      |                 |      |                 |
 +-----------------+      +-----------------+      +-----------------+
          |                        |                        |
          v                        v                        v
 +-----------------+      +-----------------+      +-----------------+
 |                 |      |                 |      |                 |
 |  Train AI Model | ---> | Evaluate Bias   | ---> |  Bias? (Yes/No) |
 |                 |      |                 |      |                 |
 +-----------------+      +-----------------+      +-----------------+
                                                          |
                                                Yes <-----+-----> No
                                                          |           
                                                          v           
                                               +-----------------+  
                                               |                 |  
                                               | Adjust Data or   |  
                                               | Model & Retrain  |  
                                               |                 |  
                                               +-----------------+  

This diagram shows how raw data is cleaned and balanced before training the AI. After training, the AI is evaluated for bias. If bias is found, data or model adjustments are made and the AI is retrained until bias is minimized.

Flowchart Quiz - 3 Questions

Why is it important to check if the data is diverse before training an AI model?

A To reduce the size of the data

B To make the training faster

C To ensure the AI learns fairly about all groups

D To avoid using any data at all

Key Result

Ensuring AI fairness requires checking data diversity, detecting bias, and adjusting the model before deployment.