Ethics in IT (ITE303c)
Vietnamese title: Đạo đức trong CNTT
Credits: 3

Description:
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.

Learning outcomes:
Understand the rise of algorithms, from the most basic to the fully autonomous, and discuss the trajectory of artificial intelligence and the broader implications for society as well
Explore fundamental issues of fairness and bias in machine learning and many aspects of building more ethical models
Explore fundamental concepts involved in security and privacy of machine learning projects and affects user privacy and transparency now and in the future while creating useful predictive models.
Able to apply bias and fairness analysis and skills in a real world or a fictional scenario

Student tasks:
Student must get the certification of Ethics in the Age of AI Specialization from Coursera to be accepted to the final examination

Tools:

Materials:
description: MOOC 1: Artificial Intelligence Algorithms Models and Limitations
publisher: Coursera
note: https://www.coursera.org/learn/ai-algorithm-limitations?specialization=ethics-in-ai
description: MOOC 2: Artificial Intelligence Data Fairness and Bias
publisher: Coursera
note: https://www.coursera.org/learn/ai-data-bias?specialization=ethics-in-ai
description: MOOC 3: Artificial Intelligence Privacy and Convenience
publisher: Coursera
note: https://www.coursera.org/learn/ai-privacy-and-convenience?specialization=ethics-in-ai
description: MOOC 4: Artificial Intelligence Ethics in Action
publisher: Coursera
note: https://www.coursera.org/learn/ai-ethics-analysis?specialization=ethics-in-ai
description: Help Articles
publisher: Coursera
note: https://www.coursera.support/s/article/208280036-Coursera-Code-of-Conduct?language=en_US
description: Ethics in the Age of AI Specialization
publisher: Coursera
note: https://www.coursera.org/specializations/ethics-in-ai

Assessments:
category: Final exam
type: Final exam
weight: 100.0
duration: 60'
no_question: Multiple choicesMarked by Computer
knowledge_and_skill: 50
grading_guide: All studied courses. Each module of course contributes 4-5 questions.
note: by exam board,
using computer

Schedule:
session: Specialization Introduction

Artificial Intelligence Algorithms Models and Limitations
- Define Algorithms and Identify how learning algorithms are used in everyday life
- Identify the basic mechanics of machine learning
- Observe how machine learning is used by companies
- Define Predictive Modeling
- Identify the goals in model accuracy
- Analyze limitations in predictive models
- Identify how models in production can hurt more than help
- Analyze how companies use predictive models
learning_outcomes: IT
session: - Identify the differences between classic and optimized training datasets
- Demonstrate the concept of training data as a mirror
- Identify potential sacrifices in obtaining accurate models
- Summarize the methodology for building accurate predictive models
- Explain machine learning training and accuracy guidelines
learning_outcomes: I
session: - Summarize moral quandaries with artificial general intelligence
- Describe the economic benefits and consequences of artificial general intelligence
- Contrast current vs future machine learning practices
- Define general vs narrow intelligence
- Analyze the trajectory of machine learning and AI research
learning_outcomes: IT
session: Artificial Intelligence Data Fairness and Bias
- Compare examples of algorithmic fairness
- Identify the constraints of fairness and accuracy in ethical models
- Analyze tradeoffs when choosing who to protect from algorithmic bias
- Compare different measures of parity
- Define fairness in the context of machine learning
learning_outcomes: I
session: Artificial Intelligence Data Fairness and Bias
- Discover auditing model attributes
- Appraise a predictive model for fairness issues
- Identify how to recognize fairness issues and deploy solutions in real-world scenarios
- Define pareto curves and identify where they're used in machine learning
- Explain how to use fairness as a modeling concept
learning_outcomes: I
session: - Identify the effects of game theory on algorithmic behavior
- Analyze methods to avoid predictive bias
- Examine the ability of algorithms to influence and decide human behavior in biased ways
- Identify potential bias errors in creating model attributes
- Define common sources of bias in data collection
learning_outcomes: IT
session: Artificial Intelligence Privacy and Convenience
- Define privacy in the context of machine learning
- Define k-anonymity
- Identify issues of privacy in anonymous datasets
- Analyze algorithmic privacy violations
Identify vulnerabilities in public datasets
learning_outcomes: I
session: Artificial Intelligence Privacy and Convenience
- Define data minimization
- Identify ways to maximize privacy and security of datasets
- Define differential privacy
- Summarize techniques used in differential privacy
learning_outcomes: I
session: - Analyze the concepts involved in creating transparent, explainable AI
- Evaluate tradeoffs involved in ethical model production
- Compare explainable models with black box models for trust and accountability
- Formulate an ethical model using privacy and fairness considerations
- Explore how explainable models can improve group outcomes using game theory
learning_outcomes: IT
session: Artificial Intelligence Ethics in Action
- Examining bias in a predictive text model
- Apply bias and fairness analysis skills in a real world scenario
learning_outcomes: I
session: Artificial Intelligence Ethics in Action
- Understanding fairness issues with sensitive predictions
- Apply bias and fairness analysis skills in a real world scenario
learning_outcomes: I
session: Artificial Intelligence Ethics in Action
- Predicting ethical issues before they arise in model theory
- Apply game theory fairness analysis and skills in a fictional scenario
Course 4 Review
learning_outcomes: IT