Introduction to AI Ethics
& Responsible Use
OVERVIEW
Today's Agenda
01
What is AI?
Defining artificial intelligence and its core technologies
02
What is AI Ethics?
Principles and frameworks for responsible development
03
Why Ethics Matters
Professional responsibility for AI engineers
04
AI Misuse in Academia
Academic integrity in the age of AI
05
Class Debate
"Is using ChatGPT cheating?"
06
Assignment
AI Usage Audit: 2-week monitoring project
01
Understanding
Artificial Intelligence
Defining AI and Its Core Technologies
FOUNDATIONS
What is Artificial Intelligence?
Core Definition
Artificial Intelligence (AI) refers to computer systems capable of performing tasks that typically require human intelligence. These include understanding natural language, recognizing patterns, making decisions, and learning from experience.
Key Characteristics
Learning: Improves performance through experience and data
Reasoning: Draws inferences and applies logic to solve problems
Perception: Interprets sensory information (vision, speech, text)
Language: Understands and generates human language
The AI Hierarchy
AI encompasses Machine Learning (ML) , which uses algorithms to learn from data. ML includes Deep Learning (DL) , which employs neural networks with multiple layers to process complex patterns. DL powers Generative AI like ChatGPT.
Types of AI
1
Narrow AI (Weak AI)
Designed for specific tasks (e.g., chess, translation, image recognition). All current AI systems fall into this category.
2
General AI (Strong AI)
Hypothetical AI with human-like general intelligence across all domains. Not yet achieved.
3
Super AI
Theoretical AI surpassing human intelligence in all aspects. Subject of ongoing debate and research.
Key Insight
While we often use "AI" to describe today's tools, most are sophisticated forms of machine learning—not truly "intelligent" in the human sense, but powerful pattern recognition systems.
REAL-WORLD APPLICATIONS
AI in Everyday Life
Virtual Assistants
Siri, Alexa, Google Assistant use natural language processing to understand voice commands, answer questions, and control smart home devices.
Technology: NLP, Speech Recognition
Recommendation Systems
Netflix, Spotify, YouTube analyze your preferences and behavior to suggest movies, music, and videos tailored to your tastes.
Technology: Collaborative Filtering, ML
Autonomous Vehicles
Self-driving cars use computer vision, sensors, and deep learning to navigate roads, detect obstacles, and make real-time driving decisions.
Technology: Computer Vision, Deep Learning
Medical Diagnosis
AI systems analyze medical images, detect diseases, and assist doctors in diagnosing conditions like cancer, eye diseases, and heart conditions.
Technology: Image Recognition, Neural Networks
Language Translation
Google Translate, DeepL use neural machine translation to convert text between languages with increasing accuracy and fluency.
Technology: NLP, Transformers
Generative AI
ChatGPT, DALL-E, Midjourney generate human-like text, images, and creative content based on user prompts and instructions.
Technology: Large Language Models, GANs
Important Note: AI is already deeply integrated into our daily lives, often in ways we don't even realize. Understanding how these systems work—and their ethical implications—is essential for informed citizenship and responsible engineering.
02
Foundations of
AI Ethics
Principles and Frameworks for Responsible Development
ETHICAL FOUNDATIONS
What is AI Ethics?
Definition
AI Ethics is the study of moral issues and societal impacts arising from the development and deployment of artificial intelligence systems. It examines how AI technologies affect human values, rights, and wellbeing, and establishes guidelines for responsible innovation.
Why AI Ethics Matters
High Stakes: AI systems make consequential decisions affecting millions of lives in healthcare, criminal justice, finance, and employment
Rapid Innovation: Technology evolves faster than regulations, creating gaps that ethical frameworks must fill
Power Imbalances: AI can amplify existing inequalities and create new forms of discrimination
Accountability Gaps: When AI makes mistakes, determining responsibility is complex
"Innovation in AI consistently outpaces government regulation, creating an urgent need for internal ethical leadership."
— Digital Ethics Research, 2024
Core Ethical Principles
1
Fairness & Non-Discrimination
AI systems should treat all individuals equitably, without amplifying societal biases or creating discriminatory outcomes
2
Transparency & Explainability
AI decision-making processes should be understandable to stakeholders, enabling scrutiny and informed consent
3
Accountability & Responsibility
Clear lines of responsibility must exist for AI system outcomes, with mechanisms for redress when harms occur
4
Privacy & Data Protection
Personal data must be protected throughout the AI lifecycle, with individuals maintaining control over their information
Additional Principles
Beneficence: Promoting good
Justice: Fair distribution
Autonomy: Respecting choice
GLOBAL FRAMEWORK
UNESCO's 10 Core Principles for AI Ethics
1
Proportionality & Do No Harm
AI use must not exceed what's necessary to achieve legitimate aims. Risk assessments should prevent harms to individuals and society.
2
Safety & Security
AI systems must avoid unwanted harms (safety risks) and vulnerabilities to attacks (security risks) through robust design and testing.
3
Right to Privacy & Data Protection
Privacy must be protected throughout the AI lifecycle. Adequate data protection frameworks should be established and enforced.
4
Multi-stakeholder Governance
International law and national sovereignty must be respected. Diverse stakeholder participation is necessary for inclusive AI governance.
5
Responsibility & Accountability
AI systems should be auditable and traceable. Oversight mechanisms must avoid conflicts with human rights and environmental wellbeing.
6
Transparency & Explainability
Ethical AI deployment depends on transparency. The level should be appropriate to context, balancing with privacy and security needs.
7
Human Oversight & Determination
AI systems must not displace ultimate human responsibility. Humans must remain in control of critical decisions affecting people's lives.
8
Sustainability
AI technologies should be assessed against sustainability goals, including environmental impact and alignment with UN Sustainable Development Goals.
9
Awareness & Literacy
Public understanding of AI should be promoted through education, civic engagement, digital skills training, and AI ethics literacy programs.
10
Fairness & Non-Discrimination
AI actors should promote social justice and fairness, taking an inclusive approach to ensure AI's benefits are accessible to all.
Global Context: These principles, adopted by UNESCO member states in 2021, represent the first global standard on AI ethics. They provide a human-rights centered approach that all AI practitioners should understand and apply.
03
Ethics for
AI Engineers
Why Professional Responsibility Matters
PROFESSIONAL RESPONSIBILITY
Why Ethics Matters for AI Engineers
The Stakes Have Never Been Higher
AI systems now power critical decisions in healthcare, criminal justice, finance, and employment. As an AI engineer, your code can affect millions of lives—determining who gets a loan, who receives medical treatment, or who is flagged as a security risk.
Critical Statistic: Over 85% of AI projects are projected to deliver erroneous outcomes due to ethical oversights by 2024, highlighting the urgent need for ethical frameworks in development.
The Innovation-Regulation Gap
AI technology evolves exponentially faster than laws and regulations can adapt. This creates a dangerous gap where harmful practices can become entrenched before safeguards are implemented.
Government regulation typically lags 5-10 years behind technology
Industry self-regulation is often insufficient or nonexistent
Engineers must proactively consider ethical implications
Engineer's Ethical Responsibilities
Consider Societal Impact
Think beyond technical requirements to how your system affects communities and individuals
Include Diverse Perspectives
Ensure development teams represent diverse backgrounds to identify blind spots
Test for Bias
Regularly audit algorithms for discriminatory outcomes across different groups
Document Decisions
Maintain clear records of design choices and ethical considerations
Speak Up
Report ethical concerns even when it's uncomfortable or unpopular
Key Insight
Ethical AI development isn't about preventing innovation—it's about ensuring that innovation serves humanity's best interests and doesn't cause unintended harm.
CASE STUDIES
Real-World Consequences of Unethical AI
Biased Hiring Algorithms
Amazon developed an AI recruiting tool that was trained on 10 years of hiring data. The system learned to penalize resumes containing the word "women's" (as in "women's chess club captain") and favored male candidates for technical positions.
Impact: The system perpetuated gender discrimination at scale. Amazon eventually scrapped the project, but not before it had influenced hiring decisions.
Facial Recognition Bias
Multiple studies found that facial recognition systems from major tech companies had significantly higher error rates for darker-skinned women (up to 34.7%) compared to lighter-skinned men (0.8%).
Impact: These systems, deployed in law enforcement and security contexts, led to false arrests and surveillance disparities affecting marginalized communities.
Criminal Justice Algorithms
The COMPAS recidivism prediction tool, used in courtrooms across the U.S., was found to falsely flag Black defendants as future criminals at twice the rate of white defendants.
Impact: Judges used these biased risk scores to determine sentencing and parole decisions, perpetuating systemic racism in the criminal justice system.
Privacy Violations
AI systems trained on vast datasets often contain sensitive personal information. Cambridge Analytica harvested data from 87 million Facebook users without consent to build psychological profiles for political targeting.
Impact: Personal data was weaponized to manipulate elections and undermine democratic processes, violating fundamental privacy rights.
Critical Lesson: These aren't just technical failures—they're ethical failures with real human consequences. Each case demonstrates why AI engineers must prioritize fairness, transparency, and accountability from the earliest stages of development.
04
AI Misuse in
Academia
Understanding Academic Integrity in the Age of AI
BY THE NUMBERS
The Rise of AI in Academic Settings
Student AI Usage Statistics
43%
of college students
have used ChatGPT or similar AI tools
89%
of AI users
used it for homework assistance
53%
of AI users
used it for writing essays
48%
of AI users
used it for at-home tests
The Cheating Epidemic
UK
Nearly 7,000 UK university students were formally caught cheating with AI tools in the 2023-24 academic year
This represents 5.1 cases per 1,000 students—triple the rate from the previous year
K-12
26% of K-12 teachers have caught a student cheating with ChatGPT
50% of teachers know at least one student who faced consequences for AI misuse
HS
6.4% to 24.1% of high school students admitted to using AI to cheat, varying by school type
Charter schools: 24.1% | Public schools: 15.2% | Private schools: 6.4%
Student Perspectives
Think using ChatGPT is cheating
51%
Still use it despite believing it's cheating
22%
Believe AI should explain concepts
60%
The Nuanced Reality
While many students recognize ethical concerns, they also see legitimate educational uses for AI. The challenge is distinguishing between appropriate assistance and academic dishonesty—a distinction that varies by context and assignment.
REAL-WORLD CASE
Case Study: The Grammarly Girl Incident
The Incident
In October 2023, Marley Stevens, a student at the University of North Georgia (UNG), received a zero on a paper and was accused of using AI to cheat. Her offense? Using Grammarly—an AI-powered grammar and spell-checking tool—to proofread her work.
The Irony: Grammarly was listed as a recommended resource on UNG's own website for promoting "grammar and style."
The professor's syllabus prohibited AI use, but many students understood this to mean generative AI like ChatGPT, not grammar checkers they'd been using for years.
The Consequences
Zero on the paper affecting her GPA
Academic probation until February 2025
Lost scholarship and financial aid
Required to attend academic integrity workshops
Six-month appeals process with no ability to further appeal
The Detection Problem
Turnitin's AI detection software flagged Stevens' paper as AI-generated. However, AI detectors are known to be highly unreliable:
University of Pennsylvania study: detectors easily fooled by spelling variations
Stanford study: biased against non-native English speakers
OpenAI disabled their own detection tool due to low accuracy
University of Reading: 94% of AI-written submissions went undetected
The Aftermath
Stevens took her story to TikTok, where it gained widespread attention. This public pressure prompted the university to address the case, but the damage was already done.
Positive Outcome: Grammarly developed "Authorship"—a tool to track text sources and AI modifications—in response to Stevens' case. She was invited to speak at Educause about her experience.
Discussion Questions
TECHNICAL CHALLENGES
The AI Detection Problem
How AI Detectors Work
AI detection tools analyze text for patterns like "burstiness" (variation in sentence structure) and "perplexity" (unpredictability of word choices). They assume human writing is more varied and creative than AI-generated text.
Key Difference: Unlike plagiarism detectors that compare text to databases, AI detectors look for statistical patterns—which can be highly unreliable.
Major Detection Failures
Racial & Linguistic Bias
Stanford study found detectors misclassified over 50% of non-native English writing as AI-generated, while native speaker accuracy was nearly perfect. This creates discriminatory outcomes.
High False Negative Rate
University of Reading test: 94% of AI-written submissions went undetected. Most cheaters aren't being caught, while some innocent students are falsely accused.
OpenAI Abandoned Detection
ChatGPT's own creator disabled their AI detection platform due to low accuracy rates, acknowledging the fundamental limitations of current detection methods.
Why Detection is Fundamentally Flawed
1
Arms Race Dynamics
As AI models improve, they produce more human-like text, making detection increasingly difficult
2
Human Editing Blurs Lines
Students can edit AI-generated text, making it statistically indistinguishable from human writing
3
Writing Style Variation
Human writing varies enormously by individual, context, and purpose—no universal "human pattern" exists
4
False Positives Harm Innocents
Wrongful accusations damage students' academic records, mental health, and future opportunities
Better Alternatives
Process-Oriented Assessment
Require drafts, outlines, and revision histories to track student work over time
Oral Exams & Presentations
Have students explain their thinking in person, demonstrating genuine understanding
In-Class Assignments
Design assessments that occur in controlled environments where AI use is limited
Clear AI Policies
Establish explicit guidelines about when and how AI tools can be used in coursework
Expert Consensus: Leading researchers and even AI companies agree that current detection tools are not reliable enough for high-stakes academic decisions. Relying on them risks harming innocent students while failing to catch actual cheaters.
05
Class
Debate
Is Using ChatGPT Cheating?
POSITION: YES
Arguments FOR: Using ChatGPT is Cheating
Academic Integrity Violation
Passing off AI-generated content as your own original work violates the fundamental principles of academic honesty. When you submit work that wasn't created by you, you're misrepresenting your knowledge, skills, and effort.
Key Point: Academic integrity requires that submitted work be your own authentic creation, produced through your own intellectual effort.
Skill Development Prevention
Using ChatGPT to complete assignments prevents you from developing essential skills: critical thinking, problem-solving, writing ability, and research skills. These competencies are the entire point of education—not just the final product.
Key Point: The learning happens in the struggle, the drafting, the revision—not in receiving a finished product from AI.
Unfair Advantage
Students who use AI to complete assignments gain an unfair advantage over those who do the work honestly. This creates an uneven playing field and devalues the achievements of students who put in genuine effort.
Key Point: If some students use AI while others don't, grades no longer reflect actual learning or ability.
Assessment Invalidation
Assignments are designed to assess your understanding and skills. If AI completes the work, the assessment becomes meaningless—neither you nor your instructor can accurately gauge what you've actually learned.
Key Point: Education relies on accurate assessment. AI use makes it impossible to evaluate genuine learning.
Student Opinion
51% of students believe using ChatGPT is cheating, and 95% of private high school students say AI should never be allowed to write an entire paper. Many students themselves recognize the ethical concerns.
POSITION: NO
Arguments AGAINST: Using ChatGPT is NOT Cheating
AI as a Learning Tool
ChatGPT is just another tool in the learning process, similar to calculators, spell-checkers, grammar tools, or search engines. These technologies were once controversial but are now accepted as legitimate educational aids.
Key Point: Tools don't cheat—people do. The ethical line depends on how you use the tool, not the tool itself.
Legitimate Educational Uses
Using AI for brainstorming, understanding complex concepts, getting explanations, or checking grammar enhances learning rather than replacing it. These uses are similar to asking a tutor or using educational resources.
Key Point:46-60% of students believe AI should always be allowed for explaining concepts—this is educational support, not cheating.
Context Matters
Whether AI use is appropriate depends entirely on context: the assignment's learning objectives, instructor guidelines, and institutional policies. Blanket bans ignore the nuanced reality of different educational scenarios.
Key Point: Using AI for a creative writing assignment differs from using it on a final exam. Context determines appropriateness.
Professional Preparation
In professional settings, using AI tools is becoming standard practice. Learning to use AI ethically and effectively is a valuable skill that prepares students for the modern workforce where AI assistance is the norm.
Key Point: The future workplace will require AI literacy. Banning AI in education may leave students unprepared.
The Transparency Solution
Many argue that the solution isn't banning AI, but requiring transparency and disclosure. If students clearly indicate how they used AI tools, they maintain academic integrity while benefiting from technological assistance—similar to citing sources or acknowledging collaborators.
NUANCED PERSPECTIVE
Finding the Middle Ground
The Ethical Use Spectrum
The ethical use of ChatGPT depends on a combination of factors: context, assignment guidelines, transparency, and learning objectives. Rather than a simple yes/no answer, we must consider where on the spectrum a particular use falls.
Ethical AI Use = Context + Transparency + Intent
✓ Generally Appropriate Uses
Brainstorming & Idea Generation
Getting initial ideas for essays, projects, or creative work
Concept Explanation
Asking AI to explain difficult topics in different ways
Grammar & Style Checking
Proofreading your own work (with disclosure if required)
Learning Coding Concepts
Understanding how code works, not copying solutions
Research Assistance
Finding sources and getting overviews (verifying accuracy)
✗ Generally Inappropriate Uses
Submitting AI-Generated Work as Original
Passing off AI-written essays, code, or assignments as your own
Using AI on Individual Assessments
Tests, exams, or assignments meant to evaluate your knowledge alone
Failing to Disclose When Required
Not acknowledging AI assistance when policies require transparency
Replacing Core Learning Activities
Using AI to skip essential practice that builds skills and understanding
Critical Thinking Framework
Before using AI for academic work, ask yourself:
1. What are the assignment's learning objectives?
2. Does my instructor allow AI use for this task?
3. Am I using AI to enhance learning or avoid it?
4. Can I explain and defend my work if asked?
5. Am I being transparent about AI assistance?
The Bottom Line: The ethical use of AI in education isn't about finding loopholes or pushing boundaries—it's about using technology to enhance your learning while maintaining integrity. When in doubt, ask your instructor and err on the side of transparency.
06
Your
Assignment
AI Usage Audit: 2-Week Monitoring Project
PRACTICAL EXERCISE
Assignment: AI Usage Audit
Assignment Overview
For the next two weeks, you will conduct a comprehensive audit of your AI tool usage. This exercise is designed to help you develop awareness of when, how, and why you use AI tools—and to reflect on the ethical implications of your choices.
Goal: Build self-awareness and ethical judgment about AI use, not to judge or penalize you. There are no "right" or "wrong" answers—only honest reflection.
What to Track
1
Every AI Tool Instance
Record each time you use ChatGPT, Grammarly, GitHub Copilot, Midjourney, or any other AI-powered tool
2
Purpose & Context
Note what you were trying to accomplish (homework help, coding, brainstorming, grammar check, etc.)
3
Course/Assignment
Identify which class or assignment you were working on (if applicable)
4
Time Spent
Estimate how long you used the AI tool for that task
5
Ethical Assessment
Reflect on whether this use felt appropriate, questionable, or inappropriate
Sample Tracking Template
Date | Tool | Purpose | Assessment |
03/04 | ChatGPT | Explain recursion | ✓ OK |
03/05 | Grammarly | Proofread essay | ✓ OK |
03/06 | ChatGPT | Debug code | ? Maybe |
03/07 | ChatGPT | Write conclusion | ✗ No |
Reflection Questions
At the end of two weeks, answer these questions in a 1-2 page reflection:
1. What patterns did you notice in your AI usage?
2. Which uses felt most/least ethically justified? Why?
3. How did AI use impact your learning and skill development?
4. What would you do differently? What will you continue?
5. How has this audit changed your perspective on AI ethics?
Submission Details
Due Date: Two weeks from today. Submit your tracking log and reflection paper via email. This assignment will be evaluated on completeness, thoughtfulness, and honest self-reflection—not on how much or how little AI you used.
SUMMARY
Key Takeaways & Best Practices
Understanding AI
AI is a powerful tool with both benefits and risks
Machine learning and deep learning are subsets of AI
AI is already deeply integrated into daily life
Current AI is "Narrow AI"—not general intelligence
AI Ethics
Core principles: Fairness, Transparency, Accountability, Privacy
UNESCO provides 10 core principles for ethical AI
Ethics is about societal impact, not just technical performance
Principles are interconnected and must be balanced
Engineer's Role
AI engineers have special responsibility to society
Consider societal impact from the earliest design stages
Test for bias and include diverse perspectives
Document decisions and speak up about concerns
Academic Integrity
AI misuse is a growing problem in education
AI detection tools are unreliable and biased
False positives can harm innocent students
Clear policies and transparency are essential
The ChatGPT Debate
Context determines whether AI use is appropriate
Transparency and disclosure are key ethical practices
AI can enhance learning when used appropriately
Submitting AI work as original is academic dishonesty
Best Practices
Always check your instructor's AI policy first
Be transparent about AI assistance when required
Use AI to enhance learning, not replace it
Develop critical thinking alongside technical skills
Remember: Ethics is not a one-time decision but an ongoing practice. As AI continues to evolve, so too must our ethical frameworks and personal guidelines. The goal is not perfection, but thoughtful consideration of how our actions affect ourselves, our communities, and society at large.
Questions for Reflection
How will you use AI tools ethically in your academic journey?
What responsibilities do AI engineers have to society?
How can we balance innovation with ethical considerations?
"The real question is not whether machines think, but whether humans do."
— B.F. Skinner