Computer Science
Grade 12
20 min
Ethical Considerations
Ethical Considerations
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1
Introduction & Learning Objectives
Learning Objectives
Analyze the ethical implications of emerging technologies like AI, quantum computing, and bioinformatics.
Evaluate the societal impact of algorithmic bias in machine learning models.
Apply ethical frameworks (e.g., utilitarianism, deontology) to complex computing dilemmas.
Propose design solutions that mitigate privacy risks in large-scale data systems.
Deconstruct the ethical responsibilities of a software engineer throughout the product lifecycle.
Differentiate between legal compliance and ethical responsibility in technology development.
🤖 If an AI creates a life-saving drug but also a dangerous weapon from the same data, who is responsible? Let's explore the code of conduct for the future.
This lesson moves beyond just writing code to explore t...
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Key Concepts & Vocabulary
TermDefinitionExample
Algorithmic BiasSystematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.A facial recognition system trained predominantly on light-skinned faces fails to accurately identify individuals with darker skin tones, leading to higher error rates for that demographic.
Explainable AI (XAI)A set of methods and techniques in artificial intelligence that allows human users to understand and trust the results and output created by machine learning algorithms.A medical AI not only diagnoses a condition but also highlights the specific features in a patient's scan (e.g., tumor shape, density) that led to its conclusion, allowing a doctor to verify the reasoning.
Ethical FrameworkA structur...
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Core Syntax & Patterns
The ACM Code of Ethics: Principle 1.1
Contribute to society and to human well-being, acknowledging that all people are stakeholders in computing.
Use this as a foundational principle when designing any system. Before writing a line of code, ask: 'Who are all the stakeholders? What are the potential positive and negative impacts on them? Does this work enhance human well-being?'
Privacy by Design (PbD) Framework
Proactive not Reactive; Privacy as the Default Setting; Embedded into Design.
A system design principle where privacy is a core requirement, not an add-on. When designing an application, default all user settings to the most private option and build security measures into the architecture from the start.
The FAT Principle in Machine Learning
Systems sh...
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Challenging
You are tasked with designing a system to mitigate bias in the predictive policing algorithm from the worked example. The goal is to maintain public safety without creating a feedback loop. Synthesizing concepts from the tutorial, which of the following represents the most comprehensive ethical solution?
A.Replace the historical arrest data with census and economic data to predict areas of need, not crime.
B.Implement an XAI dashboard for public oversight, diversify input data beyond historical arrests to include social service calls, and establish a clear accountability structure for the algorithm's impact.
C.Apply the 'Privacy by Design' framework by encrypting all data used by the algorithm and making its predictions anonymous.
D.Adopt a 'move fast and break things' approach by deploying multiple versions of the algorithm in different precincts to see which one performs best.
Challenging
A startup is building a mental health chatbot. The CEO wants to use a 'move fast and break things' approach to capture the market. As the lead engineer, you need to advocate for a more responsible development lifecycle. Which argument best synthesizes the tutorial's principles to persuade the CEO?
A.'move fast' approach will generate more bugs, slowing us down in the long run.
B.We are legally required to follow HIPAA, so we must move slowly.
C.As a Data Fiduciary for sensitive health data, our duty of care requires a 'Privacy by Design' approach. A failure here could cause significant harm, violating our ethical responsibility under the ACM Code and destroying user trust, which is our most valuable asset.
D.Competitors are not moving fast, so we have time to be more thoughtful in our design process.
Challenging
Evaluate the ethical implications of using advanced bioinformatics and AI to offer personalized education plans based on a student's genetic predispositions for certain cognitive traits. Which statement presents the most critical synthesis of the ethical risks discussed in the tutorial?
A.The system would be unethical because it violates the 'Technology is Neutral' fallacy.
B.This system could create a new, genetically-based 'Digital Divide', introduce algorithmic bias by misinterpreting complex genetic data, and raise profound privacy issues, effectively creating a form of technological determinism for a child's future.
C.The primary ethical issue is one of accountability, as it would be unclear whether the student, teacher, or algorithm is responsible for learning outcomes.
D.The system is ethically sound if it adheres to the 'Privacy by Design' framework by encrypting the genetic data.
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