Course: AI Ethics
Chapter 1: Approaching AI Ethics introduces the urgent need to align artificial intelligence with human values as AI reshapes healthcare, finance, and public life. The chapter frames AI ethics around core principles—beneficence, justice, transparency, accountability, and stakeholder engagement—that should guide every stage of design and deployment. Beneficence urges developers to maximize social good while minimizing harms, including long-term societal impacts such as job displacement and cultural change. Justice demands fairness and equity in datasets, algorithms, and outcomes so that marginalized communities are not further disadvantaged by automated decisions. Transparency and explainability are presented as prerequisites for trust, enabling affected individuals to understand and contest algorithmic decisions. The chapter stresses accountability, calling for clear legal and organizational lines of responsibility when AI systems cause errors or harm. It highlights practical challenges such as biased training data, opaque models, and the tension between proprietary systems and public scrutiny. Regulatory frameworks and adaptive policies are recommended to ensure that governance keeps pace with rapid technological change. Interdisciplinary collaboration—bridging computer science, ethics, law, and social sciences—is shown to be essential for nuanced solutions. Stakeholder engagement, especially including those communities most affected, is emphasized as both an ethical obligation and a source of better design. The chapter also introduces methods for identifying AI actions, classifying assisted, autonomous, and malicious behaviors to assess risk and mitigation strategies. Finally, it argues that ethical AI is not a static checklist but an ongoing process of reflection, audit, and reform as technologies and societies evolve. By instilling these principles early in the AI lifecycle, we can better harness innovation while protecting human dignity and democratic values.Lesson
Topic: Chapter 1 — Approaching AI Ethics
Learning Outcomes
- Understand core AI ethics principles: beneficence, justice, transparency, accountability.
- Identify common ethical risks (bias, privacy, autonomy, sustainability).
- Apply stakeholder-centered practices to AI design and deployment.
- Use basic tools for assessing transparency and fairness in models.
- Draft simple accountability and governance checkpoints for projects.
Conceptual Explanation
This lesson introduces foundational concepts in AI ethics and situates them within practical development lifecycles. Beneficence asks designers to promote positive outcomes and foresee harms; justice focuses on equitable treatment across groups. Transparency requires explainability and traceability so decisions can be inspected; accountability assigns responsibility across teams and institutions. Stakeholder engagement, interdisciplinary review, and adaptive regulation are recommended to manage evolving risks.
Bilingual (EN → বাংলা)
| English | বাংলা (Bangla) |
|---|---|
| Ethics | নীতিশাস্ত্র |
| Beneficence | কল্যাণকামীতা |
| Justice / Fairness | ন্যায় / সমতা |
| Transparency | স্বচ্ছতা |
| Accountability | দায়িত্বশীলতা |
| Bias | পক্ষপাত |
Important Notes
- Ethics is iterative: integrate reviews at design, testing, deployment, and post-deployment stages.
- Data provenance and diversity are as important as modeling technique for fairness.
- Explainability requirements vary by context—balance interpretability and performance accordingly.
- Accountability requires documentation: model cards, audit logs, impact assessments.
Exercises
True / False
- Transparency always requires releasing the full source code of a model.
- Bias can be introduced by skewed training data.
- Accountability is solely the developer's responsibility.
Answer
- False — transparency can mean clear documentation, explanations, and audits; not always full code disclosure.
- True — biased or unrepresentative training data is a common source of algorithmic bias.
- False — accountability is shared among developers, deployers, organizations, and regulators.
Fill in the Blanks
- Principle that focuses on maximizing benefits and minimizing harm: __________.
- Making an AI decision process understandable is called __________.
- Ensuring fair outcomes across groups addresses __________.
Answer
- Beneficence
- Explainability (or Transparency)
- Justice / Fairness
Multiple Choice Questions (MCQ)
- Which practice helps reduce dataset bias?
A. Ignoring minority classes
B. Augmenting or re-sampling underrepresented groups
C. Removing all metadata
D. Using smaller models only - Explainable AI methods such as LIME and SHAP are examples of:
A. Data collection techniques
B. Post-hoc explanation tools
C. Encryption standards
D. Regulatory bodies
Answer
- B — augmenting or re-sampling to improve representation helps reduce bias.
- B — LIME and SHAP provide post-hoc explanations for model outputs.
Matching
Match the term with its primary aim.
| Term | Aim |
|---|---|
| Transparency | 1. Preserve user privacy |
| Privacy | 2. Make decision logic understandable |
| Accountability | 3. Define responsibility and redress |
Answer
- Transparency → 2 (Make decision logic understandable)
- Privacy → 1 (Preserve user privacy)
- Accountability → 3 (Define responsibility and redress)
Descriptive
- Briefly explain why stakeholder engagement matters in AI ethics (2–3 sentences).
- List three practical steps an organization can take to improve AI accountability.
Answer
- Stakeholder engagement ensures that voices of those affected by AI—especially marginalized groups—inform design choices, revealing harms technical teams may overlook and improving legitimacy and trust.
- Three steps: (a) publish impact assessments and model cards; (b) institute independent audits and review boards; (c) maintain incident logs and clear avenues for remedies and appeals.
Creative
- Draft a one-paragraph policy blurb (3–4 sentences) that an app developer could add to their privacy & ethics page to reassure users.
Answer
We commit to designing our AI systems with fairness, transparency, and user control at the forefront. We document model intent and limitations publicly, protect personal data by default, and provide users with options to review and correct automated decisions. Independent audits are conducted regularly and we maintain clear channels for redress should harms occur. Our team engages community representatives to guide updates and ensure the system serves diverse needs responsibly.
Activities
- Group discussion: analyze a publicized AI failure case and identify ethical lapses (30–45 min).
- Mini-audit: review a dataset for representation issues and propose fixes (45–60 min).
- Explainability demo: use an XAI tool to explain a simple classifier and present results (30 min).
Vocabulary
- Beneficence, Non-maleficence
- Bias, Fairness, Disparate Impact
- Explainability, Interpretability, Model Card
- Accountability, Redress, Governance
Homework
- Read a short article on GDPR and write a 200-word reflection on how it shapes AI design.
- Prepare a one-page checklist for “Ethics by Design” to apply to a small AI project.
Practice Task
Apply the checklist from Homework #2 to a sample dataset: identify at least two ethical risks and propose mitigations. Submit a 1–2 page report.