Assignment Instructions on Ethical Issues in Artificial Intelligence and Automation
Assignment 4
General Assessment Guidance
This assignment is the main assessed component of the module. Expected length: 1,000–1,500 words, allowing sufficient space for nuanced exploration without superficial treatment. Submissions below this range risk underdeveloped reasoning; submissions above it risk diluting focus.
All work must be uploaded via Turnitin online access. Submissions by email, pen drive, or hard copy will not be considered. Late submissions are ineligible for marking.
Maintain anonymity using only your Student Reference Number (SRN). Including personal identifiers may invalidate your submission.
A total of 100 marks is available; a minimum pass mark is 50%. Use Harvard referencing consistently. Unreferenced use of published material is plagiarism. AI tools may be used only for language review or draft proofreading, not for content creation, analysis, or ethical interpretation.
Attach a completed Assignment Cover Sheet. Missing documentation may result in administrative rejection.
Assessment Brief
Analytical Context
This assignment requires a critical investigation of ethical dilemmas in AI and automation. The focus is on practical, theoretical, and societal considerations: algorithmic bias, privacy concerns, accountability, transparency, and human oversight.
Your report should integrate empirical evidence, case studies, and ethical frameworks to explore how AI technologies challenge organizational practices, regulatory systems, and societal norms. Avoid a purely descriptive account; aim to demonstrate analytical depth, ethical reasoning, and scholarly insight.
Learning Outcomes
LO1 – Evaluate the ethical implications of AI and automation in applied contexts.
LO2 – Assess organizational, societal, and regulatory complexities arising from automated systems.
LO3 – Apply ethical frameworks to critically examine real-world AI dilemmas.
LO4 – Present evidence-based insights that combine theory, analysis, and practical understanding.
Key Areas to Cover
- Executive Overview
- Emerging Ethical Risks in AI Systems
- Societal and Organizational Impact
- Analytical Focus of the Report
- Stakeholder Perspectives
- Critical Evaluation Using Secondary Sources
Insights and Forward-Looking Reflections
Analysis must demonstrate integration of ethical theory, case evidence, and policy discourse. All assertions should be grounded in scholarly sources; anecdotal or media-driven claims are not sufficient.
Suggested Report Structure
Cover page with SRN
• Title page
• Table of contents
• Executive overview
• Emerging ethical risks in AI systems
• Societal and organizational impact
• Analytical focus of the report
• Stakeholder perspectives
• Critical evaluation using secondary sources
• Insights and forward-looking reflections
• Harvard references
• Appendices (if required)
Word count applies only to the main body. Front matter, references, and appendices are excluded.
Word Count Breakdown (Approximate)
Executive Overview – 120
Emerging Ethical Risks – 200
Societal and Organizational Impact – 250
Analytical Focus – 100
Stakeholder Perspectives – 200
Critical Evaluation – 450
Insights and Reflections – 250
Total – approximately 1,470 words
These allocations are indicative; analytical depth and clarity take precedence.
Executive Overview
Prepare this section last. Summarize the report’s main findings, including ethical risks, key stakeholders, analytic approach, and core insights. A strong overview highlights why these ethical issues matter for society, organizations, and policy, without simply listing sections.
Emerging Ethical Risks in AI Systems
Analyze major ethical challenges, including algorithmic bias, data privacy, transparency gaps, accountability issues, and job displacement. Use contemporary examples from healthcare, finance, autonomous vehicles, or other sectors to illustrate each challenge.
Societal and Organizational Impact
Evaluate how AI and automation reshape organizational decision-making, sectoral outcomes, and societal norms. Discuss trade-offs between efficiency, innovation, and ethical responsibility, highlighting both intended and unintended consequences.
Analytical Focus of the Report
Clarify the report’s purpose: assessing risk, evaluating ethical frameworks, analyzing organizational or policy responses. Position your work as evidence-based analysis rather than advocacy or prescriptive instruction.
Stakeholder Perspectives
Identify and examine stakeholders such as developers, regulators, companies, employees, and affected communities. Assess influence, interest, and ethical responsibility, highlighting conflicts or synergies.
Critical Evaluation Using Secondary Sources
Engage with academic literature, policy reports, and case studies. Apply ethical frameworks, utilitarianism, deontology, virtue ethics, or stakeholder theory, to evaluate decisions, trade-offs, and consequences. Address methodological limitations and contrasting perspectives.
Insights and Forward-Looking Reflections
Offer evidence-informed insights and potential pathways for ethical governance, transparency, or accountability in AI deployment. Conclude by reflecting on broader societal and organizational implications, emphasizing analytical depth and ethical reasoning.
References and Presentation
Use Harvard referencing consistently. Include academic journals, policy documents, and reputable industry reports. Ensure professional formatting: clear headings, numbered pages, labelled tables/figures.
High-quality submissions integrate ethical theory, empirical evidence, and organizational analysis, presenting AI and automation as complex ethical challenges requiring careful, evidence-based reflection.