Theory 01
The Ethics of Scale, Scope, and Learning
Iansiti & Lakhani (2020) argue that the drivers of digital value—Scale, Scope, and Learning—also create unique ethical challenges. As digital systems grow, their potential for harm scales proportionally.
Market Concentration
Digital returns favor giants. This leads to platform monopolies that can stifle competition and choice (Iansiti & Lakhani, 2020).
Digital Amplification
A single algorithmic error (bias or leak) affects millions instantly due to the speed and scale of the operating model.
Privacy Erosion
The "AI Factory" requires massive data. The pressure to feed the machine often conflicts with individual privacy rights.
Theory 02
Algorithmic Bias & Fairness
AI models inherit human biases present in training data. Session 10 Course Materials highlight that "blindly" following data leads to the replication of social injustices.
Key Ethical Risks
- Data Bias: Training models on historically skewed data (e.g., medical trials excluding certain demographics).
- Feedback Loops: Biased predictions leading to actions that create more biased data (e.g., predictive policing).
- Lack of Recourse: When the "Black Box" makes a mistake, who is held accountable, and how can the victim appeal?
Theory 03
AI & Environmental Impact
Marabelli & Davison (2025) explore the often-ignored environmental cost of digital transformation. Training and running Large Language Models (LLMs) consumes massive energy and water.
Carbon Footprint
Data centers are energy-intensive. Training a single large model can emit as much CO2 as five cars in their lifetime.
Water Usage
Cooling data centers requires billions of liters of water, often in areas already facing water scarcity (Marabelli & Davison, 2025).
Greening via AI
Strategic opportunity: Using AI to optimize supply chains and reduce waste, potentially offsetting its own footprint.
Case Application
Novo Nordisk — Moral Complexity
Ethical synthesis for the pharmaceutical case:
- The Ethics of Efficiency: The 9,000 layoffs mentioned in your synopsis are an ethical "Internality" of AI adoption. Does the gain in R&D speed justify the human cost?
- Bio-data Privacy: Partnering with OpenAI means Novo must ensure that sensitive patient data used for training models is rigorously protected and never "leaks" into the public model.
- Healthcare Inequity: If the AI is trained on data from Western clinical trials, the drugs discovered might be less effective for global populations, creating a global health bias.
Exam Preparation
Likely Oral Exam Questions
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Core How does the "Scale" of digital firms create new ethical responsibilities? ▶
- Because digital models serve millions of users, any ethical failure is amplified.
- Concentrated power in platforms requires higher standards of **Transparency** and **Accountability** than traditional, fragmented markets.
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Core Discuss the environmental trade-offs of using AI in the pharmaceutical industry. ▶
- **Cost:** High energy/water usage for LLMs (Marabelli & Davison, 2025).
- **Benefit:** AI can discover greener chemical processes or more efficient supply routes.
- **Conclusion:** Firms must implement "Green AI" governance to ensure the net impact is positive.
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Synthesis Is a firm responsible for the ethical lapses of its digital partner (e.g., OpenAI)? ▶
- Yes, through the concept of **Supply Chain Responsibility**.
- Just as a firm is responsible for child labor at its clothing suppliers, a modern firm like Novo is increasingly held responsible for the **Bias** or **Privacy** standards of its AI providers.