Theory 01
Principal-Agent Theory in AI
Dowding & Taylor (2024) apply the Principal-Agent (P-A) framework to understand the delegatory relationship between humans and algorithms. In this model, the Principal (the firm) delegates tasks to an Agent (the AI).
Key Agency Problems (Dowding & Taylor, 2024)
- Information Asymmetry: The Agent (AI) possesses complex decision logic that the Principal cannot easily verify.
- Misalignment: The AI might optimize for a different goal (e.g., short-term accuracy) than the Principal's long-term strategic objective.
- Agency Costs: The costs of monitoring the AI, creating evaluation benchmarks, and the "residual loss" from imperfect decisions.
Theory 02
Trust in Algorithmic Systems
Trust is a prerequisite for delegation. Session 09 Course Materials differentiate between different bases of trust when dealing with opaque algorithmic systems.
Institution-Based Trust
Trusting the system because it's governed by regulations, audits, and professional standards (Dowding & Taylor, 2024).
Explainability (XAI)
The technical ability to make the "Black Box" transparent. If we understand the *why*, we can trust the *what*.
Relational Trust
Trust built over time through consistent, reliable performance of the algorithm in practice.
Theory 03
Algorithmic Internalities
Dowding & Taylor (2024) introduce Algorithmic Internalities: the harms or suboptimal outcomes borne by the users/principals themselves due to their choice of algorithm.
Internalities vs. Externalities
While society worries about bias (Externalities), the firm worries about Internalities:
- Loss of Human Capability: De-skilling of workers who become over-reliant on AI.
- Strategic Narrowing: The algorithm might optimize for past patterns, missing radical future innovations.
- Financial Loss: Bad predictions leading to failed R&D or poor resource allocation.
Case Application
Novo Nordisk — Governing the Agent
Applying Session 09 logic to the OpenAI partnership:
- Agency Costs at Novo: The cost isn't just the OpenAI subscription. It's the cost of Novo's scientists verifying every lead the AI generates. If they don't verify, the Agency Risk (bad drug leads) is too high.
- Institutional Trust: Novo must rely on Institutional Trust. They need clear governance frameworks for how clinical data is used and how AI results are validated against biological ground truth.
- Black Box Risk: In pharmaceutical research, a "Black Box" prediction isn't enough for the FDA. Novo needs Explainability to transform AI-output into scientific knowledge.
Exam Preparation
Likely Oral Exam Questions
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Core Why is the Principal-Agent framework useful for studying AI in organizations? ▶
- It highlights that AI is not just a tool, but a **delegation of authority**.
- It forces us to look at **Information Asymmetry**: the firm doesn't know *how* the AI makes a choice, creating room for misalignment and errors.
- It helps quantify the **Agency Costs** of governing AI (monitoring, auditing).
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Apply What is "Institution-Based Trust" and why is it relevant for Novo Nordisk? ▶
- It's trust based on the "rules of the game" (regulations, external audits).
- Relevant because Novo cannot monitor OpenAI's trillions of parameters directly. They must trust that the institution of the partnership (contracts, data privacy laws) will protect them.
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Synthesis Can a firm ever fully eliminate Agency Costs when using AI? ▶
- No. There is always a **Residual Loss**.
- You can increase monitoring (which costs money) or accept more risk (which can cost money).
- The goal is to find the **optimal point** where the marginal cost of monitoring equals the marginal benefit of reduced error.