FAIR Lens: Week 1, Class 1 – Foundations & Frameworks

Opening Question for Reflection

You are a senior product manager at a technology company facing a potential liability issue: your mobile app has a feature that occasionally fails in ways that could cause users financial loss. Before you even consult a lawyer, consider:

• Flexibility: How might different legal frameworks (tort law, contract law, regulation) change how you approach this problem? What assumptions are you making about which rules apply?
• Adaptability: The “products liability” framework we’re about to explore was developed in a world of physical goods. What aspects of your digital product fit neatly into that framework? What doesn’t?
• Imagination: Imagine a user harmed by this feature. What story might they tell about your responsibility? What story might your company tell? Who is right, and why?
• Resilience: How should a responsible manager prepare for legal uncertainty when the rules haven’t yet adapted to new technology? What organizational practices build resilience in the face of unclear liability exposure?

FAIR Lens: Week 1, Class 2 – Two Sides of Algorithmic Defect

Consider two algorithmic systems, both causing harm:

Scenario A (Manufacturing Defect): An AI hiring algorithm is designed to identify “qualified candidates.” The engineering team built it according to specifications. But the algorithm was trained on historical hiring data that reflects decades of past discrimination. It consistently screens out qualified candidates from underrepresented groups. An engineering expert testifies: “The algorithm is working exactly as designed; it’s learning from the training data.” A social scientist testifies: “The design embeds historical biases into the system. The training data itself is the defect.”

Scenario B (Design Defect): A social media platform’s algorithm is intentionally designed to maximize engagement, the metric that drives advertising revenue. The design works perfectly: it identifies polarizing content and amplifies it because polarization drives clicks. A user harmed by algorithmic amplification claims that the platform should have designed an algorithm that prioritizes user well-being over engagement. The platform argues: “Well-being is harder to define and measure. Engagement optimization is economically essential to our business model.”

Both scenarios raise the same core question: In algorithmic systems, what counts as a “defect”? And who decides?

FAIR Exploration

Adaptability & Flexibility

• How would you adapt the “manufacturing defect” concept (deviation from design specifications) to fit digital products? In Scenario A, is the algorithm defective because it was built incorrectly, or because the specifications themselves were flawed? What do you lose or gain by translating manufacturing defect doctrine to algorithms?
• How would you adapt the “design defect” concept (unreasonably dangerous design when a reasonable alternative exists) to fit algorithmic systems? In Scenario B, is intentional engagement-maximization a design choice or a design defect? Who should determine what is “reasonable” in algorithmic design?

Imagination

• In Scenario A, the algorithm “learns and changes over time.” What does “deviation from design specifications” even mean for a system that was designed to learn? If the algorithm behaves differently than anticipated because it learns new patterns in data, is that a manufacturing defect or a feature?
• In Scenario B, what would a “reasonable alternative design” for a social media algorithm look like? How would you define “prioritizing user well-being”? What are the tradeoffs? (Is engagement-maximization more honest than wellness-maximization, since engagement is measurable and wellness is contested?)
• Under Daubert, whose expert testimony should be admissible? The engineer who built the system? The data scientist who trained it? The social scientist who understands bias? The ethicist who questions the business model? Do we need all of these experts to determine if an algorithm is “defective”?

Resilience

• How should liability law account for multiple contributors to algorithmic harm? When courts can’t agree on what counts as a manufacturing defect vs. a design defect in algorithmic systems, how should companies design, test, and deploy their products to manage liability exposure? What organizational practices would build resilience in the face of uncertain legal standards?
• In both scenarios, the “defect” arises from the interaction between human choice (what training data to use, which metric to optimize) and algorithmic execution. Where does the responsibility lie, with the human decision-makers, the algorithm itself, or the system as a whole? How should liability law account for this distributed responsibility?
• Algorithms are often proprietary and opaque; users and courts may not fully understand how they work. How should liability law adapt to systems that are intentionally designed to be black boxes? Should companies be required to make algorithms more transparent to reduce liability exposure?

Ethical Reflection

• In Scenario A, the company’s engineering team selected and deployed an algorithm trained on discriminatory historical data. What did the company know about the training data bias before deployment, when did they know it, and what did they choose to do with that knowledge?
• In Scenario B, the company knew its design prioritizes engagement over safety. What did they know about the radicalization risks, when did they know it, and what did they choose to optimize for instead?
• Now step out of these cases and think about your own future role: if you were the decision-maker facing the same evidence and the same pressures to deploy, what would you have chosen, and what does your answer reveal about the kind of manager you intend to be?

FAIR Lens: Week 3, Class 4 – The Warning Doctrine’s Limits

Scenario

A social media platform discovers that its recommendation algorithm amplifies polarizing content in ways that increase user engagement but also contribute to radicalization. The company considers issuing a “warning” to users. But what would that warning look like? “Warning: This algorithm may amplify extreme viewpoints.” An opt-out mechanism? A redesign? Should the company issue a recall-style update instead?

FAIR Exploration

• Imagination & Flexibility: What does “warning” mean for an algorithmic system? What would adequate disclosure look like? Who needs to be warned, and about what specifically?
• Adaptability & Resilience: The traditional “duty to warn” assumes consumers can read a label and make an informed choice. Does that assumption hold for algorithmic systems?
• Integrity: Boyle-Midway had documented evidence of the hazard before Spruill was harmed. What did the company know, when did they know it, and what did they choose to do with that knowledge? If you were the product manager who signed off on the warning label, what would your answer say about the kind of manager you intend to be?

FAIR Lens: Week 3, Class 5 – Testing Defectiveness in Digital Products

Dilemma for Discussion

A financial app makes algorithmic investment recommendations. Most users expect the algorithm to maximize their returns. The app’s actual design prioritizes risk reduction and diversification. Is this a design defect? Under the Consumer Expectations Test, yes, because users’ reasonable expectations are not met. Under the Risk-Utility Test, maybe not; the safer design benefits most users and avoids catastrophic losses. Which test is fairer? More efficient?

FAIR Questions

• Flexibility & Imagination: Design your own “defectiveness” test for digital products. What would it capture that traditional tests miss?
• Adaptability: How do consumer expectations form in digital contexts where features change constantly?
• Resilience: If two equally rigorous tests reach opposite conclusions about whether your financial app’s algorithm is defective, what should a responsible company do: default to the more protective test, disclose the ambiguity to users, or wait for a court to decide? What does the choice reveal about whether the company is managing risk or managing liability exposure?
• Ethical Reflection: Suppose the company’s own legal and product teams knew the Consumer Expectations Test would call the algorithm defective while the Risk-Utility Test would not, and marketed the product as maximizing returns anyway. What did they know about that gap, when did they know it, and what did they choose to disclose to users? Now step out of the case: if you were the product manager who saw both test results before launch, what would you have done, and what does your answer reveal about whether you’re optimizing for legal protection or for honest accountability?

FAIR Lens: Week 4, Class 6 – Standards, Expectations & Digital Innovation

Case Study

Liebeck v. McDonald’s teaches that even a widely used product (hot coffee) can be defective if the risk is foreseeable and the cost of prevention is low. Now apply this to a digital product: A cryptocurrency exchange has a user interface that makes it easy to accidentally send large sums to the wrong address. The company knew this was a usability risk, estimated the fix would cost $50,000, but decided the risk was acceptable because “users should be more careful.” Is this a design defect under Liebeck’s logic?

FAIR Reflection

• Imagination: How would Liebeck’s jury have decided this case? What would they have wanted to know about the company’s decision-making process?
• Flexibility & Adaptability: Does the Liebeck framework translate to digital products? Where does it work? Where does it break down?
• Resilience: If you were advising this company, what would you recommend they do to manage liability exposure while continuing to innovate?
• Integrity: McDonald’s had documented evidence of more than 700 burn injuries from its coffee before Stella Liebeck was harmed. What did the company know, when did they know it, and what did they choose to do with that knowledge? Faced with the same evidence, the same cost calculations, and the same institutional pressures, what would you have done, and what does your answer reveal about the kind of manager you intend to be?

FAIR Lens: Week 4, Class 7 – Regulation vs. Tort Liability

Policy Dilemma

The federal government proposes new regulations for AI hiring algorithms, requiring transparency and bias testing. The regulations are less stringent than what some states’ products liability courts have demanded. A company complies with federal regulations but still faces state tort suits for algorithmic bias. Should federal compliance shield the company from tort liability? If yes, does that mean unsafe algorithms can be legally compliant? If no, what incentive does federal regulation provide?

FAIR Analysis

• Adaptability: When does federal regulation complement tort liability, and when does it conflict?
• Flexibility: Should companies be able to choose between federal compliance and tort liability?
• Imagination: Design a regulatory scheme that would preempt tort liability while still protecting consumers from digital harms.
• Resilience: In a world of rapid digital innovation, should manufacturers be shielded by federal regulatory compliance, or does tort liability provide essential accountability? When regulation and tort liability conflict, who decides which one governs, and how should companies structure their compliance strategies when the answer is unsettled?
• Ethical Reflection: If a company’s legal team identified a gap between federal regulation and state tort liability, knew the gap existed and posed liability risk, and the company deployed its product anyway, what did they know, when did they know it, and what did they choose to do with that knowledge? Now consider your own position: if you were advising that company and saw the same gap, would you have recommended caution or deployment, and what does your answer reveal about the kind of advisor and leader you intend to be?

FAIR Lens: Week 5, Class 8 – Standards-Setting & Responsibility

Scenario

An autonomous vehicle manufacturer complies with all federal safety standards for automated driving systems, but the vehicle causes a crash that injures a pedestrian. The injured party sues, claiming the vehicle’s design is defective under state products liability law. The manufacturer argues that federal standards preempt the state claim. Who is right? Should it be?

FAIR Questions

• Flexibility & Adaptability: In a world of rapid digital innovation, should manufacturers be shielded by federal regulatory compliance, or does tort liability provide essential accountability?
• Imagination & Resilience: How would you design a system where federal regulation and state tort law work together to deter harm without freezing innovation?
• Adaptability: When regulation and tort liability conflict, who decides which one governs, and how should companies structure their compliance strategies when the answer is unsettled?

FAIR Lens: Week 5, Class 9 – Who Owes Responsibility to Whom?

Historical Perspective

In the early 20th century, if you bought a defective car from a dealer and were injured, you typically had no claim against the manufacturer, only against the dealer, because you had no contract with the manufacturer. MacPherson v. Buick changed that by allowing consumers to sue manufacturers directly. Now consider modern platforms: A user on a social media app is harmed by algorithmic amplification. The user has no traditional contract with the platform (they agreed to Terms of Service, but is that a “contract”?). Does the platform owe the user a duty similar to that owed by an automobile manufacturer?

FAIR Exploration

• Imagination: In MacPherson’s time, a user could inspect an automobile firsthand. Can a user of a modern platform inspect and understand the algorithm that governs their experience?
• Flexibility & Adaptability: How would you adapt the privity doctrine to digital platforms? Who counts as a “user” owed a duty?
• Resilience & Responsibility: If manufacturers owe duties to remote consumers (following MacPherson), what does a platform owe to its users? To investors? To society?

FAIR Lens: Week 6, Class 10 – Proving Negligence in Invisible Systems

Causation Puzzle

A user of a financial app makes poor investment decisions, resulting in significant losses. The user sues, claiming the app’s interface design negligently encouraged risky behavior. The app gave neutral recommendations; it didn’t tell the user to invest in risky assets. The app made aggressive investment options prominent. Did the app negligently cause harm, or did the user choose poorly?

FAIR Thinking

• Imagination: How would Palsgraf’s “foreseeability” test apply to algorithmic systems?
• Adaptability & Flexibility: Res ipsa loquitur assumes that if something goes wrong, we can infer negligence. Can we make that inference about a digital product?
• Resilience: In contexts where causation is hard to prove, how should liability be allocated?
• Adaptability: Who bears the burden of proving causation, the plaintiff or the defendant who has access to system logs? How should procedural doctrine adapt to correct the information asymmetry built into digital product litigation?

FAIR Lens: Week 6, Class 11 – Multiple Causes & Shared Responsibility

Causation Complexity

In a wrongful death case involving an autonomous vehicle, the crash resulted from: (1) the manufacturer’s algorithm design, (2) poor road conditions, (3) the pedestrian’s presence in a high-traffic zone, and (4) a mechanical failure of the vehicle’s brakes. Section 328D requires that the defect be a “substantial factor” in causing harm. But who caused the death, the manufacturer, the road authority, the pedestrian, or all of them?

FAIR Exploration

• Flexibility & Imagination: How should causation doctrine allocate responsibility when multiple factors contribute to harm?
• Adaptability: Section 328D was developed for simpler products. How should it apply to complex systems with many contributors?
• Resilience & Adaptability: When causation is genuinely shared across multiple parties and systems, how can tort law fairly allocate responsibility and incentivize safety in distributed digital environments?

FAIR Lens: Week 7, Class 12 – Truth in Algorithmic Marketing

Scenario

A company markets an AI hiring algorithm as “bias-free” and “objective.” In fact, the algorithm has been trained on historical hiring data that reflects past discrimination, and it replicates those patterns. Is this misrepresentation? Fraud? Or just “puffery” (exaggerated marketing claims)? If a company is more honest, “Our algorithm identifies patterns in hiring data”, does it avoid liability?

FAIR Reflection

• Imagination: What does truth require in the marketing of algorithmic systems? Can a company market AI as “objective” when all algorithms embed human choices? What would honest marketing of algorithmic capabilities look like, and what are the tradeoffs between aspirational marketing and transparency?
• Ethical Reflection: The company in our scenario knew its algorithm had been trained on discriminatory historical data before marketing it as “bias-free.” What did they know, when did they know it, and what did they choose to claim? Now step out of the case: imagine you work in marketing for an AI company, you know the algorithm embeds human biases, and you’re under pressure to make confident claims to compete. Faced with that same pressure and knowledge, what would you have claimed, and what does your answer reveal about your commitment to truth-telling as a business leader?
• Flexibility & Adaptability: How would you adapt the facts vs. opinions distinction to algorithmic claims?
• Resilience: In a world where AI marketing often makes broad capability claims, how should companies protect against fraud liability while still marketing their products?

FAIR Lens: Week 8, Class 13 – Warranties in Digital Contexts

Warranty Dilemma

A company sells AI software to businesses, claiming it “accurately identifies fraud with 98% confidence.” This is an express warranty. But “accuracy” is contextual; 98% correct is great for some applications and unacceptable for others. If a business buys software expecting fraud prevention and the algorithm misses a fraud case, has the company breached the warranty?

FAIR Questions

• Adaptability & Imagination: How would you adapt express and implied warranties to digital products that are constantly updated?
• Flexibility: Does the Magnuson-Moss Act apply to digital goods sold to businesses?
• Resilience & Adaptability: In the face of rapid changes in digital products, how can companies give meaningful warranties, and what institutional practices allow them to honor those commitments as products evolve?

FAIR Lens: Week 8, Class 14 – Disclaimers & Consumer Protection

Enforceability Question

• A software company’s Terms of Service include: “We disclaim all warranties, express or implied. We are not liable for any damages, including lost profits, personal injury, or data loss.” A user’s data is lost due to a software failure. The user sues; the company relies on the disclaimer. Is the disclaimer enforceable? Should it be? What does fairness require?

FAIR Exploration

• Flexibility & Imagination: Design fair limits on warranty disclaimers in digital products.
• Adaptability: How should Magnuson-Moss protections apply to digital goods and services?
• Resilience: If companies can disclaim all liability, what incentive structure is created for safety investment? How can liability law be structured to incentivize corporate safety practices and meaningful accountability when companies can legally opt out of responsibility?
• Ethical Reflection: When a company uses a legal right to disclaim all liability, when it can legally opt out but knows users cannot negotiate away that risk, what did the company’s legal team know about the fairness of that disclaimer, when did they know it, and what did they choose to enforce anyway? Now put yourself in that position as the legal counsel advising the company: would you recommend including it, what would you tell the company about the ethical risks, and what does your answer reveal about the kind of legal advisor you intend to be?

FAIR Lens: Week 9, Class 15 – Strict Liability’s Tradeoffs

Policy Debate

Greenman established strict liability because courts believed manufacturers should bear the cost of defective products, they’re best positioned to prevent harm, can spread the cost across consumers, and should be incentivized to design safely. Should AI companies be strictly liable for algorithmic harms? On the one hand, they have control over the algorithm. On the other hand, strict liability might discourage innovation.

FAIR Analysis

• Flexibility & Imagination: Design your own liability framework for digital products.
• Adaptability: How do Greenman’s rationales (loss-spreading, deterrence) apply to digital products?
• Imagination & Resilience: If strict liability discourages AI innovation, is that a cost worth paying to protect consumers? Design a liability framework that preserves incentives to innovate while ensuring companies cannot externalize the cost of foreseeable harm onto users.

FAIR Lens: Week 9, Class 16 – Redefining “Product” for Digital Era

Boundary Question

Is a social media algorithm a “product”? Traditional products liability applies to tangible goods. A social media platform delivers services (hosting, curation, connection) using an algorithmic tool. The algorithm itself is neither sold nor owned by users; it’s embedded in the service. If the algorithm causes harm, should strict products liability apply?

FAIR Exploration

• Imagination & Flexibility: How would you define “product” to encompass algorithmic systems?
• Adaptability: If the same algorithm is sold as a standalone tool vs. embedded in a platform, should liability be different?
• Adaptability & Resilience: Who should be liable when a digital product-service hybrid causes harm, and how should companies structure their offerings to manage liability exposure across the product-service boundary?

FAIR Lens: Week 10, Class 17 – Services, Products & Gray Zones

Classification Dilemma

A cloud computing company provides infrastructure services. A customer’s data is lost due to the cloud provider’s failure to implement adequate backups. Is this a products liability case or a contract case? The service is not a product; it’s infrastructure. But the customer relied on the provider’s promised uptime and data protection. Where does PL doctrine apply? Where does contract law govern?

FAIR Thinking

• Flexibility & Imagination: How should courts classify hybrid product-service transactions for liability purposes?
• Adaptability: When does a “service” embed a “product” sufficiently to trigger PL liability?
• Adaptability & Resilience: If a company provides both products and services, should it have different liability obligations for each, and how should legal doctrine adapt to address a commercial reality that did not exist when PL frameworks were built?

FAIR Lens: Week 10, Class 18 – Responsibility in Complex Systems

Multi-Party Scenario

A social media company acquires an AI moderation tool from a startup. The moderation tool was trained on biased data and causes discriminatory harm. Content creators sue. Who is liable? The acquiring company? The startup? The data scientists who trained it? In modern digital products, responsibility is often distributed across many parties. How should liability be allocated?

FAIR Questions

• Flexibility & Imagination: How would you reshape liability law to match modern technological reality, where responsibility is distributed?
• Adaptability: Does the traditional successor liability framework work for AI tools acquired as components?
• Resilience & Adaptability: When harm results from the interaction of many parties’ choices across a distributed digital supply chain, how should liability be allocated fairly, and how should legal doctrine adapt to match the reality of how modern products are built?

FAIR Lens: Week 11, Class 19 – Indemnification & Risk Allocation

Contract Complexity

A technology company uses components from third-party vendors to build its product. A defect in a vendor’s component harms users. The tech company faces liability but has an indemnification agreement requiring the vendor to pay damages. But the vendor is small and insolvent. Who ultimately bears the loss? Should courts enforce indemnification clauses as written, or intervene to prevent unjust outcomes?

FAIR Exploration

• Flexibility & Imagination: Design fair indemnification allocation among multiple parties.
• Adaptability: How should indemnification work when the vendor company becomes insolvent?
• Adaptability: Who should ultimately bear responsibility for harm caused by components in complex products, and how should the indemnification doctrine adapt when traditional corporate boundaries no longer reflect where the actual design and deployment decisions were made?

FAIR Lens: Week 11, Class 20 – Measuring Harm in Digital Contexts

Damages Dilemma

An algorithmic hiring tool discriminates against candidates, preventing qualified people from getting jobs. How much can they recover? Compensatory damages cover lost wages and emotional distress, but quantifying future lost wages is difficult when the counterfactual (what would have happened without the algorithm) is uncertain. Should punitive damages apply to discourage discrimination?

FAIR Reflection

• Imagination & Flexibility: Propose a framework for damages in digital harms.
• Adaptability: How do you calculate harm from algorithmic discrimination?
• Resilience & Adaptability: How can damages law fairly allocate responsibility while incentivizing safety, and how should damages frameworks adapt when the harm is algorithmic, statistical, and dispersed across populations rather than concentrated in a single plaintiff?

FAIR Lens: Week 12, Class 21 – Proportionality & Deterrence

Damages Scenario

An algorithm causes small harms to millions of users, say, $10 of financial loss per user due to algorithmic price discrimination. Total harm is $10 million, but each user is unlikely to sue individually. Should the company face $10 million in liability? If the company knows small-scale, dispersed harms won’t generate liability, what incentive does it have to prevent them?

FAIR Questions

• Imagination: Propose a damages theory that would deter small-scale, widely dispersed harms.
• Flexibility & Adaptability: How should damage frameworks adapt to digital economies where harm is often spread across many users?
• Imagination & Resilience: What combination of individual liability, class actions, and regulatory fines would appropriately deter harmful algorithms?
• Integrity: In Acosta v. Honda, reckless indifference to documented harm was sufficient to support punitive damages. Apply that standard here: if a company’s internal data shows its algorithm causes disproportionate harm to a protected class, and the company continues deploying it without correction, what did they know, when did they know it, and what did they choose to do with that knowledge? Is that reckless indifference, and should it trigger punitive exposure?

FAIR Lens: Week 12, Class 22 – Shifting Responsibility to Users

Defense Strategy

When a manufacturer raises a “misuse” or “assumption of risk” defense, it argues that the plaintiff, not the manufacturer, bears responsibility for harm. In what contexts is it fair to shift responsibility? When was the user’s conduct unforeseeable? When did the user have a clear warning? When did the user explicitly agree to risk-taking? In digital products, where algorithms are opaque, and users have limited alternatives, is it ever fair to shift responsibility?

FAIR Questions

• Flexibility & Imagination: Design criteria for when assumption of risk and misuse defenses are fair in digital contexts.
• Flexibility: What factors should determine whether responsibility lies with the manufacturer or the user,  and how should those factors flex when the product is algorithmic, the user’s choices were constrained by design, and the company controlled the information environment?

FAIR Lens: Week 13, Class 23 – Shared Responsibility in Digital Systems

Comparative Fault Scenario

A user falls victim to a cryptocurrency scam facilitated by the exchange’s platform. The exchange’s design made it easy to send money to external wallets without verification (platform fault). The user also ignored warning signs and didn’t use available security features (user fault). Under comparative negligence, both share responsibility. But how much to each? 50/50? 70/30? Should platform design be held to a higher standard because the company profits from and controls the system?

FAIR Reflection

• Flexibility & Imagination: Propose a comparative negligence framework for digital products that fairly accounts for power imbalances between platforms and users.
• Adaptability & Resilience: How should liability be allocated to incentivize both platform safety and user awareness?
• Adaptability: Who should have greater responsibility, the entity that controls the system or the individual using it? How should comparative negligence doctrine adapt when the power asymmetry between a platform and a user makes true “comparative” fault a fiction?

FAIR Lens: Week 14, Class 24 – Causation When Many Contributed

Market Share Scenario

Multiple social media algorithms amplify polarizing content and contribute to radicalization. A person radicalized online commits violence. Can the injured parties sue under market share liability, holding each platform liable for its share of the harm? Should they? What are the tradeoffs? Now anchor this hypothetical in a real verdict: In K.G.M. v. Meta (March 2026), a jury found Meta and Google negligent in the design and operation of Instagram and YouTube, and negligent in failing to warn. The jury was never asked whether either platform had a products-liability design defect. Consider whether the case was, at bottom, an Integrity case: what does the trial record show each company knew, and when?

FAIR Exploration

• Adaptability & Imagination: When harm emerges from the interaction of many platforms, who should bear responsibility, and can market share doctrine adapt to assign proportional liability across an AI industry where individual causation cannot be traced?
• Integrity: The complaint in Sindell alleged that the DES manufacturers knew or should have known the drug was carcinogenic and ineffective to prevent miscarriage, failed to test it for efficacy and safety, and kept marketing it as a miscarriage preventative anyway. What did they know, when did they know it, and what did they choose to do with that knowledge? As a future manager, what does this tell you about the relationship between what a company knows, what it chooses to do, and legal accountability?

FAIR Lens: Week 14, Class 25 – Integration & Looking Forward

Final Reflection

The course began with basic doctrine (what is a product? what is a defect?) and evolved to frontier questions (how should AI systems be governed? who is responsible for algorithmic harms?). Throughout, the FAIR Framework has been present: asking you to be flexible in applying old doctrine to new contexts, adaptable in your thinking, imaginative in envisioning solutions, and resilient in managing uncertainty, all grounded in Integrity, which asks not just what you can do, but whether you should.

Final FAIR Questions for Integration

• Flexibility: What are the core principles of products liability that should never change? What principles should flex?
• Adaptability: How have your views on manufacturer responsibility evolved over 14 weeks?
• Imagination: If you were to propose a single change to PL doctrine to govern digital products better, what would it be?
• Resilience: As a future business leader, how will you apply these lessons to make decisions about product safety, legal compliance, and ethical responsibility?
• Integrity: Looking back across the course, from Spruill to Liebeck to Sindell, what is the common thread in every case where a manufacturer faced the harshest consequences? What did those companies know, what did they choose, and what does that pattern tell you about the kind of leader you intend to be?