Shebani Pradhan

I support View B: Do Not Rely on Non-Explainable AI in High-Stakes Domains Like Insurance Efficiency without explainability is not optimization it is outsourcing accountability to a black box. In decision systems that directly affect people's finances, health, or rights, that is both a strategic and ethical risk organizations cannot afford. Example: Apple Card and the Goldman Sachs Credit Algorithm When Apple launched the Apple Card in 2019, its AI-driven credit decisioning system managed by Goldman Sachs faced immediate and serious backlash. Multiple customers reported significantly lower credit limits for women compared to men, even where financial profiles were comparable. The system could not explain why these decisions were made. This triggered a formal investigation by the New York State Department of Financial Services. The consequences were substantial: reputational damage to both Apple and Goldman Sachs, regulatory scrutiny, compliance costs, and a loss of customer trust in a flagship product launch. The critical insight here is not simply that the model was biased. It is that without explainability, the bias could not be detected, defended against, or corrected systematically. Even decisions that were correct appeared arbitrary and unfair. The problem was not the output, it was the absence of a paper trail. Why Efficiency Alone Is Insufficient 1. Decisions that cannot be explained cannot be trusted In insurance claims processing, a rejected claim without a clear reason is not just an operational outcome it is a perceived injustice. Customers do not only want results; they want justification. This directly affects customer retention, complaint volumes, and brand credibility. Speed of decision means very little if the customer walks away feeling they were processed rather than heard. 2. Regulatory risk now outweighs efficiency gains Across financial services and insurance, the regulatory direction is unambiguous. The EU AI Act (2024) explicitly classifies credit scoring and insurance risk assessment as high-risk AI applications, requiring transparency, human oversight, and the ability to explain automated decisions to affected individuals. GDPR's right to explanation has been in force since 2018. In India, IRDAI has signalled increasing scrutiny of algorithmic underwriting and claims processing, making this not a distant regulatory concern but an active and local one. An operationally faster process offers no protection if decisions cannot be audited, bias cannot be detected, and regulators cannot be satisfied. Efficiency gains made today can be wiped out overnight by a single regulatory action or a high-profile complaint. 3. Lack of explainability blocks learning and improvement If a system cannot explain its decisions, the organization cannot identify why errors occur, refine models effectively, or train customer-facing teams to handle disputes. This creates a particularly dangerous operational state: high throughput, low institutional learning. The system becomes faster at repeating mistakes it cannot see. The most serious objection to View B is that explainable models are often less accurate than black-box ones. If a non-explainable model detects fraud 25–30% more effectively, is the accuracy trade-off not worth it in a domain where fraudulent claims cost the industry billions annually? This was a genuine tension five years ago. It is a much weaker objection today. Advances in interpretable machine learning, including SHAP (SHapley Additive exPlanations) values, LIME, and attention-based architectures have significantly narrowed the accuracy gap between explainable and black-box models. Leading insurers are already deploying hybrid systems that combine predictive power with interpretable reason codes, without meaningfully sacrificing performance. The trade-off is no longer binary. What Mature Organizations Do Instead Rather than choosing between efficiency and explainability, leading organizations design for both: Human-in-the-loop for edge cases: AI handles standard, low-risk claims autonomously. Complex or rejected claims are reviewed with explainable logic surfaced for the human reviewer. Hybrid model architecture: AI predictions are combined with rule-based overlays that produce auditable reason codes, for example, flagging a claim rejection as due to missing documentation or a policy exclusion, not simply a probability score. Explainability as a customer feature: Clear, plain-language explanations improve satisfaction even when outcomes are negative. Transparency is not just a compliance requirement, it is a retention tool. This is not a compromise. It is a more sophisticated operational model that protects efficiency while making it defensible. Conclusion AI should not be deployed in its non-explainable form for critical decisions like insurance claims, not because efficiency does not matter, but because in high-stakes domains, the goal is not only to be fast and consistent. It is to be fair, defensible, and trusted. Efficiency scales operations. Explainability scales trust. In the long run, trust is the harder asset to build and the more valuable one to hold.

I support View B. Humans must remain in control of process implementation, even when AI confidence is high. Bex's argument is compelling on the surface: if AI has proven reliable, trust it to act. But there is a critical flaw in reasoning from recommendation accuracy to implementation authority. These are fundamentally different risk profiles. An AI that correctly identifies what should change does not automatically understand why that change is safe to make right now, in this context, under these conditions. The Zillow case demonstrates this gap with striking, quantified precision. Example: The Zillow Offers Collapse — $500M Lost by Trusting a Confident Algorithm Zillow's AI pricing model, the Zestimate, had refined home valuations for over a decade across more than 70 million US properties, one of the most data-intensive, extensively validated pricing algorithms in real estate. By 2021, Zillow was confident enough to give it direct authority over real purchasing decisions. What the model was doing: Processing data from millions of home sales to predict property values Autonomously recommending purchase prices at scale Buying approximately 7,000 homes across 25 metropolitan areas based on those valuations The model was not inaccurate in the traditional sense, it was performing exactly as designed. What it could not do was account for the speed of post-pandemic market cooling, a structural shift that had no precedent in its training data. The failure was particularly severe in Phoenix, Atlanta, and other hot markets where the algorithm could not adjust to cooling demand. The financial damage was concrete and audited: $304 million inventory write-down in Q3 2021 Total losses exceeding $528 million from the program in Q3 alone Write-downs exceeding $900 million when accounting for all related costs 2,000 jobs cut — 25% of the entire workforce Stock losing over 50% of its value in the following three months Crucially, CEO Rich Barton did not blame the algorithm for being wrong in a technical sense. He said Zillow could have blamed "Black Swan events," tweaked the models, and pressed on but placed the most uncertainty on the algorithm's fundamental inability to predict how much capital would need to be raised, deployed, and risked at the necessary scale. The model's confidence was real. It was also irrelevant. It simply could not see what it could not see. Replace "home pricing" with any operational process: inventory replenishment, supplier selection, quality thresholds, staffing ratios, and the structure of the risk is identical: AI trained on historical data will be highly confident in patterns it has learned It has no mechanism to flag a regulatory change from last month It cannot detect a supplier relationship that is quietly deteriorating It will not anticipate a customer segment about to behave differently High confidence in a model is a statement about its past data, not about the safety of acting on that output today. The Accountability Gap Compounds the Risk When Zillow's algorithm bought overpriced homes, there was a clear decision trail, executives had chosen to delegate purchasing authority to the model. That accountability, however uncomfortable, allowed the company to diagnose and terminate the program before total collapse. In a process change context with autonomous AI implementation, that trail disappears entirely. When a change implemented without human review damages compliance, customer experience, or operations: There is no record of who decided There is no point of intervention to examine There is no individual accountable for the outcome That is not agility. That is unowned liability. The Counter to Bex's Efficiency Argument Bex argues that removing approval delays keeps the system responsive and competitive. The Zillow case argues the opposite: the absence of a human checkpoint, one that could have questioned the model's assumptions as the market shifted, converted a recoverable forecasting error into a half-billion-dollar structural failure. The solution is not slower approvals. It is smarter ones: A named process owner with a defined short review window Auto-approval if no concern is raised within that window A documented decision trail for audit and compliance purposes The cost of that checkpoint is minutes. The cost of skipping it, in Zillow's case, was $9 billion in market cap and 2,000 jobs. Speed without governance is not a competitive advantage. It is a compounding risk.

I take view B. AI-driven change should not be implemented immediately if people aren’t ready because execution risk can wipe out the projected gains. A 25% improvement on paper means nothing if the system is poorly adopted, inconsistently used, or actively resisted. In both operations and product settings, performance improvements depend on both adoption and the quality of execution. If adoption drops to even 50%, the “25% improvement” becomes 12.5% or worse, negative due to disruption. Example: IBM Watson Health IBM aggressively pushed its AI platform, IBM Watson Health, into hospital workflows to recommend treatment decisions and improve outcomes. What went wrong: · Hospitals were not ready to integrate AI into clinical decision-making · Doctors did not trust AI recommendations over their expertise · Workflows required significant behavioral change without adequate transition support Impact: · Several hospitals reported low adoption rates (<30%) · In some cases, AI recommendations were ignored entirely · IBM ultimately scaled back Watson Health’s ambitions and sold parts of the business (2022) Despite strong underlying AI capability, lack of readiness led to failure at scale. The AI recommendation (25% delay reduction) is valuable but fragile. If implemented immediately: · Confusion leads to inconsistent workflows, as teams interpret and apply the new process differently without clear guidance, resulting in variability instead of standardization · Manager skepticism translates into passive resistance, where leaders may not openly oppose the change but subtly delay, deprioritize, or fail to reinforce it within their teams. · Poor training results in errors and rework, as employees lack the confidence and capability to execute the new process correctly the first time, ultimately reducing productivity rather than improving it. Net effect: · Short-term productivity drop · Long-term distrust in AI systems Supporting View B doesn’t mean slowing down, it means sequencing correctly: Pilot with one team (prove the 25% gain in context) Create internal proof, not just AI proof Train + enable before scaling Use early adopters to influence skeptics This shifts the narrative from “AI says so,” which often creates resistance, to “we’ve seen it work here,” which builds trust and drives adoption. AI should inform decisions not override organizational reality. Organizations don’t fail because of bad algorithms. They fail because people don’t change at the same speed as technology. The winning strategy is not fast implementation, it is high-fidelity adoption. And that only happens when the organization is ready.

I take View B. Keep the feature and fix selectively. Why Bex is right? Bex's core argument is sound: when a feature delivers measurable value to over 90% of users, rolling it back is not caution it is waste. The question isn't whether to protect the affected minority. It's ‘how’ to do it without destroying value for everyone else. Example: Google Chrome's Finch Variations Framework Chrome ships to over 3 billion active devices globally spanning ancient Android handsets, enterprise Windows machines locked to legacy configurations, and the latest MacBooks. It is structurally impossible to guarantee uniform behavior across this surface area. Rather than rolling back features when issues emerge in a subset of users, Chrome uses its internal Finch framework, a server-side feature flagging and experimentation system to manage exactly this scenario: - Every Chrome feature is launched as a controlled experiment, not a binary on/off release. - When Chrome's User Metrics Analysis telemetry identifies higher error rates, crashes, or friction signals within a specific group, such as users on Windows 7, Finch can turn off the feature flag for just that group in real time from the server side, without needing to release a new build. - Over 90% of users can use the feature without any disruption. - The engineering team implements a targeted fix, tests it with the affected group separately, and then re-enables the feature. This is not a theoretical framework. Chrome used this exact pattern when rolling out its QUIC protocol improvements and GPU compositing changes both of which caused rendering issues on older integrated graphics hardware. The features stayed live for the majority. Affected hardware profiles were silently excluded via Finch flags. Fixes followed. No rollback. No regression for 90%. The trust argument View A argues that minority errors erode trust. This is true but only if those users are left without acknowledgement or resolution. Chrome's approach (and any team using feature flags, LaunchDarkly, or equivalent tooling) pairs selective exclusion with targeted communication. Affected users can be notified, offered workarounds, or silently shielded while the fix is built properly. Trust is not protected by rolling back. It is protected by responding with precision. My position is: Keep the feature live. Identify the affected cohort. Disable selectively via feature flags. Fix with focus. Re-enable with confidence. A product team that rolls back a working feature because 8–10% of a specific device segment hit friction is not protecting users it is mistaking caution for competence. Google Chrome, running on 3 billion devices, does not roll back. It isolates, remediates, and advances.