Engineering trade-offs for user trust

𝐎𝐧𝐞 𝐥𝐞𝐬𝐬𝐨𝐧 𝐦𝐲 𝐰𝐨𝐫𝐤 𝐰𝐢𝐭𝐡 𝐚 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐞𝐚𝐦 𝐭𝐚𝐮𝐠𝐡𝐭 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐔𝐒 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫𝐬: Convenience sounds like a win… But in reality—control builds the trust that scales. We were working to improve product adoption for a US-based platform. Most founders instinctively look at cutting clicks, shortening steps, making the onboarding as fast as possible. We did too — until real user patterns told a different story. 𝐈𝐧𝐬𝐭𝐞𝐚𝐝 𝐨𝐟 𝐫𝐞𝐝𝐮𝐜𝐢𝐧𝐠 𝐭𝐡𝐞 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐰𝐞 𝐭𝐫𝐢𝐞𝐝 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠 𝐜𝐨𝐮𝐧𝐭𝐞𝐫𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞: -Added more decision points -Let users customize their flow -Gave options to manually pick settings -instead of forcing defaults -Conversions went up. -Engagement improved. Most importantly, user trust deepened. You can design a sleek two-click journey. But if the user doesn’t feel in control, they hesitate. Especially in the US, where data privacy and digital autonomy are non-negotiable — transparency and control win. Some moments that made this obvious: People disable auto-fill just to type things in manually. They skip quick recommendations to compare on their own. Features that auto-execute without explicit consent? Often uninstalled. It’s not inefficiency. It’s digital self-preservation. A mindset of: “Don’t decide for me. Let me drive.” I’ve seen this mistake cost real money. One client rolled out an automation that quietly activated in the background. Instead of delighting users, it alienated 20% of them. Because the perception was: “You took control without asking.” Meanwhile, platforms that use clear prompts — “Are you sure?” “Review before submitting” Easy toggles and edits — those build long-term trust. That’s the real game. What I now recommend to every tech founder building for the US market: Don’t just optimize for frictionless onboarding. Optimize for visible control. Add micro-trust signals like “No hidden fees,” “You can edit this later,” and toggles that show choice. Make the user feel in charge at every key step. Trust isn’t built by speed. It’s built by respecting the user’s right to decide. If you’re a tech founder or product owner, stop assuming speed is everything. Start building systems that say: “You’re in control.” 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐚𝐭 𝐜𝐫𝐞𝐚𝐭𝐞𝐬 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐬𝐭𝐢𝐜𝐤𝐬. 𝐖𝐡𝐚𝐭’𝐬 𝐲𝐨𝐮𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐰𝐢𝐭𝐡 𝐭𝐡𝐢𝐬? 𝐋𝐞𝐭’𝐬 𝐝𝐢𝐬𝐜𝐮𝐬𝐬. #UserExperience #ProductDesign #TrustByDesign #TechForUSMarket #businesscoach #coachishleenkaur LinkedIn News LinkedIn News India LinkedIn for Small Business

Trust in AI isn't a PR problem. It's an engineering one. Public trust in AI is falling fast. In the UK, 87% of people want stronger regulation on AI and a majority believe current safeguards aren't enough. We can't rebuild that trust with ethics statements, glossy videos, or "trust centers" that nobody reads. We need to engineer trust into AI systems from day one. That means: Designing for transparency and explainability (not just performance) Piloting high-benefit, low-risk use cases that prove value (and safety) Embedding value-alignment into system architecture using standards like ISO/IEEE 24748-7000 Engineers can no longer afford to be left out of the trust conversation. They are the trust conversation. Here’s how: 🔧 1. Value-Based Engineering (VBE): Turning Ethics into System Design Most companies talk about AI ethics. Few can prove it. Value-Based Engineering (VBE), guided by ISO/IEEE 24748-7000, helps translate public values into system requirements. It’s a 3-step loop: Elicit values: fairness, accountability, autonomy Translate into constraints: e.g., <5% error rate disparity across groups Implement & track across dev lifecycle This turns “fairness” from aspiration to implementation. The UK’s AI Safety Institute can play a pivotal role in defining and enforcing these engineering benchmarks. 🔍 2. Transparency Isn’t a Buzzword. It’s a Stack Explainability has layers: Global: what the system is designed to do Local: why this output, for this user, right now? Post hoc: full logs and traceability The UK’s proposed AI white paper encourages responsible innovation but it’s time to back guidance with technical implementation standards. The gold standard? If something goes wrong, you can trace it and fix it with evidence. ✅ 3. Trust Is Verifiable, Not Assumed Brundage et al. offer the blueprint: External audits and third-party certifications Red-team exercises simulating adversarial misuse Bug bounty-style trust challenges Compute transparency: what was trained, how, and with what data? UK regulators should incentivise these practices with procurement preferences and public reporting frameworks. This isn’t compliance theater. It’s engineering maturity. 🚦 4. Pilot High-Impact, Low-Risk Deployments Don’t go straight to AI in criminal justice or benefits allocation. Start where you can: Improve NHS triage queues Explainable fraud detection in HMRC Local council AI copilots with human-in-the-loop override Use these early deployments to build evidence and public trust. 📐 5. Build Policy-Ready Engineering Systems Public trust is shaped not just by what we build but how we prove it works. That means: Engineering for auditability Pre-wiring systems for regulatory inspection Documenting assumptions and risk mitigation Let’s equip Ofcom, ICO, and the AI Safety Institute with the tools they need and ensure engineering teams are ready to deliver. The public is asking: Can we trust this? The best answer isn’t a promise. It’s a protocol.

Effective AI augmentation of human decision-making requires clarity on the specific role of AI relative to humans. An interesting research study used two different AI agents - ExtendAI and RecommendAI - each optimized to play different roles in a financial investment decision process. The findings give useful insight into both the design of AI tools to augment human decisions, and how we deliberately choose to use AI to enhance our decision competence. 🧠 ExtendAI encourages self-reflection and informed decisions. Participants who used ExtendAI—an assistant that builds on users' own rationales—spent more time reflecting and revising their plans. They made 23.1% of trades that diverged from their original ideas, showing that feedback embedded in their own reasoning helped identify blind spots and improve diversification and balance. ⚡ RecommendAI sparks new ideas with low effort. RecommendAI, which directly suggests actions, led to a 45% adoption rate of its recommendations. It was perceived as more insightful (67% vs. 52% for ExtendAI) and easier to use, requiring half the time (8.6 vs. 17.5 minutes) compared to ExtendAI. 🧩 Feedback format impacts trust and comprehension. ExtendAI’s suggestions, interwoven into the user's rationale, were found easier to verify and interpret. Participants felt more in control (76% vs. 71% trust) and reported that it “supports how I’m thinking” instead of dictating actions. RecommendAI, by contrast, sometimes felt like a “black box” with unclear reasoning. 🌀 Cognitive load differs by interaction style. Using ExtendAI imposed more cognitive effort—an average NASA-TLX score of 57 vs. 52.5 for RecommendAI—due to the need for upfront reasoning and engagement with nuanced feedback. This reflects the trade-off between deeper reflection and ease of use. 💡 Users want AI insights to be both novel and relatable. Participants valued fresh insights but were most receptive when suggestions aligned with their reasoning. ExtendAI sometimes felt too similar to user input, while RecommendAI occasionally suggested strategies users rejected due to perceived misalignment with their views or market context. 🧭 Decision satisfaction and confidence diverge. Despite feeling more confident with RecommendAI (86% vs. 67%), participants reported higher satisfaction after using ExtendAI (67% vs. 43%). This suggests that while direct suggestions boost confidence, embedded feedback might lead to decisions users feel better about in hindsight. More coming on AI augmented decision making.

9 real hard truth of AI Agents & RAG 95% who talk about AI/RAG have not deployed one in production Here’s what actually bites you when real users show up. 1️⃣ 𝗦𝗺𝗮𝗿𝘁 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 > 𝗕𝗶𝗴𝗴𝗲𝗿 𝗠𝗼𝗱𝗲𝗹𝘀 → Send routine calls to small, cheap models; escalate to an LLM only when complexity or confidence demands it. → Quantise, cache and batch before you think about new GPUs. → Typical result: 70 % cost drop, 50 % lower latency, same UX. 2️⃣ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 • 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 • 𝗖𝗼𝘀𝘁 - 𝗽𝗶𝗰𝗸 𝘁𝘄𝗼 → You can’t maximise all three. Agree with product which axis bends. → Build SLOs, dashboards and alerts around that decision. → Clear trade-offs prevent endless scope creep. 3️⃣ 𝗗𝗮𝘁𝗮 & 𝗗𝗿𝗶𝗳𝘁 𝗻𝗲𝘃𝗲𝗿 𝘀𝗹𝗲𝗲𝗽 → Documents change, policies change, embeddings grow stale. → Schedule re-chunking / re-embedding; lint the corpus for missing metadata and duplicates. → Fresh data beats fancy models every single time. → You need to have proper MLOps setup if using fine-tuned embeddings! 4️⃣ 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗳𝗶𝗿𝘀𝘁, 𝗠𝗼𝗱𝗲𝗹-𝘀𝗲𝗰𝗼𝗻𝗱 → Hybrid search (BM25 + vectors + metadata) is default, not a “nice-to-have”. → Fine-tuned bi-encoders or rerankers regularly outperform generic embeddings. → Most “hallucinations” trace back to bad recall, not model weakness. 5️⃣ 𝗣𝗿𝗼𝗺𝗽𝘁𝗶𝗻𝗴 𝗶𝘀 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 → Treat prompts like code: version control, A/B tests, rollbacks. → Tiny edits often unlock 2–3× gains in speed or cost. → Prompt debt is as real as tech debt. 6️⃣ 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆  → Log every call: inputs, outputs, latency, spend. → Layered tests - prompt unit tests ➜ retrieval tests ➜ end-to-end. → Alerting, fallbacks and graceful degradation keep trust intact. 7️⃣ 𝗞𝗲𝗲𝗽 𝗮𝗴𝗲𝗻𝘁𝘀 𝗯𝗼𝗿𝗶𝗻𝗴 (𝗩𝗘𝗥𝗬 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗧) → Rule logic + model routing solves most workflows. → Multi-hop, self-reflective mega-graphs look cool - until you debug them at 3 a.m. → Minimum viable planning, maximum visibility. 8️⃣ 𝗧𝗲𝗮𝗺 & 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗴𝗹𝘂𝗲 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 → Data Eng, ML, Backend, DevOps, Product, Compliance—one roadmap. → Define clear SLAs on latency, uptime and error budgets. → On-call runbooks belong in the repo before launch, not after the first incident. 9️⃣ 𝗗𝗼𝗻’𝘁 𝗳𝗮𝗹𝗹 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗵𝘆𝗽𝗲 → Social-media “5-minute builds” last about 10 minutes in real life. → Production means weeks of instrumenting, hardening, and maintenance. → Build for your system: performance, compliance, and longevity beat viral demos every day. That’s a few of field-tested playbook. What scars or wins have you collected putting AI into production? 😅 -- ♻️ Repost to help others 🤗 ➕ Follow me - Shantanu for Production AI - ML - MLOps content and Career tips!

📸Meta’s request for camera roll access signals a critical inflection point in AI development—one that reveals the inadequacy of our current consent frameworks for both individuals and organizations. The core issue isn’t privacy alone. It’s the misalignment between how AI systems learn and how humans actually share. When we post a photo publicly, we’re making a deliberate choice—about context, audience, meaning. Camera roll access bypasses that intentionality entirely. Your unshared photos hold different signals: 📍 family moments 📍 screenshots of private conversations 📍 creative drafts 📍 work documents All of it becomes potential training data—without your explicit intent. For individuals, this shift creates three serious concerns: 1. Consent erosion — the boundary between “what I share” and “what gets analyzed” disappears 2. Context collapse — meaning is flattened when private data fuels generalized models 3. Invisible labor — your memories become unpaid inputs for commercial systems For organizations, the implications are just as pressing: 🔹 Data strategy: Companies must distinguish between available data and appropriate data. Consent isn’t binary—it’s contextual and evolving. 🔹 Long-term trust: The businesses that optimize for genuine user agency—not maximum data extraction—will be the ones that sustain real relationships and build better systems. Here’s a quick evaluation framework I use: ✅ Does this data improve the specific task the user requested? ✅ Could similar results be achieved with targeted, user-controlled input? ✅ Are we optimizing for system performance or user autonomy? The future of AI will be shaped by these choices. Not just what we can do with data—but what we choose to honor. We need systems that amplify human judgment, not bypass it. Design that aligns with consent, not convenience. The question isn’t just: can AI understand us? It’s: will it respect how we want to be understood? → How are you thinking about these trade-offs in your personal tech use? → And if you’re building AI—what frameworks are you using to balance capability with care? #AIethics #ConsentByDesign #RelationalAI #ResponsibleInnovation #MetaAI #DataGovernance #DigitalSovereignty #WeCareImpact

Ask any founder about their biggest failure, and they’ll point to the obvious one. Big failures get headlines. But the real collapse rarely gets noticed. Products rarely die from a single mistake. They bleed out in silence every time trust is traded for a shortcut. Betray your best users, lose your edge. Your earliest adopters do more than provide feedback or cheer from the sidelines. They troubleshoot, stretch your product, and set new expectations. When their needs go unmet, or when you break their workflows, you are losing the resilience that keeps your product alive in tough moments. Most teams only notice the damage after the fact, when those users are already gone. How you end matters as much as how you launch. Product migrations and sunsets are never just technical. Every missed detail, whether it’s a broken export, a lost file, or a confusing transition, creates another fracture in user trust. The companies that get this right pay attention to the small stuff and respect what users built with them. The ones that treat it as a checklist always leave a mess behind. A clean, clear ending tells your customers that their time and work mattered. Chasing breadth, losing depth. Expanding into new markets or adding features can look like progress. What usually happens is you lose the discipline and detail that made people care in the first place. Winning new jobs means putting in more effort, not less. Most teams spread themselves thin and become forgettable. The teams that win stay focused long enough to build depth users can’t find anywhere else. Friction is a slow exit. Forced signups and hidden paywalls push users to start searching for alternatives, even if they don’t leave right away. The short-term gains from adding friction almost always come at the cost of long-term loyalty. In the end, trust is what keeps people around. Lose it, and all you’ve done is start a countdown for your competitors. The pattern repeats: The slow decline of a product begins each time trust is traded for a shortcut or a quick win. Protect user trust as fiercely as you fight for every launch or metric. The best teams never make their top users regret the energy or belief they put into the product.

→ A secret every system designer whispers at 2 AM • Imagine your database in the middle of a network blackout.   • One choice will keep data consistent. Another will keep the service alive.   • You cannot have both. Not always. Not at scale. → What CAP actually says (short and clear) • C = Consistency. All nodes see the same data at the same time.   • A = Availability. Every request gets a response: success or failure.   • P = Partition Tolerance. The system keeps working even when nodes can’t talk.   • Pick two. Lose or weaken the third. That’s the heart of CAP. → Why it matters now • Cloud is distributed by default.   • Edge and mobile add more partitions.   • Choices made early shape outages, data bugs, and user trust.   • Trade-offs translate to real user pain. → Practical choices (no fluff) • CP systems (e.g., many relational setups): favor correctness. Good for payments and ledgers.   • AP systems (e.g., some NoSQL stores): favor uptime. Good for social feeds and caches.   • CA is only possible when partitions are impossible, unrealistic at scale. → Rules of the game • Define your invariants first. What must never be wrong?   • Decide acceptable inconsistency windows. Can eventual consistency work?   • Design for failures. Test partitions. Observe behavior under chaos. → Final thought • CAP isn’t a law to fear.   • It’s a lens to make choices visible.   • Use it to design systems that match your users’ priorities. follow Sandeep Bonagiri for more insights

From Hype to Trust: Why Responsible AI Will Define the Next Decade Is Your AI Strategy Building Trust—or Risking It? 87% of executives say Responsible AI is essential. Only 15% feel ready to implement it. That gap isn’t just technical—it’s strategic. As someone who’s driven AI transformations across multinational banks and government institutions, I’ve seen firsthand that responsible AI isn’t a luxury—it’s a competitive edge. New research shows that responsible AI features like privacy, auditability, and transparency drive higher adoption than even price or performance in financial products. Adoption jumped from 2.4% to 63.19% when these features were included in AI-driven pension apps. That’s not compliance—it’s business impact. Here's why this matters now: Ethical shortcuts are tempting in the race for AI dominance. But the future belongs to leaders who design AI that’s not just smart but trusted. A.   Privacy-first models now outperform over-engineered, data-hungry alternatives. B.     Hybrid AI systems—like Apple’s on-device+cloud model—balance innovation with integrity. C.    Responsible AI is becoming the new tech insurance against reputational damage and regulatory backlash. As CIOs and tech leaders, we must shift from building AI systems that can do everything to building systems that should. Key Takeaways for Tech Leaders: 1. Design ethics in, not bolt it on. Responsible AI must be part of your tech DNA. 2. Run trade-off experiments. Let your users tell you what matters most: privacy or personalisation, speed or auditability. 3. Turn ethics into brand value. Make your responsible AI strategy loud, visible, and credible. What’s your approach? How are you integrating ethics into your AI initiatives? What trade-offs are you navigating in your product or policy design? Let’s build a better AI future—together. Share your thoughts #ResponsibleAI #CIOLeadership #DigitalTransformation #AICompliance #TechEthics #FutureOfAI #InclusiveInnovation #AITrust #BoardroomTech #AITrends #TechLeadership #DigitalTransformation #AICompliance #TrustByDesign #FutureOfAI #CIOAgenda #EthicalInnovation #AIRegulation #RAIStrategy

𝗠𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝘁𝗵𝗶𝗻𝗸 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗶𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗲𝗿𝘃𝗲𝗿𝘀, 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀, 𝗮𝗻𝗱 𝗳𝗮𝗻𝗰𝘆 𝗯𝘂𝘇𝘇𝘄𝗼𝗿𝗱𝘀. But the real game? It’s about decisions under pressure. Let me spill the truth 👇 A few months back, I was breaking down the architecture of a food delivery app (think Zomato/Swiggy). Sounds cool, right? Scale, millions of users, orders flying every second. 𝗕𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: The toughest part wasn’t handling traffic. It was answering this one question: 👉 What do you do when 10,000 people order Biryani, but the restaurant has only 50 plates left? Now pause. If you were the system architect, what would you pick?  • Cancel orders → keep data consistent, but piss off customers.  • Accept orders → stay available, but issue refunds later. This is where the CAP Theorem slaps you in the face. Because in the real world, you can’t have everything, you trade pain. 𝗔𝗻𝗼𝘁𝗵𝗲𝗿 𝘀𝘁𝗼𝗿𝘆: While working on rider assignment logic, I realized: This isn’t about coding APIs. This is literally graph matching + human psychology. Assign too many deliveries to one rider → late orders → bad UX. Assign too few → idle riders → wasted money. 𝗕𝗮𝗹𝗮𝗻𝗰𝗲 𝗶𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. The biggest lesson? System Design is not about tools. It’s about trade-offs:  • Consistency vs. availability  • Cost vs. latency  • Perfection vs. shipping fast And honestly… it’s the same with life. You can’t optimize everything. But you can decide what to sacrifice. So, next time you hear “System Design”, don’t just think microservices or databases. Think like a business owner: “What’s the pain I’m willing to take, and what’s the pain I’ll never accept?” That’s the mark of an engineer who thinks like a leader. So tell me honestly, if you were designing Swiggy/Zomato, Would you prioritize availability (take all orders) or consistency (reject some)? Drop your answer 👇 Curious to see who thinks like a dev vs who thinks like a founder. #systemdesign #zomato #swiggy #dsa #softwarearchitecture #developer Software Data Structures Algorithms Architecture Software as a Service (SaaS)

Earning Users’ Trust with Quality When users interact with an AI-driven product, they may not see your data pipelines, but they definitely notice when the system outputs something that doesn’t make sense. Each unexpected error chips away at credibility. Conversely, consistently accurate, sensible recommendations gradually build lasting trust. The secret to winning that trust? Prioritize data quality above all else. How data quality fosters user confidence: Consistent performance: Reliable data inputs yield stable outputs. Users become comfortable knowing the AI rarely “goes rogue” with bizarre suggestions. Predictable behavior: High-quality data preserves known patterns. When the AI behaves predictably—reflecting real-world trends—users can rely on it for critical tasks. Transparent provenance: Even if users don’t dig into the data details, they appreciate knowing there’s a rigorous process behind the scenes. When you communicate your governance efforts—without overwhelming them—you reinforce trust. Error mitigation: When anomalies do appear, high-quality data pipelines often include fallback mechanisms (e.g., default rules, human-in-the-loop checks) that stop glaring mistakes from reaching end users. Consequences of ignoring data quality: User frustration: Imagine an e-commerce AI recommending out-of-stock products or the wrong sizes repeatedly. Frustration mounts quickly. Brand erosion: A few high-profile misfires can tarnish your company’s reputation. “AI that goes haywire” becomes a memorable tagline that sticks. Decreased adoption: Users who lose faith won’t invest time learning or relying on your platform. They revert to manual processes or competitor tools they perceive as more reliable. Building user trust isn’t a one-time effort; it’s continuous vigilance. Regularly audit your data sources, validate inputs, and refine processes so your AI outputs remain solid. Over time, this dedication to data quality cements confidence, turning skeptics into loyal advocates who believe in your product’s reliability.