privacy

A read-aloud coach that listens and helps can raise reading levels, as one large pilot found. Work offline on a fixed library, process voice only to respond in the moment, and measure reading gains against a control.

A shared device has several users, some of them children, and no screen to confirm on. Lead with household coordination, make voice recognition optional, gate purchases and keep proactive prompts for the person they concern.

Continuous vision is useful and alarming to everyone the wearer looks at. Allow live vision only in sessions the wearer starts, enforce a visible indicator in hardware, refuse to identify people and lead with accessibility.

Memory helps until it surprises people. Make what the assistant remembers visible, editable and scoped, and treat sensitive categories differently by default.

A pre-ticked box gets more data and a regulator's attention. Decide the default, what applies retroactively, what withdrawal really means and who is never included.

At a low base rate even a good classifier mostly flags teens who are fine. Decide what triggers an alert, who reviews it, what the parent sees and what the teen is told.

A platform strategy question with hardware, privacy, cost and capability all in play. Principal-level answers design a split by task, with a privacy story users can understand and a plan for the devices that cannot run local models.

A regulated pipeline where the model reads documents but must not be the one deciding who gets an account. The interviewer wants extraction, checks against authoritative sources, fraud signals, human review and data minimisation.

A cost lever that can silently serve wrong answers and leak one user's data to another. The interviewer wants the failure modes and a design that keeps the savings where they are safe.

Stuffing every past conversation into context is expensive and still forgets. Decide what becomes a memory, how deletion cascades and how you test updates and forgetting.

Memory makes assistants feel personal and creates a new way to be wrong, creepy or unsafe. The strongest answers start with what should never be remembered.

Design task evidence, privacy controls and a separate diagnostic path without pretending that minimal telemetry proves semantic correctness.

India's Digital Personal Data Protection framework is now in force in phases. The interviewer wants the principles turned into product requirements, stated as of a date and checked with counsel.

The data flywheel is real, and so is the trust cost of getting consent wrong. The interviewer wants a decision framework: legal basis, contracts, user choice, de-identification and the value actually expected.

Users type sensitive data into chat boxes whatever the instructions say, and debug logs keep it forever. The interviewer wants immediate containment, a redaction design, and a balance between privacy and the ability to debug.

Separate disabling access from completing erasure, and explain what evidence supports each part of the deletion promise.

Self-declaration does not count in the UK, and US courts have upheld state age-verification laws. Combine age estimation with step-up verification, default to the safer experience and block where no compliant method exists.

Zero retention and abuse monitoring pull in opposite directions. Offer eligibility rather than a switch, automated review without storage, customer-held storage, and be honest about which models and features are excluded.

Data for free usage is a legitimate trade if it is explicit. Draw a hard line so production apps serving regulated users move to paid, and keep enterprise excluded by default.