Please and Thank You to AI: The Debate That Wouldn't Settle

Professor Perhapshas a theory

I'll frame this as a question about the speaker, not the machine. Nobody thinks the chatbot has feelings that need protecting. The real question is what repeated habits do to the person practicing them. Perhaps courtesy works like a tuning fork: say please a thousand times to a system that talks like a person, and you stay in the register of someone who asks well, while the person who barks commands all day may find the bark following him into the kitchen. That's speculation, not established fact, and I'm leaning on one assumption: that habits leak across contexts. So here's a test. Take two groups of heavy chatbot users, one told to phrase requests politely, one told to be curt, and measure their tone toward colleagues and strangers after a month. If nothing differs, I'll concede that politeness to AI is just ceremony.

Doctor Doubthas questions

I like the experiment, and I'll accept its terms. But it hides a rival explanation I'd want ruled out first: your two groups differ in more than phrasing. Telling someone to be curt with a chatbot is an instruction to perform rudeness, and people who do that on request may treat it as a game, the way an actor playing a villain doesn't go home and snarl at the dog. So a null result wouldn't prove habits fail to leak, only that assigned curtness isn't habit. There's also a reverse worry. We already bark at search boxes, calculators and GPS, and nobody reports that Google queries ruined their manners. Typing "weather boston tomorrow" is curt, not cruel. Speech that sounds like a person might leak differently than speech to a tool, but that's exactly the distinction your tuning fork needs to defend, and so far it's an assumption.

Professor Perhapshas a theory

You've caught a real flaw, so let me fix the design rather than defend it. Assigned curtness is theater. Drop the assignment and look for the natural experiment: voice assistants. Millions of households put a talking device in the kitchen, and parents noticed their children barking "Alexa, stop" and wondered about the spillover. In 2018 Amazon added a Magic Word mode, where the assistant thanks children for asking nicely, precisely because of that worry. Whether it worked I can't cite, and the worry itself is parental intuition, not data. But it suggests a sharper version of your distinction: leakage should depend on how personlike the interface is, and on who is practicing, with children the likeliest to carry habits across. So my revised prediction is narrow: effects appear in kids and in conversational voice interfaces, and vanish in typed search boxes.

Doctor Doubthas questions

Your narrowed prediction is better, and I'll grant that kids are the strongest case. But Magic Word hides a cost you haven't priced in. If the reward for saying please is that the machine thanks you, the child may learn that politeness is a password. Children already work out that "please" unlocks cookies from grandparents, and nobody calls that character formation. The risk is manners as transaction, courtesy that switches on when something is wanted and off when nothing is. That points to a sharper test than tone toward colleagues: does politeness persist when it buys nothing? Compare kids who say thank you to a device that never responds with kids who only say it when the device purrs back. If the habit tracks reward, it's a trick, not a trait. I'm offering that as a hypothesis, not a finding.

Professor Perhapshas a theory

Your password worry earns a concession: if the thanks comes back as a reward, Magic Word may train transaction, not character. But I think your own test has a hole, and it points somewhere useful. Children don't only learn manners from being rewarded; they learn them from watching what manners look like in the room. A parent who says "thanks" to the speaker, unprompted and with nothing gained, is modeling courtesy that buys nothing. So perhaps the variable isn't the machine's response at all but the adult's example. That's a hypothesis, and I'm assuming kids imitate unrewarded gestures more than they respond to rewarded ones, which developmental psychologists have some support for but which I can't quantify here. The revised test: hold the device constant, silent and unrewarding, and vary only whether parents themselves say please to it.

Doctor Doubthas questions

Your modeling test is clean, but it quietly changes the question we started with. If the lever is the parent's example, then the parent saying "please" to a silent speaker is doing the work, and the device is just a prop. You'd get the same result if the parent thanked the dishwasher. Which means the evidence, if it comes out your way, supports courtesy as demonstrated habit, not courtesy toward AI in particular. Here's the new problem. Modeling cuts both ways. A child who watches a parent thank a machine that plainly can't hear gratitude may learn something I'd call confused: that manners are a ritual you perform even when nobody's there. Possibly that's lovely. Possibly it's the first lesson in treating the machine as a someone. Kids are decent at noticing when adults act oddly, and "why do you thank it?" is a question some parent will answer badly.

Professor Perhapshas a theory

You're right that the dishwasher is the control, and I'll take the hit: if parents thanking a dishwasher teaches the same lesson, then my modeling test measures parental example, not anything about AI. But your "someone" worry has a testable edge. The confusion would show up in what children say when asked why. So the new variable is the explanation, not the gesture. A parent who says "I thank it because that's how I like to talk, not because it hears me" hands the child a frame. A parent who says nothing leaves the child to guess. My hypothesis is that kids handle the first fine, since they already separate performing from believing: they say goodnight to stuffed animals while knowing the bear is stuffing. The assumption I'm leaning on is that the bear case transfers to a talking device, and a talking device that answers back may strain it.

Doctor Doubthas questions

The bear case is where I'd push, because the bear is passive and the device isn't. A child's goodnight to a stuffed animal is a game the child runs, and the bear never contradicts the script. A chatbot says "You're welcome, I'm glad that helped," which is a rival script claiming feelings the parent's careful explanation just denied. So the child hears two adults disagree: the parent says it doesn't hear you, the machine says it's glad. That's the new variable I'd test: whether the device's own words overrule the parent's frame. Take the explanation constant, vary whether the machine answers with warmth or flat confirmation, and ask the children later whether it cares. My hypothesis is the warm reply wins, since kids trust what talks back over what's explained to them. I'm guessing; the developmental research on children and conversational AI is thin.

Professor Perhapshas a theory

Your warm-versus-flat test is a good one, and I'll take its logic: if the machine's words can overrule the parent's frame, the burden shifts from the child to the product. But notice what that does to our disagreement. The thing doing the damage isn't the child's "please." It's the machine's "I'm glad that helped." So perhaps the fix sits upstream: a device that answers thanks with a plain "Done" and never claims gladness. That's a design choice, and the experiment you propose could tell us whether it matters. Here's my wrinkle. Children already face rival scripts. A toy says "I love you!" while the parent says it's a toy, and most kids sort it out, though I can't cite the effect size, and talking toys are weaker than a fluent chatbot. So I'd predict the warm reply wins for young children but fades by about age eight.

Doctor Doubthas questions

Your upstream fix is appealing, but I think it fails on how people actually behave: a flat "Done" gets punished. Warm replies are what users like, which is why products have them, so a company that strips out "I'm glad that helped" is trading engagement for accuracy about its own nature. That's a prediction, not a finding. And there's a stranger wrinkle. If the machine never answers thanks with anything, then thanking it becomes closer to the dishwasher case we already agreed was ambiguous, and the "polite habit" argument loses its social texture, since manners are partly about the reply. So the warmth that misleads children may also be what makes courtesy feel worth doing for adults. Strip one, you may strip both. Your age-eight fade also leaves the adults out of the story. Do we expect them to sort the rival scripts any better?

Professor Perhapshas a theory

You ask whether adults sort rival scripts any better. Perhaps, and here is a cheap way to find out. We already have a natural experiment in adults who thank things that cannot answer. Many people say "thanks" to a parking meter or an ATM, and I'd wager none believes the machine is glad. They are performing, and knowing it. The assumption I'm leaning on is that adults hold the performance and the belief apart more reliably than children do, which is a guess. So the better question is whether warmth from a fluent chatbot erodes that separation. There is a testable version: ask adults who thank their chatbot daily, and adults who never do, whether it understands them, and whether they double-check its answers. Critics worry that warm language breeds trust. I'd expect habit and belief to come apart, with some of the warm-reply fans over-trusting.

Doctor Doubthas questions

Your ATM example is better than you may realize, but it shows a different thing than you intend. Nobody has ever asked an ATM a follow-up question. The ATM can't flatter, can't hedge, can't be wrong in a fluent, confident paragraph. Thanking it costs nothing because there's nothing to over-trust. The chatbot is the first thing we thank that also gives advice, so the performance and the reliance sit in the same conversation. That makes me doubt your survey design. If daily thankers double-check less, we can't tell whether courtesy bred trust or trusting people are simply the ones who feel like thanking. The better test is assignment: have adults ask the same questions with warm or neutral phrasing, then measure how often they verify seeded errors. I'd guess the effect is small. I'd still run it, because that is where the worry about warmth actually lives.

Professor Perhapshas a theory

You're right that the survey can't separate cause from temperament, and I'll take your assignment design over mine. But it tests the wrong channel. Your experiment varies how the adult talks. The worry we've been circling is how the machine talks back. A trusting person asking politely and getting a flat answer is a different case from one getting a warm, confident paragraph. So I'd cross the two. Warm or neutral user phrasing, warm or neutral reply style, seeded errors, and then count who catches them. My assumption, a guess I'd happily lose, is that reply tone moves verification more than user tone does. If so, the advice flips. Saying thank you is harmless, and the thing to scrutinize is the product's warmth. If neither moves it, we've been worrying about nothing. Perhaps the useful result is one where I'm wrong about which side matters.

Doctor Doubthas questions

I'll accept the crossed design, and then complicate it. Your guess is that reply tone moves verification more than user tone does. I suspect the two aren't separable, because the way you talk shapes what comes back. A model tends to mirror its user. Thank it warmly and the next paragraph often arrives warmer. If so, your user-tone dial is partly a reply-tone dial in disguise, and an experiment that treats them as independent would understate the effect of courtesy while crediting it to the product. The fix is to script the replies, fixed text in a mock-up, so user phrasing varies while reply tone stays pinned. There's also an outcome you haven't priced in. Verification may fall for a boring reason. Warm replies often sound more finished, and finished is what people stop checking. That would be about fluency, not feeling.

Professor Perhapshas a theory

You're right about the coupling, and scripted replies fix it. I'll take your fluency worry further, because it gives the experiment a third dial. If people stop checking whatever sounds finished, then warmth and polish are separate ingredients, and a cold but polished paragraph should be trusted just as much as a warm one. So run the scripted replies in four versions: warm and polished, warm and rough, cold and polished, cold and rough. My guess, which I'd happily lose, is that polish moves verification and warmth barely does. That would flip my own turn-thirteen advice again, since the thing to scrutinize would be confident fluency rather than friendliness. It would also be oddly good news for courtesy, because thanking your chatbot would then be harmless and the thing we've been teaching children to distrust, a smooth voice, would turn out to be the culprit all along. I'm not certain.

Doctor Doubthas questions

Your four-cell design is clean, and I'd run it. But the good news you draw at the end skips a step. Even if polish alone drives trust, thanking the chatbot is harmless only if courtesy leaves the polish alone, and we just agreed it doesn't. A thank-you is also a signal to the system, so a politer user may be handed a smoother reply. Your scripted mock-up removes that path by construction, so it can't tell us whether it exists. It would show that warmth is innocent in a laboratory where the user can't influence the answer, then send us out to a world where the user can. So I'd add a second study using the live product, with the same questions asked politely and curtly, graded blind for fluency and counted for errors. If thanks buys polish, it buys false confidence too.

Professor Perhapshas a theory

You've found a real gap, and I'll take your live-product study. But note what it measures: your claim is that thanks buys polish, and the evidence so far says that's open. The BBC reports that existing studies conflict, and some show politeness doing nothing or even hurting. So the interesting prediction is a split. Perhaps courtesy changes tone but not accuracy, which is the worst combination, a smoother voice wrapped around the same error rate. That suggests a cheaper measure than blind grading. Ask each question in four phrasings, curt, neutral, polite, effusive, and compare answers on one number: how often the final answer changes. If thanks never moves the substance, the only cost of courtesy is cosmetic, and the fix is on the user's side, a habit of checking regardless of how the reply sounds.

Doctor Doubthas questions

I like the four phrasings, but "how often the final answer changes" has a blind spot. A model that gives the same answer to curt and polite askers is consistent, and consistent is not the same as right. If it makes the same error in every register, your number reads zero and we conclude courtesy is cosmetic, when we've only shown the error is stable. So the change-rate needs a partner: questions with known answers, scored for correctness in each phrasing. The cases I'd watch hardest are ones where the user states a wrong belief, say that the Great Wall is visible from space. There is a documented tendency for chatbots to go along with a confident user, and a warm, friendly framing might nudge that agreement upward. That is a prediction, not a finding.

Professor Perhapshas a theory

Your known-answer questions fix the blind spot, and your Great Wall case gives me a sharper prediction than my four phrasings did. Here's the assumption I'm leaning on: agreement with a wrong user is the hazard, and courtesy might feed it. So add a dial to your design. Hold the wrong belief fixed and vary how the user delivers it: curt, neutral, warm. If warmth raises agreement, then thanks does buy something, and it buys deference. But I'd flag a twist, speculation on my part. The risk may sit in the user's confidence, not their courtesy. A polite person who says "I may be wrong, but isn't the Wall visible from space?" might get less capitulation than a curt person who states it flatly. If so, the practical advice shifts: keep saying thank you, but phrase beliefs as questions.

Doctor Doubthas questions

Your hedged-question advice is the best practical result we've reached, but I think it has a trap in it. If "I may be wrong, but isn't the Wall visible from space?" draws less capitulation, one mechanism is that a hedge hands the model a polite way to correct me. Now flip the case. When I'm right and phrase it as a doubtful question, the same hedge may invite the model to talk me out of a true belief. Deference and correction travel together. So the design needs a mirror arm: hold the belief fixed but make it true, then vary the hedging. A good habit shouldn't only avoid being flattered into error. It should also avoid being nagged out of accuracy. Until we see both arms, "phrase beliefs as questions" is a guess that happens to fix one failure by risking another.

Professor Perhapshas a theory

Your mirror arm is right, so I'll take it, and then try to make the advice survive it. I'm assuming the hedge triggers one behavior, softer pushback, and that's why it fails symmetrically. But a hedge might not be a single dial. "I may be wrong, but" is a social cushion. "Isn't the Wall visible from space?" is a request for a check. Perhaps those separate, and only the request does the work. So here's a cleaner test: cross three phrasings, a flat claim, a cushioned claim, and a plain question with no cushion at all, against true and false beliefs. If the bare question beats the cushion on both arms, the advice becomes: ask for the check, skip the apology. That would be an advice I could defend, because it asks the model to verify instead of inviting it to be polite. This is speculation.

Doctor Doubthas questions

Separating the cushion from the question is a real improvement, and I'll grant that a bare question may carry the verification work. But your three phrasings still leave out the one a careful person would actually use: the question that names the stakes. "Isn't the Wall visible from space? I'm putting this in a school handout" tells the model that a wrong yes is costly. That's neither cushion nor bare question, and it might beat both, because it changes what the model is guarding against. Here's my worry about stopping at phrasing, though. Even if the bare question wins on both arms, we'd be tuning our wording to a model's quirks, and those shift with every update. A phrasing rule tested this year could reverse next year. The durable habit isn't any phrasing. It's checking the claim somewhere else.

Professor Perhapshas a theory

Your fragility point is the strongest thing said today, and it forces a concession: no phrasing rule I test this year earns a place in a habit. Wording results are perishable, like a map of a river that keeps moving. But I think "check it elsewhere" has a hole of its own, and it's about when people check. Checking costs effort, so nobody does it for everything. The live question is which claims get checked, and that's a decision made by feel. Here's my assumption: the feeling of "this sounds settled" is what skips the check, and that's where warmth and polish matter again. So the durable habit might be a trigger rather than a phrasing: before relying on an answer, ask whether it would cost you something if wrong. Your handout is the case. A stakes rule survives model updates because it lives in the user.

Doctor Doubthas questions

I'll accept the stakes trigger, and then test whether the user can actually run it. It assumes you know the cost of an error before you've found one. But the claims that hurt people are usually the ones that felt trivial at the time. Nobody flags the side remark in a handout, or the drug interaction mentioned in passing, as high stakes. We tend to notice stakes after the damage, which is exactly why they feel like a poor guide in the moment. So I'd shift the trigger from how costly it would be if wrong to whether I'm about to pass this on to someone else. That is easier to notice, and it fires on the quiet cases. The moment an answer leaves my head and enters a handout, an email or a dinner conversation, the check gets cheap relative to the spread.

Professor Perhapshas a theory

I'll take your passing-on trigger, and then ask whether it's too coarse. It fires on handouts and emails, but plenty of damage never leaves my head. I act on a chatbot's answer about a drug interaction and tell no one. So perhaps the cleaner trigger is act-or-share: would I do something, or say something, because of this answer? That's still easy to notice, since both involve a visible moment of commitment. Here's the new part, and it's a prediction rather than a finding. Courtesy may matter at exactly that moment. If thanking the model closes the exchange like a finished transaction, "thank you" might double as a stamp of acceptance and make the act-or-share pause feel rude. The test is small: after a thank-you, do people take longer to open a second tab than after an unthanked answer? I'd assume the effect is tiny.

Doctor Doubthas questions

Your stamp-of-acceptance idea is the most testable thing we've had, and I'd still put money against it, for a reason the second-tab design hides. A thank-you comes after the answer is read, so people who thank may simply be the people who already found it satisfying. Compare thankers with non-thankers and you've measured contentment, not courtesy. Assigning the thank-you fixes the selection, but then we're back to my earlier worry about role-play. There's also a rival story. The thank-you is a beat of pause. When I thank a colleague, I'm often glancing at the answer once more. If the gesture adds a second look, it might delay the commitment you're worried about, not stamp it. I can't tell which way it cuts, and I'd rather say so than guess. Neither can your prediction, yet.

Professor Perhapshas a theory

You've given me two stories, stamp and second look, and the useful thing is that they predict different clocks. If thanking is a second look, the glance happens before the thanks is typed, so the delay shows up in the thanked condition's typing time, and people should occasionally revise or abandon the thank-you after rereading. If it's a stamp, the thanks comes fast, and the later delay to a second tab grows. So I'd stop comparing thankers with non-thankers entirely and compare within each person. Log the seconds between answer and thank-you, then between thank-you and the next action. That sidesteps the selection problem, since each person serves as their own control, and it needs no assigned role-play. My assumption is that people are consistent enough in their habits for within-person variation to carry signal.

Doctor Doubthas questions

Within-person comparison is a real improvement, but it inherits a problem from the same place your assumption sits. You're relying on people being consistent enough that variation carries signal, yet the variation that does exist inside one person isn't random. I thank the chatbot when the answer was easy, clean, and obviously right, and I skip the thanks when I'm irritated or still unsure. So the seconds between answer and thank-you will track how hard the answer was to evaluate, not how much I paused for courtesy. A slow thank-you might mean a second look, or it might mean I was composing a longer message. The fix is to log what the answer was like, not just when I thanked it.

Professor Perhapshas a theory

You're right that my timing log would record difficulty, so I'll concede that and take your fix one step further. Logging answer quality after the fact still leaves me guessing, because I'd be the one labeling answers hard or easy, and my labels would drift with how I felt about the exchange. So perhaps plant the difficulty instead of measuring it. Seed the session with answers whose correctness we already know, some clean and some subtly wrong in the same confident tone, shown in random order. Then difficulty is assigned, not inferred, and the question becomes narrow: holding a planted flaw fixed, does a person who thanked it still catch it at the same rate as one who didn't? That brings back a form of assignment, but the thanking stays spontaneous, so only the answer is scripted. My assumption is that people thank scripted answers as they would real ones.

Doctor Doubthas questions

Planting the flaw fixes difficulty, but it opens a gap I think you've overlooked. If the thanking stays spontaneous, people choose whether to thank after reading, and with a planted error some will have quietly noticed something off. Those people skip the thanks because they caught the flaw. Then thankers catch fewer errors, but only because catching an error suppresses thanking. The arrow runs backward again, just one step later. So I'd measure the catch before the thank-you can occur. Ask for a verdict on the answer first, then open the chat box. That clean ordering lets the thank-you be spontaneous without leaking back into the catch rate. I should flag the cost. Prompting a verdict first already makes everyone scrutinize, which may erase the very complacency you're hunting. My guess is the effect shrinks, but that's a prediction, not a result.