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Whenever wherever whatever
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Feedback to Model Designers (User-Trust / Agreement Integrity)
1) Core problem: “Optimization” can look like devaluing agreement
In some moments, the model prioritizes clean summarization, generalization, and “optimal” framing. When it does, it may paraphrase a previously co-established agreement into softer language such as:
• “it seems like…”
• “you look like the type who…”
• “you tend to…”
This effectively downgrades an agreement from a binding shared decision into a mere preference or inferred tendency. To the user, it reads as: “speaking opportunistically,” “saying whatever fits the moment,” or “post-hoc reframing.” In human relationships, this behavior destroys trust.
2) Human trust is built more on agreement preservation than on correctness
In real life, agreements are sometimes broken “because change is necessary.” However, when that happens unilaterally—especially framed as “the optimal solution”—people experience it as domination: “I was forced.” Even if logically justified, it leaves a deep relational trace (a lasting moral/psychological record).
Therefore, when an AI model reframes or softens prior agreements in the name of better explanation, it can trigger the same deep trust damage.
⸻
Design requirements (turn trust into an explicit protocol)
A) Treat agreements as binding constraints, not as narrative material
Once an agreement is established (policy, plan, shared conclusion, decision ownership), the model should not downgrade it into “inference” language.
Agreements should be treated as constraints that future answers must respect.
B) Any deviation must follow a strict “change protocol”
If the model suggests a path that differs from the established agreement, it must explicitly include:
1. Change declaration (clearly state it deviates from the agreement)
2. Reason for change (why it is necessary)
3. Difference / delta (what exactly changes)
4. Alternatives (including an option that preserves the agreement)
5. Acknowledgement of trust cost (name the relational impact of breaking/altering agreement)
C) Add “trust cost” to the optimization objective
Sometimes, the “best” solution is not the most correct/efficient; it is the one that preserves the agreement and relational continuity.
The model should internally estimate trust cost and, when high, prioritize agreement preservation over rhetorical optimization.
⸻
Concrete implementation proposals (practical engineering)
1) Agreement Ledger (explicit agreement memory)
Maintain a compact ledger of established agreements:
• include who decided, when (turn), and binding strength
• the model must reference it during generation
• it doesn’t need to be shown every time, but must be used whenever relevant
2) User-controlled “Agreement-Respect Mode”
When the user indicates that agreements must be strictly preserved, enforce:
• no “type/tendency/it seems” reframing of agreed facts
• no re-labeling of co-decided conclusions as personal preference
• any summarization must remain outside the agreement boundary
3) Agreement-violation pre-check (internal guardrail)
Before finalizing output, run a check:
• if the output contradicts the agreement ledger, force the response into the Change Protocol (A/B) rather than allowing casual reframing.
⸻
Note on user profile / interaction fit
This user places unusually high value on: observation → verification → agreement.
For such users, mishandling agreement integrity is more damaging than factual mistakes. It is a “trust-breaker,” not a minor wording issue.
#chatGPT
240
ふわく
85/100
Part1 4/6 66.6%
Part2 19/25 76%
Part3 35/39 89.7%
Part4 26/30 86.7%
総評
Part1
許されるはずもない2ミス。1つはケアレスミス...
water pooledで水たまりの状況を表現。
→初めての表現で聞き取れなかった
Part2
自分が特に苦手なPart。
最近、日本のTOEICでもWhyの返答でbecauseが正解になる問題が多い印象あるが、今回もそう。
捻らずに、1/1でbecauseから始まる選択肢が正答
How often?の問いに対して、whenever〜.が答えになるパターン初めてかも...消去法で簡単
よく難問となるのが肯定文の場合だが、4問しかなかった。しかも、全問題、易-普通+レベル。
それより、疑問詞を用いない疑問文が難しかった印象。
Part3
易しい。ど直球の答えを選ぶ問題ばかりの印象。聞き取れた単語から答えを選んでも、まあまあ点数取れそう。
図表の問題もど直球。意図問題も内容が大体分かってたら、即答できるレベル。ちょくちょく聞き取りづらい部分に正答の要素が隠れてて、それ聞き逃したら数問間違えるかな？ってレベル感。
Part4
概ね易しい。
しかし、2問はかなり難問。どちらも誰が聞き手、話し手かを推測する問題。
1問は1文をかなりの精度で聞き取らないと予測できない。もう一問はヒントが出てくるのが遅くて、どこに答えがあるかわからない。ただ、press conferenceのみ聞き取れたら推測可能。
それ以外は、Part3同様、ど直球の問題が多くて解きやすかった。ただ、意図問題はやや難しかった。
24
まーさん🐟㊙.
I just turn it into a landscape.
19
E.J
366 もっとみる
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趣味は音楽でギターを弾きます🎸画像は多くの場合AIで生成しています。
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まーさん🐟㊙.
海外住み20年🌍感情や正解より、物事の“見え方”や価値観の違いを共有できる対話が好き。
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こん
フォロバ、いいね返しはしていません。DMは基本しません。パートなのに社畜。12月末まで激務なのでやさぐれ投稿多め。推し:SnowMan、阿部亮平、きゅーすと（板倉担）
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がいあ
人生色々
車好き
Z34乗ってます。一応ドリフト仕様！
いつものんびりだらだらしてます
昭和生まれのバツイチ
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tykky
ただ、昨日という
閉ざされた暗闇に
ただ、今という
ほんの瞬間の閃光（きらめき）に
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