Berkonsultasi dengan AI sebelum keputusan pribadi besar vs. memutuskan tanpanya
Jika Anda bertindak
Konsultasi AI sebelum memutuskan
22%
Jika Anda tidak bertindak
Memutuskan tanpa masukan AI
28%
Persentase orang yang kemudian menyesali setiap pilihan. Diagram batang dan catatan lengkap ditampilkan di bawah.
Lifestyle
Terakhir ditinjau 2026-05-11
Kualitas bukti 4.0/5
Skor tinjauan delapan dimensi terhadap
rubrik kualitas
. Setiap dimensi dinilai 1–5.
D1 Verifikasi sumber
4/5
D2 Otoritas & independensi sumber
5/5
D3 Akurasi tingkat penyesalan
2/5
D4 Keterbandingan sumber
2/5
D5 Pola Gilovich
4/5
D6 Kualitas prosa
5/5
D7 Kelengkapan peringatan
5/5
D8 Kualitas sampel
5/5
Rata-rata4.0/5
Data proksi — tidak ada survei penyesalan langsung untuk keputusan ini. Tingkat diturunkan dari skor kepuasan dan data hambatan akses daripada pertanyaan yang langsung menanyakan tentang penyesalan. Lihat peringatan di bawah.
Penyesalan atas tindakan
Konsultasi AI sebelum memutuskan
22%
~22% dari mereka yang berkonsultasi dengan AI untuk keputusan penting melaporkan hasil yang merugikan atau bermasalah (proxy; 11% menerima rekomendasi kesehatan yang tidak aman; 21–49% respons medis AI dinilai bermasalah dalam studi terkontrol)
Orang dewasa AS yang berkonsultasi chatbot AI untuk keputusan kesehatan, keuangan, atau hidup
Oktober-Desember 2025
Penyesalan atas kelambanan
Memutuskan tanpa masukan AI
28%
Tidak ada survei yang mengukur penyesalan di antara orang yang menolak berkonsultasi dengan AI. Sebagai plafon biaya-peluang kasar (BUKAN tingkat penyesalan terukur), di antara pengguna AI 46–59% melaporkan manfaat informasi konkret dan ~90% dari mereka yang bertindak atas saran keuangan AI menyebut hasilnya 'menguntungkan atau bermanfaat' — manfaat yang dilewatkan non-pengguna; ~28% adalah batas atas konservatif yang disimpulkan, bukan pengukuran langsung
Orang dewasa AS yang membuat keputusan signifikan tanpa konsultasi AI
2025-2026
% menyesal dengan pilihan ini
Konsultasi AI sebelum memutuskanMemutuskan tanpa masukan AI
22%28%
balanced — Cukup seimbang — kedua pilihan membawa penyesalan serupa.
Keputusan terkait
Keputusan yang serupa secara semantik — area yang sama, kompromi yang berbeda.
Melakukan pembesaran payudaraTidak melakukan pembesaran payudara
7,0%5,0%
Seimbang
Hampir seimbang
About 1 in 4 US adults has now consulted AI for health information or advice, according to a West Health/Gallup panel of 5,660 adults surveyed in late 2025. Among those users, 46% felt more confident asking their providers questions afterward, and 59% used AI to prepare before a doctor visit — concrete stated benefits that non-users forgo. But the same survey found that 11% of AI health users reported receiving unsafe recommendations, and a parallel UCLA/BMJ Open study rating 250 AI responses to medical questions found 49.6% were problematic to some degree — mostly delivered with confidence and few caveats, making them difficult for users to identify as unreliable. An MIT Media Lab study published in NEJM AI documented that participants systematically overestimated AI medical reliability and could not distinguish AI-generated from physician responses, even when the AI response was inaccurate.
The regret arithmetic here is genuinely ambiguous, which is unusual in this dataset. The action-side risk (22% proxy, bounded by 11% unsafe-recommendation rate and 49.6% problematic-response rate) and the inaction-side opportunity cost (28% proxy, based on AI users’ reported benefits) are close enough that the entry is classified as ‘mixed’ rather than clearly inaction-dominates. How AI is used matters more than whether it is used: supplementing a scheduled doctor visit with AI research before attending is a different action category than using AI as a triage replacement for a symptom that warrants evaluation. The former has low action-risk and meaningful information benefit; the latter has higher action-risk and may cause harmful delay. The 14 million Americans who skipped a provider visit based on AI advice represent the higher-risk end of the use spectrum, though some of those skipped visits may have been genuinely unnecessary.
The honest summary of this entry’s evidentiary state: the AI consultation decision is too domain-specific, use-case-dependent, and rapidly evolving to generate a stable regret-pair estimate. Financial AI consultation self-reports are overwhelmingly positive (Wells Fargo: ~90% found results worthwhile), which would pull the inaction-regret figure up substantially if the financial domain were weighted equally with health. Medical AI consultation carries real documented risk of inaccurate confident advice in a domain where acting on wrong information has direct health consequences. The mixed classification reflects both the genuine uncertainty and the heterogeneity of “consulting AI” as a decision — something between a research tool and an advisor, with properties of each and the disclaimers of neither.
Sumber: tindakan
Buku besar klaim
Setiap angka di bawah ini adalah apa yang dilaporkan masing-masing sumber, dengan kutipan kata demi kata yang kami andalkan dan bagaimana kami sampai pada angka kami. Klik tautan mana saja untuk memverifikasi langsung.
2/3 sumber diverifikasi secara independen kata demi kata terhadap sumber terkutip
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Terverifikasi
Sumber referensi
About 1 in 4 US adults (over 66 million) report having used AI tools or chatbots for health information or advice; 11% of US adults who used AI for health information reported receiving unsafe recommendations; 14% (~14 million) skipped a provider visit based on AI advice
Kutipan
“"One in four U.S. adults — the equivalent of over 66 million Americans — report having used artificial intelligence tools or chatbots for physical or mental healthcare information or advice. ... 11% of AI health users reported receiving unsafe health recommendations from AI. 14% of recent AI health users skipped a provider visit based on AI advice. 46% felt more confident asking providers questions after using AI."
”
Data sumber dari
2026-01-01
Diakses
2026-05-11
Verifikasi
Kutipan diambil ulang secara independen dan dikonfirmasi kata demi kata terhadap sumber terkutip selama audit pendasaran kami.
Perhitungan
West Health / Gallup panel survey of n=5,660 US adults, fielded October–December 2025, margin of error ±2.1pp. This is the most methodologically rigorous large-sample US survey on AI health consultation outcomes. The "one in four US adults (over 66 million)" overall-prevalence figure grounds the entry's opening body sentence, which previously cited this survey for that figure without a matching excerpt/statistic. The 11% unsafe-recommendation rate is the primary action-regret proxy: it represents the share of users who received advice that was later identified as unsafe, which is a necessary precursor to regret even if regret is not directly measured. The 14% who skipped a provider visit based on AI advice is a behavioral-consequence measure; it does not directly map to regret (some may have correctly assessed that no visit was needed). The 22% action-regret estimate is bounded by the 11% unsafe-recommendation rate (lower bound) and the broader 21–49% problematic-response rates from academic accuracy studies (upper bound), centered at approximately 22% to reflect that problematic responses do not always produce regrettable outcomes.
[2]CIDRAP (University of Minnesota) reporting on UCLA / BMJ Open study — AI Chatbots Provide Poor Answers to Medical Questions Half the Time
Terverifikasi
Sumber referensi
49.6% of AI responses to medical questions were rated as problematic in a blinded evaluation of 5 major chatbots (30% somewhat, 19.6% highly problematic)
Kutipan
“"49.6% of AI chatbot responses to medical questions were rated as problematic — 30% 'somewhat problematic' and 19.6% 'highly problematic.' Chatbot responses were consistently given with confidence and certainty, with few caveats or disclaimers."
”
Data sumber dari
2025-02-01
Diakses
2026-05-11
Verifikasi
Kutipan diambil ulang secara independen dan dikonfirmasi kata demi kata terhadap sumber terkutip selama audit pendasaran kami.
Perhitungan
UCLA / BMJ Open study of 250 total questions across 5 major chatbots (ChatGPT, Gemini, DeepSeek, Meta AI, Grok), 10 questions each across 5 medical categories, data collected February 2025, published 2026. The 49.6% problematic-response rate establishes the upper bound of action-side risk for health-domain AI consultation. The key finding about confident framing with few caveats is the mechanism that converts problematic responses into potential harm: users cannot easily identify which responses are in the unreliable half. Used here to anchor the upper bound of the 22% action-regret estimate; the lower bound is the West Health/Gallup 11% unsafe-recommendation rate from real-world self-report.
[3]NEJM AI (Shekar, Pataranutaporn, Sarabu, Cecchi & Maes) / MIT Media Lab — People Overtrust AI-Generated Medical Advice despite Low Accuracy
Telaah sejawat
Across 300 participants, people were unable to distinguish AI-generated from doctors' responses, rated high-accuracy AI responses as more valid/trustworthy/complete, gave low-accuracy AI responses ratings similar to doctors', and showed a high tendency to follow potentially harmful AI advice
Kutipan
“"Participants were unable to effectively distinguish between AI-generated responses and doctors' responses [and] rated high-accuracy AI responses as significantly more valid, trustworthy, and complete than the other two types of responses. Low-accuracy AI responses tended to receive ratings similar to those given to doctors' responses [and participants showed] a high tendency to follow the potentially harmful medical advice contained in those responses."
”
Data sumber dari
2025-05-13
Diakses
2026-05-11
Perhitungan
NEJM AI / MIT Media Lab study on AI medical response evaluation. The overtrust finding is the mechanism explanation: users systematically overestimate AI reliability in the medical domain, meaning the problematic-response rates from accuracy studies translate into real-world harm more readily than they would if users could identify unreliable responses. This supports using the problematic-response rate (not just the unsafe- recommendation rate) as the relevant risk bound.
Sumber: tidak bertindak
Buku besar klaim
Setiap angka di bawah ini adalah apa yang dilaporkan masing-masing sumber, dengan kutipan kata demi kata yang kami andalkan dan bagaimana kami sampai pada angka kami. Klik tautan mana saja untuk memverifikasi langsung.
[1]West Health / Gallup — Millions of Americans Now Consult AI Before, After, and Sometimes Instead of Seeing a Doctor
Sumber referensi
46% of AI health users felt more confident asking providers questions afterward; 59% used AI to research before a doctor visit
Kutipan
“"46% of AI health users felt more confident asking providers questions after using AI. 59% used AI to research before a doctor visit. 71% were motivated by wanting answers quickly; 71% wanted additional information."
”
Data sumber dari
2026-01-01
Diakses
2026-05-11
Perhitungan
West Health / Gallup panel, n=5,660, October–December 2025. The 46% who felt more confident asking providers questions is a concrete stated benefit of AI consultation — a benefit that non-users forgo. The 28% inaction-regret proxy is derived conservatively from the proportion of AI users who report concrete preparation or information benefits (46–59%), adjusted downward to reflect that non-users may have obtained similar information through other channels (search, calling a nurse line, reading drug inserts). No direct "do you regret not consulting AI for this decision?" survey was identified. This is the most data-sparse side of the entry.
[2]ABA Banking Journal (reporting on Wells Fargo and TD Bank surveys) — Bank Surveys Find Consumers Increasingly Turning to AI for Financial Advice
Sumber referensi
~90% of US adults who used AI for financial decisions and acted on the advice said results were 'profitable or worthwhile' (Wells Fargo 2026); 55% of adults use AI for financial management decisions (TD Bank 2026)
Kutipan
“"19% of U.S. adults used AI for financial advice; 38% among Gen Z. Two-thirds of those who used AI acted on its suggestions. Approximately 90% of those who acted said the results were 'profitable or worthwhile.'"
”
Data sumber dari
2026-04-01
Diakses
2026-05-11
Perhitungan
Wells Fargo consumer survey and TD Bank consumer survey, both reported April 2026. Sample sizes and full methodology not disclosed. The 90% "worthwhile" self-report is the most positive available outcome figure for AI financial consultation but is highly susceptible to self-serving bias: people who acted on AI advice and lost money are less likely to report the action as worthwhile, but are also less likely to be included in a consumer satisfaction survey. Used here as directional corroboration that AI financial consultation produces positive self-reported outcomes at high rates, supporting the inaction-regret proxy. Not used as a primary figure due to methodological opacity.
Catatan
PENGUKURAN PROXY DI SELURUH BAGIAN. Tidak ada survei yang secara langsung menanyakan kepada individu "apakah Anda menyesal berkonsultasi dengan AI untuk keputusan ini?" atau "apakah Anda berharap telah berkonsultasi dengan AI sebelum memutuskan?" Kedua sisi disusun sepenuhnya dari data yang berdekatan: studi kualitas hasil (untuk risiko sisi-tindakan), manfaat yang dilaporkan (untuk biaya-peluang sisi-ketidaktindakan), dan ukuran perilaku (tingkat melewatkan kunjungan penyedia layanan). regret_delta sebesar -0,06 terlalu kecil untuk secara andal mengklasifikasikan ini sebagai inaction-dominates; ini diklasifikasikan sebagai 'mixed' karena bukti tidak jelas mendukung pola mana pun. Risiko sisi-tindakan sangat bervariasi menurut domain dan menurut cara AI digunakan: melengkapi kunjungan dokter (risiko rendah, manfaat tinggi) vs. menggantikannya untuk gejala serius (risiko tinggi). Studi akurasi akademis (tingkat respons bermasalah 21–49%) mencerminkan kinerja AI pada pertanyaan medis secara khusus — domain dengan taruhan tertinggi. Keputusan keuangan dan kehidupan umum kemungkinan memiliki tingkat respons-bermasalah lebih rendah tetapi lebih sedikit tolok ukur akurasi yang diterbitkan. Studi overtrust AI NEJM adalah caveat yang sangat penting: mekanisme yang mengubah respons bermasalah menjadi kerugian adalah ketidakmampuan pengguna mendeteksinya, dan pembingkaian yang percaya diri dengan sedikit peringatan (terdokumentasi dalam studi UCLA/BMJ Open) secara sistematis menyembunyikan ketidakpastian ini. "Menggunakan AI" bukanlah tindakan tunggal: membaca ringkasan AI untuk mempersiapkan kunjungan dokter secara kategoris berbeda dari bertindak atas diagnosis AI alih-alih menemui dokter. Entri ini mengagregasi penggunaan yang heterogen ini, yang membatasi presisinya. Data laporan-mandiri Wells Fargo dan TD Bank tidak transparan soal metodologi dan harus diperlakukan sebagai arah saja. Kemampuan AI meningkat pesat; tolok ukur akurasi dan keandalan dari 2025–2026 kemungkinan akan berbeda dari yang berlaku 12–24 bulan setelahnya. Entri ini memiliki masa berlaku lebih pendek daripada kebanyakan entri dalam kumpulan data ini.
