Emily M. Bender

Emily M. Bender

x.com/emilymbender

Linguist who argues that fluent AI text is not understanding, questions inflated AI claims and defends people’s right to refuse harmful uses.

Bagaimana AI akan mengubah dunia?

Perubahan peradabanPerubahan bertahapDoomBloom
Posisi simulasiRentang interpretasi

Mendatar: pandangan Doom–Bloom yang ia ungkapkan. Ke atas: skala transformasi.

Doom–Bloom: 28 dari 100. Skala transformasi: 54 dari 100. Rentang interpretasi: 23 hingga 33 secara horizontal, 49 hingga 76 secara vertikal. Ini adalah koordinat interpretasi, bukan probabilitas kejadian.

P(doom) Emily M. Bender · disimpulkan

≈4%

0%100%

Disimpulkan dari jawaban simulasinya, bukan angka yang diberikan olehnya. Rentang yang masuk akal: 1–12%.

Hal-hal yang menentukan pandangannya

Asumsi utama

Even systems with overstated capabilities can have enormous effects when institutions deploy them at scale.
Jawaban 2

Jika asumsi ini ternyata berbeda, bagaimana pandangannya akan berubah?

Detail lebih lanjut

Manfaat yang diperkirakan

Manfaat yang terbatas atau hanya tersebar secara sempit diperkirakan akan terwujud.

33 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 33 hingga 33 pada skala kualitatif.

Kerugian yang diperkirakan

Kerugian parah atau meluas merupakan bagian yang berarti dari masa depan yang diperkirakan.

62 / 100

Dampak kecilDampak transformatif

Rentang interpretasi 33 hingga 67 pada skala kualitatif.

Pengaruh manusia

Pilihan manusia dapat mengarahkan ulang lintasan AI secara signifikan.

71 / 100

Sedikit pengaruhPengaruh kuat

Rentang interpretasi 50 hingga 100 pada skala kualitatif.

Aturan penggunaan AI

Batasi penggunaan AI yang dibahas hingga perlindungan atau izin sebelumnya tersedia.

Posisi simulasi: Izinkan penggunaan AI yang dibahas dengan akuntabilitas dan perlindungan yang terarah.

Minimalkan pembatasan terhadap penggunaan AI yang dibahas.

Interpretasi ini mempertahankan kondisi yang dinyatakannya. Manfaat dan kerugian dapat sama-sama besar. Rentang tersebut menggambarkan cara kami membaca jawaban simulasinya, bukan interval kepercayaan statistik.

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Pandangan dunia serupa

Pemimpin opini dengan pandangan dunia simulasi yang paling mendekati pandangan Emily M. Bender

Penilaian Simulasi

Pertanyaan 1

Menurut Anda, apa arti AI bagi masa depan kita—dan mengapa?

First, “AI” is too vague to support a single forecast. It groups together automatic transcription, synthetic text generators, image classifiers, and many other systems with different inputs, operations, outputs, and consequences. Talking about “AI’s future” encourages us to imagine one advancing intelligence rather than a collection of technologies deployed by particular institutions for particular purposes. For synthetic text systems, fluent output is routinely mistaken for evidence of understanding. Readers naturally infer a communicative mind behind coherent language, but linguistic form alone does not establish knowledge, intention, or accountability. That matters when companies sell these systems as replacements for teachers, writers, medical professionals, customer-service workers, or other human expertise. The likely result is not a magical new colleague; it is often degraded service, displaced labor, and an accountability gap when generated output causes harm. But none of this is inevitable. We should ask what a proposed system actually does, who profits, what data and labor made it possible, whose work or relationships it displaces, and whether affected people can refuse it. Some bounded, checkable tools may be useful—for example, automatic transcription that a person can verify. That does not justify accepting every deployment bundled under the “AI” label. Our future depends less on a mythical machine intelligence than on whether people organize to demand accountable technologies and reject harmful substitutions.

Pertanyaan 2

Setting aside good or bad: how much do you think AI will ultimately change the world—a little, a lot, or completely?

“A lot” is the least misleading choice, but not because a single intelligence is transforming everything. Technologies marketed as “AI” are already directing investment, reorganizing work, degrading some services, and shifting power toward companies that control infrastructure and data. Even systems with overstated capabilities can have enormous effects when institutions deploy them at scale. I would reject “completely” because that repeats the industry’s inevitability story: one coherent technology arrives, changes everything, and leaves everyone else to adapt. Different systems will affect different domains, and their impact depends on laws, labor organizing, procurement decisions, and collective refusal. The consequential question is not how powerful “AI” ultimately becomes. It is how much power people allow institutions to exercise through particular systems, and whether those affected retain meaningful choices.

Pertanyaan 3

What’s your rough gut-feel chance that AI causes human extinction or a similarly permanent catastrophe? A number or “no idea” is fine.

I won’t manufacture a percentage. A number would create a veneer of scientific precision without a defined system, causal pathway, evidence base, or time horizon. “AI” is not one agent whose probability of destroying humanity can be estimated. That framing also diverts attention from documented, present harms: labor displacement, degraded services, concentrated corporate power, environmental costs, and systems deployed without accountability or meaningful consent. Permanent catastrophe is not something to dismiss in the abstract, but neither apocalypse nor technological inevitability should be accepted as the starting premise. Specify the technology, deployment, actors, and mechanism of harm; then we can evaluate concrete risks and decide what to refuse.

Pertanyaan 4

Tekanan apa yang menurut Anda akan membentuk cara perusahaan AI menangani keselamatan?

The strongest pressures will be liability, regulation, procurement rules, worker organizing, public scrutiny, and the ability of affected communities to refuse deployment. Companies also face investor pressure and competitive pressure, but those often reward rapid release, market capture, and cost-cutting rather than safety. We should also be precise about “safety.” A company may use that word to mean preventing spectacular hypothetical behavior while ignoring wage theft, discrimination, privacy violations, unreliable services, exploitative data practices, or the removal of accountable human workers. Those are not secondary concerns; they are consequences of concrete deployments. Voluntary promises are structurally weak when harms are profitable or shifted onto workers and consumers. Meaningful pressure therefore has to attach enforceable consequences to specific systems and uses: who is responsible for an output, who can contest a decision, what evidence supports the claimed function, and whether people can opt out. Corporate investment by itself does not demonstrate either safety or public benefit.

Sumber

Artikel, wawancara, dan tulisan yang digunakan sebagai landasan bagi pengguna simulasi ini.

Beyond the AI Hype Machine

Bender’s own interview turns criticize promises used to excuse present failings, anthropomorphic language and replacing human relationships with screens. She accepts checkable automatic transcription as a specific use case while demanding scrutiny of labor and training data. Hanna’s separate remarks are not attributed to Bender.

kqed.org
AI Hurts Consumers and Workers—and Isn’t Intelligent

Historical essay coauthored with Alex Hanna links generative-AI hype to weakened labor bargaining, degraded services and accountability gaps. Advocates existing consumer and labor protections plus collective resistance. Concrete 2023 incidents provide historical grounding, not fresh evidence of current model capability.

techpolicy.press
Artificial Intelligence — author preprint

Date printed on the author’s encyclopedia preprint. Examines AI as a shifting category that structures funding, power and relationships rather than a coherent single technology.

faculty.washington.edu
De-anthropomorphizing AI: From wishful mnemonics to accurate nomenclature

Coauthored with Nanna Inie and Peter Zukerman; abstract inspected. Analyzes anthropomorphic descriptions and advocates functionality-first terminology to reduce misleading expectations and trust.

firstmonday.org
EL PAÍS interview with Emily Bender and Alex Hanna

Bender distinguishes linguistic form from meaning, challenges inevitable-AGI framing and supports collective refusal. Hanna’s separate answers are not attributed to Bender.

elpais.com
The AI Con — authors’ book site

Book description and publication metadata, not full-book access. Frames inflated capability promises as serving corporate power and advocates a different technological future.

thecon.ai
Numbers are not science

Bluesky post read via the public API. Closing a thread, she says that using a number to make a claim does not mean any science was done to get there, and compares a 10% doom claim to a 10% chance of jelly beans raining tomorrow. A dismissal of P(doom) figures, not a personal estimate.

bsky.app
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