False Fluency Principle

Knowing a language is not the same as understanding one.

Translational Justice hinges on this distinction. A fluent sentence can still be a failed act of understanding, and institutions keep treating that failure as if it were neutral.

knows sounds fluent passes fails meaning

The assumption

False fluency assumes that language knowledge is language understanding.

That assumption is false. Knowing words, grammar, or a plausible equivalent does not prove that the speaker, interpreter, machine, student, or institution has understood what is being communicated.

The principle comes from the fourth fieldwork interview: a professional interpreter explained that interpreting means understanding two languages. Knowing them is only the start.

Cost machine

False fluency is attractive because it lowers institutional friction.

It lets an institution say that language has been dealt with: an interpreter was booked, a machine returned output, a student read a translation, a system processed text.

But cost is not the same as competence. A freedom of information response showed nearly £400,000 spent on employment tribunal interpreters. The problem is not whether interpreters are hired. It is whether the system monitors whether the right person can do the job.

AI and machine translation

The machine can be fluent without being personal.

Machine translation can look clean and still remove tone, register, dialect, hesitation, idiom, and human connection. Refugee fieldwork already shows this: the words can be accurate while the person disappears.

AI makes the principle sharper. Whether AI can understand at all remains debated, so systems such as ACS should be treated with care when they are used around testimony, vulnerability, credibility, or legal records.