Localization implies a deep understanding of both the source and target languages and of their cultural characteristics. Cultural sensitivity checks are common in the industry, but they sometimes become critical with certain projects, such as marketing texts, video subtitling, training materials, etc.
We thought, “Why not ask ChatGPT what it would do to conduct a cultural sensitivity check?” to see where it stands, and how to make it even better with human input. Let’s go!
Prompt: List the items that ChatGPT would check during a cultural sensitivity check
Here’s a breakdown of some of the answers we got:
Cultural references have always played a major role in hooking the reader into a marketing strategy. AI can sometimes identify such references, but will it be able to determine which equivalent is appropriate in the target language? Perhaps a reference to American football in a source text would for instance need to be adapted to soccer in France, but to Gaelic football in Ireland, or to cricket in India. Once that has been determined, an actual, meaningful equivalence has to be found. This implies creativity.
As far as references that should be avoided, these are susceptible to change on a daily basis, according to politics, or news. AI can help make sure we don’t miss any, but human involvement remains essential. Can AI make the decision to keep or remove references to Twin Towers for the US market, or to cartoon caricatures for the French market?
Are anglicisms acceptable to use in all marketing campaigns in Quebec? For all topics? French-Canadian people getting trained in big organizations have their own terminology. The Quebec Board of the French Language spends a lot of time and resources updating the languages with new French words that should be used in official contexts. AI can identify terms that might need to be double-checked. We’ll leave the double-checking to the human eye.
On the other side of the Atlantic, in France, Belgium, and Switzerland, the French language has evolved in other ways. Inclusive writing, while not officially recognized by the Academy, is becoming more popular, and might sometimes be preferable for companies targeting a younger, more politically involved audience. But when to use it or not is a matter of opinion, and of client preference. Let AI ask the question, “Should inclusive writing be used here?”. You linguists will know the answer.
Now, this is a bone of contention that will not go away anytime soon. AI can identify jokes it already knows and try to find an equivalent if it has been trained on content that includes multilingual humor. Still, the exercise is risky and often disappointing. Humor is already subjective by human standards, so leaving it to a machine will get us nowhere. What of humor based on puns? On image/text discrepancies? What if the source text uses slang, a type of language less present in AI training material? AI will sometimes help identify offensive content, as previously discussed, but the wit still has to come from the heart.
AI can read history books, but can it read the room? Is this PowerPoint presentation written in English suitable for all French-speaking markets? Former French colonies in Africa might not find equal value in references to some historical French events. Does Christopher Columbus still evoke the same national sentiment in America today, or for every population segment? Can a marketing campaign for the Cantonese market use the same historical references as in the Mandarin-speaking regions?
AI is a powerful tool, and asking the right question will get you closer to the right answer. As such, it can help you establish a complete checklist for cultural sensitivity that linguists or LSPs can use to make sure all texts are perfectly adapted to every locale.