AI tools are making the world look weird

Meaning and tone of “WEIRD”

• WEIRD = Western, Educated, Industrialised, Rich, Democratic, coined in psychology/anthropology to describe a narrow subject pool, not originally as a slur.
• Some commenters still hear it as anti‑Western or pejorative (“West ⇒ weird ⇒ bad”), arguing that everyday “weird” is negative and that alternative acronyms could have been chosen.
• Others counter that the term was coined by WEIRD researchers about themselves to challenge Western‑centrism; the “weirdness” refers to being statistically atypical, not morally inferior.
• Debate over whether dismissing complaints about the term is insensitive, versus seeing such reactions as victimhood or attempts to silence critique of Western bias.

AI bias: chauvinism vs “just bugs”

• Central concern: AI systems are implicitly WEIRD‑centric, privileging Western/Californian values and experiences.
• Examples discussed: cameras that struggle with non‑white faces, facial recognition failing on atypical faces, resume filters that misclassify underrepresented countries, “Kafkaesque” bureaucracy for people with non‑standard names or speech.
• Some argue these should be treated primarily as software/data bugs: bias becomes “racism” only if issues are ignored rather than fixed.
• Others note that if affected groups say the labeling or behavior is pejorative or exclusionary, that social meaning matters regardless of programmer intent.

Data, language, and cultural alignment of LLMs

• Many assume training corpora are overwhelmingly English and Western, leading models to “think American” or “California HR” by default.
• Human feedback is suspected to be concentrated in specific Anglophone regions, further skewing norms.
• Using other languages (e.g., Indonesian, Russian, Japanese) noticeably changes answers; non‑English performance is often weaker and can show odd failure modes (e.g., reasoning in English while replying in another language).
• Some wonder how non‑US models (Chinese, European) compare, and whether they just embed their own national biases instead.
• A linked study showing ChatGPT values clustering with Australia/New Zealand and Japan prompts questions about methodology and whether this really measures “simulation of local values” or just correlation with some countries’ answer patterns.

Homogenization and cultural nuance

• Commenters note that LLMs and AI “suggestion” tools can homogenize writing toward Western/corporate style, eroding local or subcultural nuance.
• Social media, US media, and movies have already globalized a narrow ideological slant; AI is seen as another amplifier of that, “a clone army of corporate spokesmen from the US west coast.”
• Some propose specialized, culturally tuned models for different regions and contexts as a partial remedy.