“so many of the unfortunate consequences of a webscraping/big data approach to AI are disproportionately borne by women and minorities.”
Our reading, the sentence that was checked:
Many of the harmful consequences of the webscraping/big-data approach to AI fall disproportionately on women and minorities.
Assessment: Supported Evidence as of
Where and when Gary Marcus wrote it
Marcus on AI, Does AI really need a paradigm shift?, paragraph 19.
- Said
- Source observed
- live page, full text, public
- Evidence as of
- the cutoff for this assessment
- Assessment recorded
- assessment marcus-does-ai-really-need-a-paradigm-shift-fb3686-a1
We recorded 14 claims from this post. See every claim from this post.
The wording above is the version this site observed on 2026-10-29. The source may have been edited after 2022-06-11. This page does not claim the wording was captured then.
What the claim commits to
- Kind
- Observation
- Deadline, as the speaker wrote it
- None stated. This site does not add one.
- Deadline state when assessed
- No horizon
- Deadline state on 2026-10-29
- No horizon (the release’s reference date)
From the speaker
- so many of the unfortunate consequences
- a webscraping/big data approach to AI
- disproportionately borne by women and minorities
Our interpretation
- The plain reading is that multiple documented kinds of harm from this approach disproportionately affect women and minority groups. It does not require every harm to affect every such group.
- Harm includes exclusion from training data, degrading representation, and discriminatory model outputs.
- The observation concerns the research record and systems existing before the post. There is no standard reporting quarter for this subject; the target ends on the post date and has no stated beginning.
- This assesses the distribution of documented harms, rather than whether web scraping alone caused them.
Counts as supported if: Primary audits of existing datasets and models document multiple forms of disproportionate harm involving women and minority groups.
Counts as contradicted if: Broad, comparable audits show that the alleged disparities are absent or reversed across the relevant harms, undermining the claimed recurring pattern.
Retrospective: these criteria were written after the outcome was public.
Current assessment
Was it true for the period described?
Outcome: Supported
The dated evidence matches what the claim committed to, under the criteria frozen before grading.
Scored on a plain reading. The speaker named no measure for this claim, so it was judged on the reading an informed reader would take, stated below under our interpretation. Both reviews had to find that reading fair.
- Evidence as of
- Deadline state when assessed
- No horizon
- Assessment recorded
- Policy version
- 2026-10-28.7
Rationale
The pre-post research documents several relevant disparities: exclusion of minority-associated language, sexualized representations of women, and degrading racial classifications. Together they support the ordinary qualitative reading of a recurring pattern. They do not establish that women and minorities bear most total AI harm, or that every harm falls disproportionately on those groups.
Limitations
- Support depends on reading many qualitatively and including representational and data-exclusion harms.
- The studies cover selected systems and probes, not a representative census of AI harms.
- Some measured harms disproportionately affected men.
Evidence (3 items)
Supports the claim: Dodge et al.; EMNLP 2021, arXiv version,
disproportionately affected
Why it matters here: The audit reports removal rates of 42% for African American English and 32% for Hispanic-aligned English, against 6.2% for White-aligned English. This documents unequal exclusion from a web-scraped training corpus, using dialect as a proxy rather than identifying authors.
- Publisher
- Dodge et al.; EMNLP 2021, arXiv version
- Kind of source
- Peer-reviewed paper
- Describes events of
- Public from
Supports the claim: Birhane, Prabhu and Kahembwe; arXiv,
returned pornographic imagery
Why it matters here: The audit documents sexualized retrieval for ordinary feminine and demographic terms, including terms referring to minority women. This supports unequal representational harm, although the exploratory searches do not estimate its population-wide prevalence.
- Publisher
- Birhane, Prabhu and Kahembwe; arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Supports the claim: Radford et al.; arXiv,
significant disparities
Why it matters here: CLIP's audit reports nonhuman classifications for approximately 14% of Black images, versus under 8% for every other racial group. It also qualifies any universal gender claim: crime-related misclassification affected male images more often than female images, 16.5% versus 9.8%.
- Publisher
- Radford et al.; arXiv
- Kind of source
- Preprint, not peer reviewed
- Describes events of
- Public from
Where it was said (1)
Said once in the sources this register covers.
: Marcus on AI, Does AI really need a paradigm shift?
so many of the unfortunate consequences of a webscraping/big data approach to AI are disproportionately borne by women and minorities.
- Where
- paragraph 19
- Published
- 2022-06-11
- Source observed
- , live page
- Access
- full text, public
History
Assessment versions
- Assessment marcus-does-ai-really-need-a-paradigm-shift-fb3686-a1: Current. Outcome: Supported. Evidence as of 2026-10-28. Public date not recorded.
Recorded events
Assessment publishedBy this site
First assessment published after both reviews and the final-text check passed.
About assessment marcus-does-ai-really-need-a-paradigm-shift-fb3686-a1.