REVIEW 11 cited by
Language (Technology) is Power: A Critical Survey of "Bias" in NLP
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.
Forward citations
Cited by 11 Pith papers
-
Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
Unsigned differential activations locate a few GLU-MLP neurons whose zeroing surgically destabilizes demographic bias while retaining ~99.5% of measured capabilities.
-
Model Misalignment and Language Change: Traces of AI-Associated Language in Unscripted Spoken English
After ChatGPT's release, science and tech podcast speakers used AI-associated words like 'surpass' and 'align' more often, while control synonyms showed no average shift.
-
PrefPalette: Personalized Preference Modeling with Latent Attributes
Decomposing text into latent attributes and learning community-specific attribute weights improves preference prediction on Reddit and yields interpretable community profiles.
-
FairI Tales: Evaluation of Fairness in Indian Contexts with a Focus on Bias and Stereotypes
A new India-focused benchmark shows that popular LLMs exhibit measurable negative bias against marginalized Indian identities and frequently reinforce caste, religion, region, and tribe stereotypes.
-
Words of Warmth: Trust and Sociability Norms for over 26k English Words
A new 26k-word English lexicon of trust, sociability, and warmth association scores, built by crowdsourcing, with high split-half reliability.
-
Understanding Gender Bias in AI-Generated Product Descriptions
AI-generated product descriptions on eBay show systematic gender bias, including body-size exclusions, stereotyped feature emphasis, and differences in calls to action.
-
Private, Verifiable, and Auditable AI Systems
A thesis demonstrating partial prototypes for zk-verifiable model evaluation and privacy-preserving retrieval, and arguing these pieces can compose into end-to-end auditable AI systems.
-
When Large Language Models Meet Law: Dual-Lens Taxonomy, Technical Advances, and Ethical Governance
A literature review that classifies LLM-for-law research using a dual-lens taxonomy of Toulmin argumentation components and legal practitioner roles.
-
Bias-Aware Mislabeling Detection via Decoupled Confident Learning
A per-group extension of Confident Learning detects mislabeled instances under group-dependent label noise and outperforms standard baselines on synthetic and hate speech data.
-
Public Service Algorithm: towards a transparent, explainable, and scalable content curation for news content based on editorial values
In a 30-article pilot with four editorial criteria, the best LLMs achieved up to 75% overlap with human editors' top-5 article selections.
-
A Survey on Data Security in Large Language Models
A survey of data security risks in LLMs that organizes threats, defenses, and evaluation datasets, with notable factual errors in its tables.
Discussion (0). Sign in to comment.