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Evidence record 3043 · automatically gathered

How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

Data narratives increasingly shape public understanding, but their failures are rarely just isolated factual errors or deceptive charts. Instead, they emerge through a broader meaning-making process in which quantitative evidence is transformed into claims, representations, and arguments. While prior work has examined these failures across disparate fields (e.g., statistics, visualization, and fact-checking), the community lacks a holistic lens to explain how these issues arise, propagate, and c

Record details

Published: 12 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Bias & fairness

How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process

arXiv cs.CY · 14 July 2026

Shared topic: Bias & fairness

Mitigating LLM Sycophancy in Code Smell Detection Using Evidence-Guided Reasoning Prompts

arXiv · 11 July 2026

Shared topic: Bias & fairness

Normative Alignment of Recommender Systems via Internal Label Shift

arXiv fairness query · 12 July 2026

Shared topic: Bias & fairness

Teacher-regulated generative AI support, student agency, and perceived learning gains in higher education: the moderating role of perceived fairness

Frontiers in Artificial Intelligence · 13 July 2026

Shared topic: Bias & fairness

Balancing fairness and influence spread in social networks: a multi-objective evolutionary approach

Artificial Intelligence Review · 11 July 2026

Shared topic: Bias & fairness

SynCLIP: Synonym-Coherent Language-Image Pretraining for Robust Open-Vocabulary Dense Perception

arXiv · 13 July 2026

How to cite this record

ethics.ai (12 July 2026), “How Data Narratives Go Wrong: A Taxonomy of Issues Across the Data Communication Process,” evidence record 3043, https://ethics.ai/record/3043 (originally published by arXiv cs.HC).

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