---
esco_uri: http://data.europa.eu/esco/skill/e9575f7f-053a-439e-8074-f93df3334e92
esco_label: "data ethics"
relation: essential
onet_soc: 15-2051.00
source: model-knowledge
confidence: high
qa_count: 0
generator: gemma3:27b (prompt-designed and spot-checked by Claude)
generated: 2026-07-10
---

# data ethics — Data Scientist

For a Data Scientist, 'Data Ethics' isn’t about abstract philosophical debates; it’s woven into *every* stage of a project. It means proactively identifying potential harms stemming from your data work – bias in algorithms leading to unfair outcomes (e.g., loan applications, hiring tools), privacy violations through improper anonymization or re-identification, and the misuse of predictive models for discriminatory purposes. Daily tasks involve critically evaluating datasets for representational biases *before* modeling, documenting assumptions about fairness metrics, and considering the societal impact of your predictions. Tools like Aequitas or Fairlearn can help audit models, but they're just part of it – ethical judgment is key.

## Weitere Anreicherung

Stage-2 source for future practitioner grounding: arXiv cs.LG/stat.ML (ML preprints).
