Bias and Discrimination in Data
Data Science · Further Education · Data Ethics and Privacy
This unit explores the nature and impact of bias and discrimination in data, examining how they can lead to unfair or discriminatory outcomes. Students will learn techniques to detect and mitigate bias, ensuring the responsible and ethical use of data in data science applications.
Zum Üben gibt es 52 Aufgaben. Jede Antwort bekommt sofort eine Rückmeldung, kostenlos und ohne Anmeldung.
Das gehört dazu
- Sources of Bias
- Types of Bias (e.g.
- sampling bias
- selection bias
- measurement bias)
- Consequences of Bias (e.g.
- unfair predictions
- discriminatory outcomes)
- Bias Mitigation Techniques (e.g.
- data preprocessing
- algorithmic fairness)
- Ethical Considerations in Data Science
Stichworte
- Bias
- Discrimination
- Fairness
- Ethics
- Data Science
- Machine Learning
- Algorithms