This subtopic covers ethical considerations when designing data instrumentation, such as:
- Privacy Compliance: Data instrumentation must comply with data privacy regulations, such as GDPR and FERPA, which dictate how personal information should be collected, stored, and shared. Learning engineering teams must ensure that learner data is handled securely and that privacy policies are communicated transparently to users. This has implications for data security (see 7.4.6 Security Considerations in Data Handling).
- Ethical Data Use: Ethical considerations are paramount in data instrumentation. Learning engineers must ensure that data collection is consensual, transparent, and non-exploitative. Sensitive data should be collected only when it is necessary and beneficial to the learner, and robust anonymization techniques should be applied to protect learners' identities.
- Bias and Fairness: When designing data collection methods, learning engineers should consider potential biases that could arise from the types of data collected or the ways data is analyzed. Bias in data can lead to inequitable learning outcomes, especially if certain groups of learners are underrepresented or overrepresented in the data.