C1-M4 - Understand Data and Fairness

Understanding Fairness in Data Analysis

Definition of Fairness: Ensuring analysis does not create or reinforce bias.
Analyst Responsibility: Create systems that are fair and inclusive for everyone.

Variability of Definition: Fairness does not have a single standard definition in data analytics.
True Yet Unfair Conclusions: Data conclusions can be accurate but still unfair due to external factors.

Company Culture: Notorious for lacking gender representation.
- Data Collection: Analyzing employee performance and culture.
- Initial Conclusion: That hiring more men is necessary based on their success rates.
Issues with Conclusion:
- Ignores the overall context of company culture.
- Fails to consider difficulties faced by employees of varying gender identities.
- Neglects systemic factors contributing to unequal success rates.

Critical Reflection: Analysis must address underlying problems to create fair outcomes.
Alternative Conclusion: Recognizes that toxic culture prevents diverse employees from succeeding and needs to be addressed to improve performance.

Responsibility: Analysts must ensure analyses factor in complicated social contexts to avoid bias.
Fairness as a Continuous Process:
- Consider fairness from data collection to presentation of conclusions.

Project Purpose: Develop a mobile platform for tracking cardiovascular disease in the "Stroke Belt" region.
Prioritizing Fairness:
- Collaborating with social scientists for insights into bias.
- Collecting self-reported data separately to mitigate racial bias.
- Oversampling non-dominant groups to ensure representation in the study.
Outcome: Ensured fair data collection and conclusion formulation without negatively impacting studied communities.

Ongoing Learning: The concept of fairness in data analysis will continue to evolve throughout the course.
Practical Application: Students will engage in activities to deepen understanding of fairness in data analysis.