Intersectionality and reflexivity-decolonizing methodologies for the data science process

Area of Science:

• Social Sciences
• Data Science Ethics
• Digital Humanities

Background:

• The #metoo movement's viral nature presented unique challenges for data science analysis.
• Existing data science methodologies may overlook critical social dynamics and power structures.

Discussion:

• Intersectionality as a methodology is crucial for uncovering hidden biases in data analysis.
• Standard data science tasks can inadvertently perpetuate systemic inequities.
• Data scientists must critically examine their role within power structures.

Key Insights:

• Intersectionality exposes the limitations of conventional data science approaches when applied to social movements.
• Analyzing the #metoo movement through an intersectional lens reveals significant nuances and inequities.
• A reflexive approach is necessary for data scientists to understand their complicity in existing systems.

Outlook:

• Future data science research must integrate intersectionality to ensure equitable and comprehensive analysis.
• Developing new methodologies that embrace intersectionality can lead to more responsible data science.
• Data scientists should actively work to decolonize their practices and challenge power structures.