Gender-based data bias is a pervasive problem that affects our understanding and ability to address issues of gender inequality. Gender-based data bias occurs when women or gender minorities are systematically excluded or underrepresented in datasets, leading to inaccurate or incomplete knowledge about their experiences and needs. This bias can take many forms, including inadequate data collection methods, biased data analysis, and biased data interpretation (Kozlowski et al., 2020).
International Women’s Day is a time to reflect on progress towards gender equality and to address the ongoing challenges that women face. One of the key challenges is gender-based data bias, which refers to how data collection, analysis, and interpretation can perpetuate or amplify gender- based inequalities. This bias can have serious consequences for women’s health, economic opportunities, accessing education, and social status, and can limit our understanding of the experiences and needs of women across diverse contexts.
This discussion paper explores gender-based data bias from multiple perspectives, including healthcare, workplace, education, public participation, and disability/accessibility. We will examine how gender-based data bias manifests in different contexts and intersects with other forms of bias and discrimination. Some of the key questions we consider include the following:
What are the consequences of gender-based data bias, and how does it perpetuate gender inequalities?
How does gender-based data bias manifest in different sectors, such as healthcare and business, and what are the specific consequences of this bias?
How do other forms of bias and discrimination, such as racism and ableism, intersect with gender- based data bias, and what are the consequences for individuals who belong to multiple marginalised groups?
What are some strategies for addressing gender- based data bias and creating more inclusive and equitable data practices?
To address these questions, we will draw on various academic sources, including studies on gender- based data bias in healthcare, business, education, public participation, and disability/accessibility, as well as broader discussions of intersectionality and social justice. Through this exploration, we hope to shed light on how gender-based data bias perpetuates and reinforces gender inequalities and to identify strategies for creating more equitable and inclusive data practices that promote gender equality. Therefore, it is essential to examine the extent and consequences of gender-based data bias and to explore potential solutions to this issue.
This discussion paper will also review the current state of knowledge on gender-based data bias, its consequences for gender equality, and potential solutions, including the need for improved data collection methods, greater diversity in data analysis teams, and increased transparency in data reporting.
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