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Diversity in Data Science: A Systemic Inequality

1 December 2020
Blog
Publisher : Towards Data Science

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This blog post examines the systemic inequalities prevalent in the field of data science, particularly concerning diversity in terms of race, gender, and ethnicity. It delves into the various factors contributing to the lack of representation, such as unconscious bias in hiring, limited access to education, and exclusionary workplace cultures. The article advocates for a critical examination of these systemic issues and proposes strategies to foster a more equitable and inclusive environment within the data science community, emphasizing its importance for ethical AI development.

Author(s) :

Luciano Vilas Boas

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Key topics

Education and Skill Development, Social Justice, Philanthropy and Human Rights

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Ethical Governance, Accountability, and Compliance Organisational Culture, People, and Leadership Development

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