As Artificial Intelligence (AI) and Data Science (DS) become pervasive, addressing gender disparities and diversity gaps in their workforce is urgent. These rapidly evolving fields have been further impacted by the COVID-19 pandemic, which disproportionately affected women and minorities, exposing deep-seated inequalities. Both academia and industry shape these disciplines, making it essential to map disparities across sectors, occupations, and skill levels. The dominance of men in AI and DS reinforces gender biases in machine learning systems, creating a feedback loop of inequality. This imbalance is a matter of social and economic justice and an ethical challenge, demanding value-driven diversity. Root causes include unequal access to education, disparities in academic programs, limited government investments, and underrepresented communities' perceptions of elite opportunities. This chapter examines the participation of women and minorities in AI and DS, focusing on their representation in both industry and academia. Analyzing the existing dynamics seeks to uncover the collective and individual impacts on the lives of women and minority groups within these fields. Additionally, the chapter aims to propose actionable strategies to promote equity, diversity, and inclusion (DEI), fostering a more representative and supportive environment for all.

翻译：随着人工智能（AI）与数据科学（DS）的普及，解决其从业者中的性别差异与多样性差距已刻不容缓。这些快速发展的领域进一步受到COVID-19疫情的影响，该疫情对女性和少数群体造成了不成比例的冲击，暴露出深层次的不平等。学术界与产业界共同塑造了这些学科，因此必须跨部门、跨职业和跨技能水平地描绘差异。男性在AI和DS领域的主导地位强化了机器学习系统中的性别偏见，形成了不平等的反馈循环。这种失衡不仅是社会与经济公正问题，也是一项伦理挑战，亟需以价值观驱动的多样性。根本原因包括教育机会不均、学术项目差异、政府投资有限以及少数群体对精英机会的认知不足。本章审视女性和少数群体在AI与DS领域的参与情况，重点关注他们在产业界和学术界的代表性。通过分析现有动态，旨在揭示这些领域对女性和少数群体生活产生的集体与个体影响。此外，本章力求提出可操作的策略，以促进公平、多样性与包容性（DEI），为所有人营造更具代表性且支持性的环境。