Academia is far from a meritocratic distribution of opportunities. This leads to inequalities, lack of diversity, and unfairness. The objective of this conceptual paper is to propose an integrative framework to help the academic community address its pervasive but persistent inequalities of opportunities. The framework emerges from the intersections of Bourdieu, Bronfenbrenner, and Rawls frameworks and propose the use of ethical artificial intelligence (AI) to contextualise merit and recreate true equality of opportunities. More specifically, I argue that academia has structures and doxa that may be inaccessible to individuals from different social origins, and are perpetuated by privileged individuals who achieve positions of power within academia. The privileged individuals inherit and are exposed to opportunities to acquire capital from early life, resulting in the continuation of status quo practices and alienation of minorities that do not share—or do not have the ability to acquire—capital. I argue that this process occurs as a result of the social origins of the individual and, as Bronfenbrennian framework suggests, disadvantaged individuals lack both the (inherited) capital, but also lack the ability and opportunities to acquire capital relative to privileged counterparts. I argue that the only way to mitigate this inequitable system is to retrieve the Rawlsian original position of ignorance (veil of ignorance) in the allocation of academic capital based on merit, which can only be objectively quantified relative to social origins of individuals. As opposed to current subjective assessments (e.g., peer-review) or lottery systems, I propose the use of Big Data and ethical AI to reconstruct the position of ignorance and contextualise merit based on the expected merit given individuals’ social origins. I also discuss the concept of ‘years post-PhD’ as it is used to introduce fairness in allocation of academic capital and propose a different and less relativistic landmark that accounts for the years post-first authorship publication. This is a novel conceptual framework which can stimulate further research into the ecology of social justice.

学术界远非精英分配的机会。这会导致不平等、缺乏多样性和不公平。这篇概念性论文的目的是提出一个综合框架，以帮助学术界解决普遍但持续存在的机会不平等问题。该框架来自 Bourdieu、Bronfenbrenner 和 Rawls 框架的交叉点，并建议使用道德人工智能 (AI) 来将优点置于情境中并重新创造真正的机会平等。更具体地说，我认为学术界具有不同社会出身的个人可能无法接触到的结构和信念，并由在学术界获得权力职位的特权个人延续。现状不分享或没有能力获得资本的少数群体的做法和疏远。我认为这一过程是由于个人的社会出身而发生的，正如 Bronfenbrennian 框架所表明的那样，处于不利地位的个人既缺乏（继承的）资本，也缺乏获得资本的能力和机会。我认为，缓解这种不公平制度的唯一方法是恢复罗尔斯最初的无知（无知之幕）在基于优点的学术资本分配中的位置，这只能相对于个人的社会出身进行客观量化。与当前的主观评估（例如，同行评审）或彩票系统相反，我建议使用大数据和道德人工智能来重建无知的位置，并根据给定个人社会出身的预期优点来情境化优点。我还讨论了“博士后年”的概念，因为它被用来引入学术资本分配的公平性，并提出了一个不同的、相对论较少的里程碑来解释第一作者发表后的年数。这是一个新颖的概念框架，可以激发对社会正义生态学的进一步研究。