Description: Tokenism is the practice of making only a superficial effort to be inclusive of members of marginalized groups. This strategy is often used in organizational and social contexts, where the aim is to give the impression of diversity and equity without implementing meaningful changes in power structures or internal policies. Tokenism can manifest in various ways, such as including one or two individuals from underrepresented groups in a team but without granting them real decision-making power or influence. This practice is problematic not only because it perpetuates inequality but also because it can result in a lack of authenticity in diversity initiatives. In the realm of artificial intelligence (AI), tokenism can be reflected in how models are developed and trained, where data from diverse demographics are included but without a deep analysis of the biases that may be present in that data. In summary, tokenism is a form of superficiality in inclusion that can have negative consequences for both individuals and organizations, as it does not address the roots of inequality and can lead to greater distrust between marginalized groups and the institutions that claim to represent them.
History: The term ‘tokenism’ began to gain popularity in the 1970s, especially in the context of civil rights and gender equality movements. It was used to describe the symbolic inclusion of individuals from minority groups in organizations that were otherwise predominantly homogeneous. Over the years, the concept has evolved and been applied to various areas, including corporate and academic settings, where the lack of real commitment to diversity has been criticized.
Uses: Tokenism is primarily used in organizational and social contexts to give the impression of diversity and equity. In the business realm, it can manifest in hiring employees from underrepresented groups without providing them with real opportunities for leadership or influence. In education, it can be seen in the inclusion of students from diverse ethnic backgrounds in programs without adequate support for their success. It is also observed in advertising campaigns that superficially present diversity to attract a broader audience.
Examples: An example of tokenism is the inclusion of one woman or a person of color on a board that is predominantly composed of white men, without this individual having a voice in important decisions. Another case is the representation of minorities in movies or television series, where characters are stereotypes and do not reflect the complexity of their experiences. In the realm of AI, a model trained on data from diverse ethnicities but that does not address the inherent biases in that data is also an example of tokenism.
