This chapter explores emerging social justice-oriented frameworks in data and design to advance the Accessible and Inclusive Artificial Intelligence (AI2) initiative. As artificial intelligence (AI) becomes increasingly embedded in social systems, issues related to bias, privacy, and usability highlight the necessity of inclusive design practices. AI bias stems from both training datasets and the lack of diversity among development teams, resulting in discriminatory outcomes in healthcare, employment, and criminal justice. Traditional ethical frameworks predominantly focus on individual accountability; however, scholars advocate for a shift toward “distributed responsibility” among designers, developers, and users. Participatory governance, prioritizing accessibility over technological progress, is a critical strategy for addressing AI bias. This chapter examines multiple frameworks within Data and Justice fields which claim to empower marginalized communities in data control and design processes. However, challenges such as tokenism, commodification, and persistent power imbalances complicate implementation. Employing a rapid review methodology, this chapter identifies best practices and existing limitations within justice-oriented AI development. This review concludes by recommending a framework for social justice-oriented practices for accessible and inclusive artificial intelligence.