Name: Meenu Gupta
Country: India

Year: 2026
Volume: Volume-11, Issue-1, January-June 2026
Page Number: 666-674
DOI: https://doi.org/10.5281/zenodo.20503295

1. INTRODUCTION
The rapid advancement and widespread adoption of Artificial Intelligence (AI) into various aspects of human life have evoked both excitement and apprehension among users (Li et al., 2022). On one hand, AI holds the promise to improve efficiency, uncover novel insights, and facilitate more informed decision-making (Sanderson et al., 2023). On the other hand, its deployment raises significant concerns regarding social justice, equity, and the well-being of all individuals within society (Crivellaro & Dix, 2021). A growing body of research has delved deep into the complex relationship between AI and social justice, showcasing how the technology can both uphold and contest existing power hierarchies based on social differences such as race, gender, nationality, and socioeconomic status (AI Now Institute, 2023). The scholarly community have empirically establish that the embedded biases and structural inequalities within the algorithms and data can underpin AI systems, resulting into discriminatory outcomes that may have adverse impact on the interest of marginalized communities (Hajian et al., 2016; Mills & Whittaker, 2019).
Concurrently, the potential of AI to improve transparency, accountability, and evidence-driven policymaking has also been recognized as valuable tool for addressing the existing social injustice present in the society (Min, 2023 ; Savaşan, n.d.). The challenge lies in striking a careful balance, one that harnesses the benefits of AI while minimising its risks and unforeseen consequences. Several case studies have showcased the convergence of data bias and discrimination in AI. In the realm of employment, AI-driven recruitment practices have been shown to amplify gender and racial biases, disadvantaging women and minorities during the recruitment process (Avery et al., 2023; Yarger et al., 2019). In the same vein, within the criminal justice system, predictive policing algorithms have been criticized for elevating the existing racial disparities, resulting into the over-surveillance of different communities (Purves, 2022; Richardson et al., 2019). The healthcare sector is also struggling with the growing issues of biased AI models, leading to adverse impact on underserved communities (Samorani & Blount, 2020 ; Ibrahim et al., 2020).These instances highlights the need for adopting a comprehensive, multi-stakeholder approach for addressing the challenges arising due to widespread adoption of AI in different domains. Technical solutions alone are not sufficient to address these issues rather a holistic approach must be adopted which incorporate elements of data governance, algorithmic accountability, and substantive community engagement (Aizenberg & Hoven, 2020; Sanderson et al., 2023; Leavy et al., 2020; Aizenberg & Hoven, 2020). As AI continues to evolve and permeate various aspects of society, it is imperative that we proactively understand and tackle with these complex issues. Fostering a deeper understanding of the relationship between AI and social justice, and developing effective strategies to leverage the benefits of the evolving technology while alleviating its risks, will be the key factor in shaping a more equitable and inclusive future for the coming generation (Burgess & Owen, 2004; Aizenberg & Hoven, 2020; Crivellaro & Dix, 2021).
The objective of the current study is to have a deep dive into the academic landscape of AI and explore the multifaceted relationship between AI and social justice, examining both the potential benefits and the inherent risks.