Introduction
Digital transformation plays a decisive role in virtually every aspect of social life. In this process, algorithms in particular make significant contributions to the rapid decision-making processes of individuals, institutions, and businesses. Accordingly, algorithmic systems have become an integral part of daily life by influencing decision-making processes in many fields, such as healthcare, education, finance, employment, governance, and social media. On the other hand, algorithms are not merely neutral mechanisms that process data, as is often assumed. Research on the subject indicates that algorithms can become tools that reproduce and even reinforce existing social inequalities.
Algorithmic bias arises when automated systems systematically produce unfair outcomes for specific individuals or groups. These biases typically stem from historical inequalities in training data, design processes, and implementation contexts. Disadvantaged groups may be disproportionately affected by these biases based on factors such as gender, race, ethnicity, socioeconomic status, and geographic location. As algorithmic decision-making processes expand, understanding how these systems shape social structures and power dynamics becomes increasingly important. However, algorithms should not be viewed merely as technical tools.
These systems are also socio-technical structures shaped by institutional priorities, economic interests, and political dynamics. The growing prevalence of artificial intelligence applications, platform economies, and data-driven governance processes has brought discussions on transparency, accountability, justice, and ethical responsibility into sharper focus.
This edited volume, titled Algorithmic Bias, Power, and Inequality in the Digital Age, aims to examine the complex relationships between algorithmic technologies, power structures, and social inequalities. The book seeks to bring together interdisciplinary studies addressing the social impacts of algorithmic systems.
In this context, theoretical, empirical, and methodological contributions from communication studies, media studies, sociology, political science, computer science, and related fields are encouraged. By bringing together diverse perspectives, this book aims to contribute to the development of more fair, transparent, and responsible algorithmic systems in the digital age.