Platform Governance and Algorithmic Self-Preferencing by Dominant Digital Marketplaces

ABSTRACT This paper addresses the challenges of self-preferencing through algorithms in digital platforms, drawing out implications for competition, consumer welfare, and the regulatory environment. With digital in today's world increasingly shaping business dynamics, their proprietary algorithms channel out market dynamics and the underlying influence in favour of their own products vs. its competitors. Combined with the evolution of the regulatory framework and specific focus on EU Digital Markets Act (DMA) and EU AI Act, introducing more transparency and fairness in algorithmic practices. Using the methods of systematic literature review, a case study and comparative legal analysis, the research describes the different ways of self-preferencing, quantifies its effects on consumers' decisions and examines the competitive disadvantage of smaller market actors. The results expose a lower level of transparency in operations and major adaptive algorithmic variants to make regulator oversight even more difficult. While some difficulties remain in the way, self-preferencing-removing accountability devices which include independent audits and greater transparency reporting are demonstrating success in reducing the adverse impacts of self-preferencing. The paper also presents policy recommendations to promote a fair and balanced digital marketplace, stressing the importance of establishing strong regulatory frameworks, multistakeholder governance and ethical algorithm design to support fair competition and consumer interests in the digital economy.