<p>The expansion of occupational safety, environmental reporting, and supply chain compliance requirements in logistics has created a structural asymmetry: small operators face regulatory obligations comparable to those of large firms while lacking the infrastructure, personnel, and capital to meet them. Artificial intelligence tools promise to close this gap by converting existing operational data into actionable compliance outputs. Yet the ethical dimensions of this technological shift remain underexamined in the literature. This study presents a systematic review of 89 articles drawn from Scopus, Web of Science, IEEE Xplore, Google Scholar, and the OECD iLibrary, supplemented by regulatory document analysis and practitioner field observation. Analysis was conducted through manual thematic coding following PRISMA 2020 guidelines. Findings are organized across five thematic areas: compliance burden asymmetry, AI tool adoption barriers, liability gaps when AI safety tools fail, compliance as competitive exclusion, and regulatory fragmentation between EU and U.S. approaches. The two most original contributions of this study concern the liability gap in platform logistics, where AI tool failures create unresolved questions about responsibility allocation among vendors, platforms, and small operators, and the competitive exclusion dynamic, where compliance requirements function as barriers against small operators in a pattern consistent with regulatory capture theory. Drawing on these findings, the study proposes a four-principle ethical framework grounded in shared liability, tiered compliance, public infrastructure investment, and transparency obligations, and demonstrates its operational feasibility through a detailed application to the tiered compliance principle. Limitations of the study and directions for future empirical research are discussed.</p>