The field of algorithmic auditing is witnessing remarkable growth in response to concerns around discrimination-related harms exhibited by AI systems. However, this expansion has brought ambiguity regarding the definition of "audit" and the scope of evaluations. Although algorithmic audits involve various activities, defining an effective audit can be complicated due to normative, technical, and resource limitations. Challenges abound, from the lack of standardized language to diverse auditor characteristics. Nevertheless, the need for holistic evaluation is imperative as the societal impacts of algorithms become more apparent. Bridging theory and practice, this dissertation seeks to provide insights into and considerations for the algorithmic auditing community. By drawing on the history of audit studies in the social sciences, confronting the challenges of performing an audit in practice, and investigating the broader auditing ecosystem, it explores questions around the purpose of an audit, methodologies and best practices, available resources, community growth, and accountability. By doing so, it aims to improve the efficacy of AI audits and accountability for AI systems.