Financial Characteristics of Companies Audited by Large Audit Firms

Purpose “ The purpose of this paper is to examine how financial characteristics associated with the choice of a big audit firm with further investigation on the agency costs of free cash flows.Design/methodology/approach “ The sample used for this work includes industrial listed companies from Germany and France. To test our hypothesis, we used a number of logit models, extending the standard model selection audit firm, to include the variables of interest. Following previous work, our dependent dummy variable is Big4 or non-Big4.Findings “ We observed that most independent variables in the German companies show similar results to previous work, but we did not have the same results for the French industry. Moreover, our findings suggest that the total debt and dividends can be an important reason for determining the choice of a large audit firm, reducing agency costs of free cash flows.Research limitations/implications “ This study has some limitations on the measurements of the cost of the audit fees and also generates opportunities for additional searching.Originality/value “ The paper provides only one aspect to explain the relationship between the problems of agency costs of free cash flow and influence in choosing a large auditing firm, which stems from investors\u27 demand for higher quality audits

Trustworthy AI Inference Systems: An Industry Research View

In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing current trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems

Trustworthy AI Inference Systems: An Industry Research View

In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to aid their decision, while at the same time utilizing appropriate security protection mechanisms for AI models. Additionally, such systems should also use Privacy-Enhancing Technologies (PETs) to protect customers' data at any time. To approach the subject, we start by introducing trends in AI inference systems. We continue by elaborating on the relationship between Intellectual Property (IP) and private data protection in such systems. Regarding the protection mechanisms, we survey the security and privacy building blocks instrumental in designing, building, deploying, and operating private AI inference systems. For example, we highlight opportunities and challenges in AI systems using trusted execution environments combined with more recent advances in cryptographic techniques to protect data in use. Finally, we outline areas of further development that require the global collective attention of industry, academia, and government researchers to sustain the operation of trustworthy AI inference systems