Small Business and Intellectual Asset Governance: An Integrated Analytical Framework

Having identified that there exists, as yet, no MaturityModel for Intellectual Asset (IA) Governance in Small andMedium Enterprises (SMEs), the authors have attempted todevelop theoretically one such Model and present it in this paper.Twelve dimensions of IA governance and enterpriseinfrastructure for IA governance were identified. The model alsodistinguishes among five archetypes according to their level ofsophistication. Initial testing of the model with small andmedium enterprises indicates that it provides insights into howenterprises approach intellectual governance and could be of useto businesses and policymakers alike

Communicating strategically – talking less, targeting better Qualitative study on corporate communication’s learning in leading global companies

Companies need to communicate strategically in order to maintain dialogue and relationships with their stakeholders. In the crowded media and social media space the messages disappear in the noise generated by multiple actors. Therefore, to be heard the enterprises need to consider their communication strategically. It is not about the amount of information; it is about right targeting and usage of the right tools and channels. Social media allowed the companies to communicate directly with their stakeholders and customers. Different channels can address different stakeholders. This study focuses on a qualitative assessment of the learning patterns and profiles among 60 world leading companies. It includes enterprises from different countries and industries but with international scope of operations. The study proposes a maturity model for corporate communications strategic management

Making Intelligence: Ethics, IQ, and ML Benchmarks

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review

Communicating strategically – talking less, targeting better. Qualitative study on corporate communication‘s learning in leading global companies

Companies need to communicate strategically in order to maintain dialogue and relationships with their stakeholders. In the crowded media and social media space the messages disappear in the noise generated by multiple actors. Therefore, to be heard the enterprises need to consider their communication strategically. It is not about the amount of information, it is about right targeting and usage of the right tools and channels. Social media allowed the companies to communicate directly with their stakeholders and customers. Different channels can address different stakeholders. This study focuses on a qualitative assessment of the learning patterns and profiles among 60 world leading companies. It includes enterprises from different countries and industries but with international scope of operations. The study proposes a maturity model for corporate communications strategic management

Antecedents of E-Business Assimilation in Manufacturing SMEs

In order to further our knowledge on the use of the Internet and the Web in manufacturing SMEs, the present research seeks through an empirical study of 108 Canadian firms to explore the following questions: For what purposes are the Internet and the Web presently used, i.e., to what extent are e-business functions assimilated in manufacturing SMEs? What characteristics of the SMEs’ environmental, strategic, managerial, operational, and technological context are associated with e-business assimilation

Making Intelligence: Ethics, IQ, and ML Benchmarks

The ML community recognizes the importance of anticipating and mitigating the potential negative impacts of benchmark research. In this position paper, we argue that more attention needs to be paid to areas of ethical risk that lie at the technical and scientific core of ML benchmarks. We identify overlooked structural similarities between human IQ and ML benchmarks. Human intelligence and ML benchmarks share similarities in setting standards for describing, evaluating and comparing performance on tasks relevant to intelligence. This enables us to unlock lessons from feminist philosophy of science scholarship that need to be considered by the ML benchmark community. Finally, we outline practical recommendations for benchmark research ethics and ethics review

A Framework for Assurance Audits of Algorithmic Systems

An increasing number of regulations propose AI audits as a mechanism for achieving transparency and accountability for artificial intelligence (AI) systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently lacks agreed-upon practices, procedures, taxonomies, and standards. We propose the criterion audit as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue that AI audits should similarly provide assurance to their stakeholders about AI organizations' ability to govern their algorithms in ways that mitigate harms and uphold human values. We discuss the necessary conditions for the criterion audit and provide a procedural blueprint for performing an audit engagement in practice. We illustrate how this framework can be adapted to current regulations by deriving the criteria on which bias audits can be performed for in-scope hiring algorithms, as required by the recently effective New York City Local Law 144 of 2021. We conclude by offering a critical discussion on the benefits, inherent limitations, and implementation challenges of applying practices of the more mature financial auditing industry to AI auditing where robust guardrails against quality assurance issues are only starting to emerge. Our discussion -- informed by experiences in performing these audits in practice -- highlights the critical role that an audit ecosystem plays in ensuring the effectiveness of audits