AI management an exploratory survey of the influence of GDPR and FAT principles

As organisations increasingly adopt AI technologies, a number of ethical issues arise. Much research focuses on algorithmic bias, but there are other important concerns arising from the new uses of data and the introduction of technologies which may impact individuals. This paper examines the interplay between AI, Data Protection and FAT (Fairness, Accountability and Transparency) principles. We review the potential impact of the GDPR and consider the importance of the management of AI adoption. A survey of data protection experts is presented, the initial analysis of which provides some early insights into the praxis of AI in operational contexts. The findings indicate that organisations are not fully compliant with the GDPR, and that there is limited understanding of the relevance of FAT principles as AI is introduced. Those organisations which demonstrate greater GDPR compliance are likely to take a more cautious, risk-based approach to the introduction of AI

The Scaling Mindset – Shifting from Problems to Solutions. Insights from the Review of CCAFS Scaling Activities, 2019

In the frame of the review of CCAFS scaling activities in 2019, 21 project leaders and –implementers were interviewed about their scaling processes, touching a series of aspects that had been identified as crucial and/or critical by earlier research. Results were analysed with a systemic approach, to draw organisational learnings. The findings were validated with CCAFS core team during their Scaling Workshop in Madrid, May 2019, in which the Core Team also prioritized its programmatic areas of response. This working paper captures the main insights and learnings from both the interviews on project level, followed by the results’ analysis. It then summarized the Core Team workshop’s main discussion points and shortly outlines the programmatic areas of response that CCAFS identified. The learnings and insights on the realities of scaling agricultural innovations presented in this working paper can provide a rich basis for further synthesis and/or deeper research on the different aspects of innovation development and scaling

Exploring the Applicability of Test Driven Development in the Big Data Domain

Big data analytics and the according applications have gained huge importance in daily life. This results on the one hand from their versatility and on the other hand from their capability to greatly improve an organization’s performance when utilized appropriately. However, despite their prevalence and the corresponding attention through practitioners as well as the scientific world, the actual implementation still remains a challenging task. Therefore, without the adequate testing, the reliability of the systems and thus the obtained outputs is uncertain. This might reduce their utilization, or even worse, lead to a diminished decision-making quality. The publication at hand explores the adoption of test driven development as a potential approach for addressing this issue. Subsequently, using the design science research methodology, a microservice-based test driven development concept for big data (MBTDD-BD) is proposed. In the end, possible avenues for future research endeavours are indicated

The regulation of AI trading from an AI life cycle perspective

Among innovative technologies, Artificial Intelligence (AI) is often avouched as the game changer in the provision of financial services. In this regard, the algorithmic trading domain is no exception. The impact of AI in the industry is a catalyst for transformation in the operations and the structure of capital markets. In effect, AI adds a further layer of system complexity, given its potential to alter the composition and behaviour of market actors, as well as the relationships among them. Despite the many expected benefits, the wide use of AI could also impose new and unprecedented risks to market participants and financial stability. Specifically, owing to the potential of AI trading to disrupt markets and cause harm, global financial regulators are faced today with the daunting task of how best to approach its regulation in order to foster innovation and competition without sacrificing market stability and integrity. While there are common challenges, each market player faces problems unique to the context-specific use of AI. In other words, there are no one-size-fits-all solutions for regulating AI in automated trading. Rather, any effective and future-proof AI-targeting regulation should be proportionate to the particular and additional risks arising from specific applications (eg, due to the specific AI methods applied with their respective capability, validity and criticality). Therefore, financial regulators face a multi-faceted challenge. They must first define the additional risks posed by specific use cases that call for more in-depth scrutiny and, hence, identify the technical specificities that can facilitate the occurrence of those risks. Based on this assessment, they finally need to determine which AI characteristics require special regulatory treatment. Inspired by the EU AI Act proposal, this paper examines the advantages of a ‘rule-based’ and ‘risk-oriented’ regulatory approach, combining both ex-ante and ex-post regulatory measures, that needs to be put in perspective with the ‘AI life cycle’. By advocating for a multi-stakeholder engagement in AI regulatory governance, it proposes a way forward to assist financial regulators and industry players – but even actors in public education – in understanding, identifying and mitigating the risks associated with automated trading through an engineering approach for the purpose of complexity mastering

Carving out new business models in a small company through contextual ambidexterity: the case of a sustainable company

Business model innovation (BMI) and organizational ambidexterity have been pointed out as mechanisms for companies achieving sustainability. However, especially considering small and medium enterprises (SMEs), there is a lack of studies demonstrating how to combine these mechanisms. Tackling such a gap, this study seeks to understand how SMEs can ambidextrously manage BMI. Our aim is to provide a practical artifact, accessible to SMEs, to operationalize BMI through organizational ambidexterity. To this end, we conducted our study under the design science research to, first, build an artifact for operationalizing contextual ambidexterity for business model innovation. Then, we used an in-depth case study with a vegan fashion small e-commerce to evaluate the practical outcomes of the artifact. Our findings show that the company improves its business model while, at the same time, designs a new business model and monetizes it. Thus, our approach was able to take the first steps in the direction of operationalizing contextual ambidexterity for business model innovation in small and medium enterprises, democratizing the concept. We contribute to theory by connecting different literature strands and to practice by creating an artifact to assist managemen

Women and ICT: exploring obstacles and enablers of a possible career

The ICT industry is a key contributor to the EU’s economy. Unfortunately, women’s presence is low overall and it decreases as they climb the corporate ladder. Underrepresentation of women in ICT is a research area that has received attention mostly in U.S.A., UK and some European countries. This phenomenon, termed “IT gender gap”, has not received much attention in Italy, yet. Therefore, the purpose of this study is doing an “initial” research and understanding the characteristics of the IT workplace culture in Italy. Based on the international research, a framework and a questionnaire have been developed. To test the questionnaire, a first research sample (without any statistical relevance compared to the Italian context) has been created and the potential respondents were contacted via email. Data analysis discusses the workplace environmental factors that hinder and support the career development of women in ICT in this country. Understanding the limitations of this research project has given rise to some open points that deserve being analysed and further explored

Heuristic usability evaluation on games: a modular approach

Heuristic evaluation is the preferred method to assess usability in games when experts conduct this evaluation. Many heuristics guidelines have been proposed attending to specificities of games but they only focus on specific subsets of games or platforms. In fact, to date the most used guideline to evaluate games usability is still Nielsen’s proposal, which is focused on generic software. As a result, most evaluations do not cover important aspects in games such as mobility, multiplayer interactions, enjoyability and playability, etc. To promote the usage of new heuristics adapted to different game and platform aspects we propose a modular approach based on the classification of existing game heuristics using metadata and a tool, MUSE (Meta-heUristics uSability Evaluation tool) for games, which allows a rebuild of heuristic guidelines based on metadata selection in order to obtain a customized list for every real evaluation case. The usage of these new rebuilt heuristic guidelines allows an explicit attendance to a wide range of usability aspects in games and a better detection of usability issues. We preliminarily evaluate MUSE with an analysis of two different games, using both the Nielsen’s heuristics and the customized heuristic lists generated by our tool.Unión Europea PI055-15/E0

Ecosystem of Innovation in Industry 4.0: The case of collaborations in Startups in Brazil

The article identifies how Research and Development (R&D) collaborations in startups can influence digital innovation in Brazilian manufactures. A qualitative multiple case study was performed with startups incubated at the Federation of Industries of Paraná (FIEP), through semi-structured interviews to the Chief Executive Officer (CEOs) and case document’s, applying the content analysis. The results indicate that the sources of knowledge of the startups and the collaboration with companies, universities, government development agencies and incubators, characterize the actions in the ecosystem of open innovation. It has been found that the complexity of the innovation ecosystem of startups is a strategic asset, and the nature of the collaborations is informal, coupled with a stage of maturity considered low in startups. This study contributes to highlight the nature, dynamics and progress of startup collaborations in the development of digital transformation, and the challenges for the leverage of Industry 4.0 in Brazil