A multi-disciplinary perspective on the built environment: Space Syntax and cartography – the communication challenge

8-11 June 2009

Sensemaking Practices in the Everyday Work of AI/ML Software Engineering

This paper considers sensemaking as it relates to everyday software engineering (SE) work practices and draws on a multi-year ethnographic study of SE projects at a large, global technology company building digital services infused with artificial intelligence (AI) and machine learning (ML) capabilities. Our findings highlight the breadth of sensemaking practices in AI/ML projects, noting developers' efforts to make sense of AI/ML environments (e.g., algorithms/methods and libraries), of AI/ML model ecosystems (e.g., pre-trained models and "upstream"models), and of business-AI relations (e.g., how the AI/ML service relates to the domain context and business problem at hand). This paper builds on recent scholarship drawing attention to the integral role of sensemaking in everyday SE practices by empirically investigating how and in what ways AI/ML projects present software teams with emergent sensemaking requirements and opportunities

The Grammar of Interactive Explanatory Model Analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits. Combined, these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table

Examining Collegiality and Social Justice in Academia and the Private Sector: an Exploratory SYMLOG Analysis

This research compares the perceptions of the private sector, high-technology employees to the perceptions of university faculty members regarding organizational culture, social justice and collegiality concepts. The SYMLOG assessment technique was used to record the perceptions of respondents to four different concepts of organizational culture, two different aspects of social justice and two measures of collegiality. Comparative findings of gender differences across the eight concepts raise key organizational culture, legal, measurement, governance, and social policy issues for academia and high tech organizations. The development of a conceptual framework to guide future research and a blueprint to discuss desired organizational change are highlighted

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

Innovation as a Nonlinear Process, the Scientometric Perspective, and the Specification of an "Innovation Opportunities Explorer"

The process of innovation follows non-linear patterns across the domains of science, technology, and the economy. Novel bibliometric mapping techniques can be used to investigate and represent distinctive, but complementary perspectives on the innovation process (e.g., "demand" and "supply") as well as the interactions among these perspectives. The perspectives can be represented as "continents" of data related to varying extents over time. For example, the different branches of Medical Subject Headings (MeSH) in the Medline database provide sources of such perspectives (e.g., "Diseases" versus "Drugs and Chemicals"). The multiple-perspective approach enables us to reconstruct facets of the dynamics of innovation, in terms of selection mechanisms shaping localizable trajectories and/or resulting in more globalized regimes. By expanding the data with patents and scholarly publications, we demonstrate the use of this multi-perspective approach in the case of RNA Interference (RNAi). The possibility to develop an "Innovation Opportunities Explorer" is specified.Comment: Technology Analysis and Strategic Management (forthcoming in 2013

The impact of Digital Platforms on Business Models: an empirical investigation on innovative start-ups

Digital platforms have the ability to connect people, organizations and resources with the aim of facilitating the core interactions between businesses and consumers as well as assuring a greater efficiency for the business management. New business concepts, such as innovative start-ups, are therefore created based on innovation, scalability and the relationships within the community around them. The purpose of this work is to deeply understand the evolution of business models brought by innovative and dynamic companies operating through online platforms. In order to achieve the objectives set, an exploratory multiple-case study was designed based on in-depth structured interviews. The aim was to conduct a mixed analysis, in order to rely both on qualitative and quantitative data. The structured interview protocol was therefore designed to collect and then analyse data concerning the company profile and managers’ perspectives on the phenomenon of interest. The interview protocol was submitted in advance and then face-to-face interviews were carried out with the following professional figures: Chief Executive Officer (CEO), General Manager, Chief Technology Officer (CTO), Marketing Manager and Developers. Collected data were analysed and processed through the Canvas Business Model in order to clearly outline similarities and differences among the sample. Results can be considered under two viewpoints. On the one hand, this work provides a detailed overview of the companies interviewed, according to the dimensions of: reference market dynamics, type and number of customers, scalability. On the other one, they allow to identify some success patterns regarding key activities, key resources, channel mix strategy, costs management, value proposition, customer segmentation, key partners and the way to obtain revenues. Results from the multiple-case study with 15 Italian start-ups provide interesting insights by comparing the innovative business models developed and highlighting key differences and similarities. verall, the start-ups analyzed, operating in several sectors, showed great growth prospects and the possibility to create value for their customers through innovative products and services offered through digital platforms