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

Mapping AI Arguments in Journalism Studies

This study investigates and suggests typologies for examining Artificial Intelligence (AI) within the domains of journalism and mass communication research. We aim to elucidate the seven distinct subfields of AI, which encompass machine learning, natural language processing (NLP), speech recognition, expert systems, planning, scheduling, optimization, robotics, and computer vision, through the provision of concrete examples and practical applications. The primary objective is to devise a structured framework that can help AI researchers in the field of journalism. By comprehending the operational principles of each subfield, scholars can enhance their ability to focus on a specific facet when analyzing a particular research topic

Machine Learning methods in climate finance: a systematic review

Evitar la materialización del cambio climático es uno de los principales retos de nuestro tiempo. En esta tarea, el sector financiero desempeña un papel fundamental, motivando a economistas académicos a desarrollar un nuevo campo de investigación, las finanzas climáticas. A la vez, el uso de tecnologías de aprendizaje automático (ML, por sus siglas en inglés) se ha popularizado para analizar problemas relacionados con las finanzas climáticas, debido principalmente a la necesidad de gestionar un volumen elevado de datos relacionados con el clima, y para modelizar relaciones no lineales entre variables climáticas y económicas. De esta manera, proponemos una revisión de la literatura académica para explorar cómo esta tecnología está posibilitando el crecimiento de las finanzas climáticas. Para ello, primero realizamos una búsqueda sistemática de estudios en esta materia en tres bases de datos científicas. Luego, usando un modelo de identificación automática de temas (Latent Dirichlet Allocation), identificamos estadísticamente siete áreas del conocimiento donde el ML está desempeñando un papel relevante: catástrofes naturales, biodiversidad, riesgo agrícola, mercados de carbono, energía, inversión responsable y datos climáticos. Para finalizar, hacemos un análisis de las principales tendencias de publicación, así como una clasificación de los modelos estadísticos utilizados en función del área de estudio. La principal contribución de este artículo es la provisión de una estructura de temas o problemas solventados gracias al uso del ML en finanzas climáticas, lo cual esperamos que facilite a expertos en esta tecnología la comprensión de las principales fortalezas y limitaciones de dicha tecnología aplicada en este campo de investigación.Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area

FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning

Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement learning is challenging due to three major factors, namely, low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting in the backtesting stage. In this paper, we present an openly accessible FinRL-Meta library that has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we will provide hundreds of market environments through an automatic pipeline that collects dynamic datasets from real-world markets and processes them into gym-style market environments. Second, we reproduce popular papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, FinRL-Meta provides tens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. FinRL-Meta is available at: https://github.com/AI4Finance-Foundation/FinRL-MetaComment: NeurIPS 2022 Datasets and Benchmarks. 36th Conference on Neural Information Processing Systems Datasets and Benchmarks Trac

자동차 사양 변경을 실시간 반영하는 데이터 기반 디자인 접근 방법

학위논문 (박사) -- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2020. 8. 곽노준.The automotive industry is entering a new phase in response to changes in the external environment through the expansion of eco-friendly electric/hydrogen vehicles and the simplification of modules during the manufacturing process. However, in the existing automotive industry, conflicts between structured production guidelines and various stake-holders, who are aligned with periodic production plans, can be problematic. For example, if there is a sudden need to change either production parts or situation-specific designs, it is often difficult for designers to reflect those requirements within the preexisting guidelines. Automotive design includes comprehensive processes that represent the philosophy and ideology of a vehicle, and seeks to derive maximum value from the vehicle specifications. In this study, a system that displays information on parts/module components necessary for real-time design was proposed. Designers will be able to use this system in automotive design processes, based on data from various sources. By applying the system, three channels of information provision were established. These channels will aid in the replacement of specific component parts if an unexpected external problem occurs during the design process, and will help in understanding and using the components in advance. The first approach is to visualize real-time data aggregation in automobile factories using Google Analytics, and to reflect these in self-growing characters to be provided to designers. Through this, it is possible to check production and quality status data in real time without the use of complicated labor resources such as command centers. The second approach is to configure the data flow to be able to recognize and analyze the surrounding situation. This is done by applying the vehicles camera to the CCTV in the inventory and distribution center, as well as the direction inside the vehicle. Therefore, it is possible to identify and record the parts resources and real-time delivery status from the internal camera function without hesitation from existing stakeholders. The final approach is to supply real-time databases of vehicle parts at the site of an accident for on-site repair, using a public API and sensor-based IoT. This allows the designer to obtain information on the behavior of parts to be replaced after accidents involving light contact, so that it can be reflected in the design of the vehicle. The advantage of using these three information channels is that designers can accurately understand and reflect the modules and components that are brought in during the automotive design process. In order to easily compose the interface for the purpose of providing information, the information coming from the three channels is displayed in their respective, case-specific color in the CAD software that designers use in the automobile development process. Its eye tracking usability evaluation makes it easy for business designers to use as well. The improved evaluation process including usability test is also included in this study. The impact of the research is both dashboard application and CAD system as well as data systems from case studies are currently reflected to the design ecosystem of the motors group.자동차 산업은 친환경 전기/수소 자동차의 확대와 제조 공정에서의 모듈 단순화를 통해서 외부 환경의 변화에 따른 새로운 국면을 맞이하고 있다. 하지만 기존의 자동차 산업에서 구조화된 생산 가이드라인과 기간 단위 생산 계획에 맞춰진 여러 이해관계자들과의 갈등은 변화에 대응하는 방안이 관성과 부딪히는 문제로 나타날 수 있다. 예를 들어, 갑작스럽게 생산에 필요한 부품을 변경해야 하거나 특정 상황에 적용되는 디자인을 변경할 경우, 주어진 가이드라인에 따라 디자이너가 직접 의견을 반영하기 어려운 경우가 많다. 자동차 디자인은 차종의 철학과 이념을 나타내고 해당 차량제원으로 최대의 가치를 끌어내고자 하는 종합적인 과정이다. 본 연구에서는 여러 원천의 데이터를 기반으로 자동차 디자인 과정에서 활용할 수 있도록 디자인에 필요한 부품/모듈 구성요소들에 대한 정보를 실시간으로 표시해주는 시스템을 고안하였다. 이를 적용하여 자동차 디자인 과정에서 예상 못한 외부 문제가 발생했을 때 선택할 구성 부품을 대체하거나 사전에 해당 부품을 이해하고 디자인에 활용할 수 있도록 세 가지 정보 제공 채널을 구성하였다. 첫 번째는 자동차 공장 내 실시간 데이터 집계를 Google Analytics를 활용하여 시각화하고, 이를 공장 자체의 자가 성장 캐릭터에 반영하여 디자이너에게 제공하는 방식이다. 이를 통해 종합상황실 등의 복잡한 인력 체계 없이도 생산 및 품질 현황 데이터를 실시간으로 확인 가능하도록 하였다. 두 번째는 차량용 주차보조 센서 카메라를 차량 부착 뿐만 아니라 인벤토리와 물류센터의 CCTV에도 적용하여 주변상황을 인식하고 분석할 수 있도록 구성하였다. 차량의 조립 생산 단계에서 부품 단위의 이동, 운송, 출하를 거쳐 완성차의 주행 단계에 이르기까지 데이터 흐름을 파악하는 것이 디자인 부문에 필요한 정보를 제공할 수 있는 방법으로 활용되었다. 이를 통해 기존 이해관계자들의 큰 반발 없이 내부의 카메라 기능으로부터 부품 리소스와 운송 상태를 실시간 파악 및 기록 가능하도록 하였다. 마지막으로 공공 API와 센서 기반의 사물인터넷을 활용해서 도로 위 차량 사고가 발생한 위치에서의 현장 수리를 위한 차량 부품 즉시 수급 및 데이터베이스화 방법도 개발 되었다. 이는 디자이너로 하여금 가벼운 접촉 사고에서의 부품 교체 행태에 대한 정보를 얻게 하여 차량의 디자인에 반영 가능하도록 하였다. 시나리오를 바탕으로 이 세 가지 정보 제공 채널을 활용할 경우, 자동차 디자인 과정에서 불러들여오는 부품 및 모듈의 구성 요소들을 디자이너가 정확히 알고 반영할 수 있다는 장점이 부각되었다. 정보 제공의 인터페이스를 쉽게 구성하기 위해서, 실제로 디자이너들이 자동차 개발 과정에서 디자인 프로세스 상에서 활용하는 CAD software에 세 가지 채널들로부터 들어오는 정보를 사례별 컬러로 표시하고, 이를 시선추적 사용성 평가를 통해 현업 디자이너들이 사용하기 쉽게 개선한 과정도 본 연구에 포함시켜 설명하였다.1 Introduction 1 1.1 Research Background 1 1.2 Objective and Scope 2 1.3 Environmental Changes 3 1.4 Research Method 3 1.4.1 Causal Inference with Graphical Model 3 1.4.2 Design Thinking Methodology with Co-Evolution 4 1.4.3 Required Resources 4 1.5 Research Flow 4 2 Data-driven Design 7 2.1 Big Data and Data Management 6 2.1.1 Artificial Intelligence and Data Economy 6 2.1.2 API (Application Programming Interface) 7 2.1.3 AI driven Data Management for Designer 7 2.2 Datatype from Automotive Industry 8 2.2.1 Data-driven Management in Automotive Industry 8 2.2.2 Automotive Parts Case Studies 8 2.2.3 Parameter for Generative Design 9 2.3 Examples of Data-driven Design 9 2.3.1 Responsive-reactive 9 2.3.2 Dynamic Document Design 9 2.3.3 Insignts from Data-driven Design 10 3 Benchmark of Data-driven Automotive Design 12 3.1 Method of Global Benchmarking 11 3.2 Automotive Design 11 3.2.1 HMI Design and UI/UX 11 3.2.2 Hardware Design 12 3.2.3 Software Design 12 3.2.4 Convergence Design Process Model 13 3.3 Component Design Management 14 4 Vehicle Specification Design in Mobility Industry 16 4.1 Definition of Vehicle Specification 16 4.2 Field Study 17 4.3 Hypothesis 18 5 Three Preliminary Practical Case Studies for Vehicle Specification to Datadriven 21 5.1 Production Level 31 5.1.1 Background and Input 31 5.1.2 Data Process from Inventory to Designer 41 5.1.3 Output to Designer 51 5.2 Delivery Level 61 5.2.1 Background and Input 61 5.2.2 Data Process from Inventory to Designer 71 5.2.3 Output to Designer 81 5.3 Consumer Level 91 5.3.1 Background and Input 91 5.3.2 Data Process from Inventory to Designer 101 5.3.3 Output to Designer 111 6 Two Applications for Vehicle Designer 86 6.1 Real-time Dashboard DB for Decision Making 123 6.1.1 Searchable Infographic as a Designer's Tool 123 6.1.2 Scope and Method 123 6.1.3 Implementation 123 6.1.4 Result 124 6.1.5 Evaluation 124 6.1.6 Summary 124 6.2 Application to CAD for vehicle designer 124 6.2.1 CAD as a Designer's Tool 124 6.2.2 Scope and Method 125 6.2.3 Implementation and the Display of the CAD Software 125 6.2.4 Result 125 6.2.5 Evaluation: Usability Test with Eyetracking 126 6.2.6 Summary 128 7 Conclusion 96 7.1 Summary of Case Studies and Application Release 129 7.2 Impact of the Research 130 7.3 Further Study 131Docto

AI Governance for Businesses

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there is limited work on AI governance targeted to companies and corporations. This work views AI products as systems, where key functionality is delivered by machine learning (ML) models leveraging (training) data. We derive a conceptual framework by synthesizing literature on AI and related fields such as ML. Our framework decomposes AI governance into governance of data, (ML) models and (AI) systems along four dimensions. It relates to existing IT and data governance frameworks and practices. It can be adopted by practitioners and academics alike. For practitioners the synthesis of mainly research papers, but also practitioner publications and publications of regulatory bodies provides a valuable starting point to implement AI governance, while for academics the paper highlights a number of areas of AI governance that deserve more attention

Studying the Executive Perception of Investment in Intelligent Systems and the Effect on Firm Performance

This research was conducted to examine the relationship between investment in intelligent systems resources and capabilities (based on artificial intelligence and machine learning algorithms) and the effect on company performance. Despite existing research on the benefits of adopting intelligent systems, companies have been slow to adopt as there is lack of research on intelligent systems use cases that will increase firm performance. This research study used resource-based view (RBV) and dynamic capabilities (DCF) theory to investigate firms’ investment in intelligent systems resources that build intelligent systems capabilities and the association to organization performance dimensions, revenue and profits. To answer this question, an online survey was administered and received responses from 165 participants from companies in Canada and USA. The study findings provide empirical evidence that intelligent systems infrastructure resources and intelligent systems IT human resources increase firm performance, but intelligent systems business resources constructs selected for the study do not contribute to firm performance

Dynamic Datasets and Market Environments for Financial Reinforcement Learning

The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets. Building high-quality market environments for training financial reinforcement learning (FinRL) agents is difficult due to major factors such as the low signal-to-noise ratio of financial data, survivorship bias of historical data, and model overfitting. In this paper, we present FinRL-Meta, a data-centric and openly accessible library that processes dynamic datasets from real-world markets into gym-style market environments and has been actively maintained by the AI4Finance community. First, following a DataOps paradigm, we provide hundreds of market environments through an automatic data curation pipeline. Second, we provide homegrown examples and reproduce popular research papers as stepping stones for users to design new trading strategies. We also deploy the library on cloud platforms so that users can visualize their own results and assess the relative performance via community-wise competitions. Third, we provide dozens of Jupyter/Python demos organized into a curriculum and a documentation website to serve the rapidly growing community. The open-source codes for the data curation pipeline are available at https://github.com/AI4Finance-Foundation/FinRL-MetaComment: 49 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:2211.0310