ExSS 2018: Workshop on explainable smart systems

Smart systems that apply complex reasoning to make decisions and plan behavior are often difficult for users to understand. While research to make systems more explainable and therefore more intelligible and transparent is gaining pace, there are numerous issues and problems regarding these systems that demand further attention. The goal of this workshop is to bring academia and industry together to address these issues. The workshop includes a keynote, poster panels, and group activities, towards developing concrete approaches to handling challenges related to the design, development, and evaluation of explainable smart systems

Notions of explainability and evaluation approaches for explainable artificial intelligence

Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years. This is due to the widespread application of machine learning, particularly deep learning, that has led to the development of highly accurate models that lack explainability and interpretability. A plethora of methods to tackle this problem have been proposed, developed and tested, coupled with several studies attempting to define the concept of explainability and its evaluation. This systematic review contributes to the body of knowledge by clustering all the scientific studies via a hierarchical system that classifies theories and notions related to the concept of explainability and the evaluation approaches for XAI methods. The structure of this hierarchy builds on top of an exhaustive analysis of existing taxonomies and peer-reviewed scientific material. Findings suggest that scholars have identified numerous notions and requirements that an explanation should meet in order to be easily understandable by end-users and to provide actionable information that can inform decision making. They have also suggested various approaches to assess to what degree machine-generated explanations meet these demands. Overall, these approaches can be clustered into human-centred evaluations and evaluations with more objective metrics. However, despite the vast body of knowledge developed around the concept of explainability, there is not a general consensus among scholars on how an explanation should be defined, and how its validity and reliability assessed. Eventually, this review concludes by critically discussing these gaps and limitations, and it defines future research directions with explainability as the starting component of any artificial intelligent system

Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A2C1011198) , (Institute for Information & communications Technology Planning & Evaluation) (IITP) grant funded by the Korea government (MSIT) under the ICT Creative Consilience Program (IITP-2021-2020-0-01821) , and AI Platform to Fully Adapt and Reflect Privacy-Policy Changes (No. 2022-0-00688).Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI mode ľs decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related aspects. Therefore, in this comprehensive study, we provide readers with an overview of the current research and trends in this rapidly emerging area with a case study example. The study starts by explaining the background of XAI, common definitions, and summarizing recently proposed techniques in XAI for supervised machine learning. The review divides XAI techniques into four axes using a hierarchical categorization system: (i) data explainability, (ii) model explainability, (iii) post-hoc explainability, and (iv) assessment of explanations. We also introduce available evaluation metrics as well as open-source packages and datasets with future research directions. Then, the significance of explainability in terms of legal demands, user viewpoints, and application orientation is outlined, termed as XAI concerns. This paper advocates for tailoring explanation content to specific user types. An examination of XAI techniques and evaluation was conducted by looking at 410 critical articles, published between January 2016 and October 2022, in reputed journals and using a wide range of research databases as a source of information. The article is aimed at XAI researchers who are interested in making their AI models more trustworthy, as well as towards researchers from other disciplines who are looking for effective XAI methods to complete tasks with confidence while communicating meaning from data.National Research Foundation of Korea Ministry of Science, ICT & Future Planning, Republic of Korea Ministry of Science & ICT (MSIT), Republic of Korea 2021R1A2C1011198Institute for Information amp; communications Technology Planning amp; Evaluation) (IITP) - Korea government (MSIT) under the ICT Creative Consilience Program IITP-2021-2020-0-01821AI Platform to Fully Adapt and Reflect Privacy-Policy Changes2022-0-0068

Imaging of the Water Velocity Distribution in Water Continous Multiphase Flows Using Inductive Flow Tomography (IFT)

In the oil-gas fields, slurry flows, gas-in-water two phase flows, and oil-gas-water three phase flows are frequently encountered. Generally, the measurement of volumetric flow rate for each phase is of most interest, especially in subsea oil-gas production applications, where it is essential to obtain oil, water and gas flow rates in inclined oil wells. The problem of how to accurately measure these flow parameters for such complicated flow phenomena, without using expensive and large test separators, is a major challenge for the industry. Most conventional multiphase flow meters have severe limitations regarding types of flow and their measurement reliability. Some useful techniques containing radioactive sources are available but they are expensive and potentially harmful to humans. Thus, many academic and industrial researchers are working to develop a multiphase flow meter based on tomographic techniques that does not contain a radioactive source. Such a device would normally involve at least two independent flow metering techniques. Tomographic techniques have been successfully used in multiphase flows to determine the local volume fraction distributions of the various phases; however, only a very small number of results can be found in the published literature concerning the equally significant problem of local velocity distribution. Therefore, the aim of this research is to develop a non-intrusive flow measurement technique, without the use of radioactive sources, for measuring the local axial velocity distribution of the electrically conducting continuous phase in multiphase flows. This thesis reports the development of a multi-electrode electromagnetic flow metering technique, the so-called Inductive Flow Tomography (IFT), for obtaining the local flow velocity distributions of the electrically conducting continuous phase in multiphase flows, with particular relevance to gas-in-water two phase and oil-gas-water three phase flows. Previous research has indicated that the electromagnetic flow meter (i.e. Electromagnetic Velocity Profiler) is a promising technique for measuring the local axial water velocity in single phase and solid-in-water two phase flow. However, that technique has several limitations, which means it is valid only for determining water velocity profiles in seven regimes of the pipe crosssection. A novel multi-electrodes electromagnetic IFT flow metering system has been developed in the research described in this thesis, which is capable of determining the local conducting continuous phase velocity at any position in the flow cross-section (in vertical and inclined pipes). The theoretical work carried out in developing the IFT system includes a completely novel flow velocity distribution “image reconstruction algorithm”, which is described in the thesis. This thesis also describes the design and subsequent implementation of the hardware and software for the IFT system. In the final sections of this thesis, a series of experiments, which include inclined gas-oil-water three phase flow and gas-in-water two phase flow, were performed to investigate the performance of the IFT system. The experimental results obtained show a good agreement between the reference measurements and velocity measurements obtained using the IFT syste

Ammon in the Hebrew Bible: a Textual Analysis and Archaeological Context of Selected References to the Ammonites of Transjordan

The study of the Transjordanian Iron-Age (ca. 1200-550 BC) state of Ammon is important to students of the Bible because of the numerous references to the Ammonites (bene \u27ammon) included in the historical and prophetic sections of the Hebrew canon. The book of Genesis traces the ancestry of the Sons of Ammon to an eponymous ancestor named Ben Ammi--son/grandson of Abraham\u27s nephew Lot (Gen 39:17). Chapter 1 points out how Ammon--though often ignored or slighted in studies up to the mid-20th century--increasingly receives scholarly attention. It also shows a need for applying the results of archaeological research to facilitate a fuller understanding of the biblical text. Chapter 2 outlines recent trends in the relationship between the fields of biblical studies and archaeology. Criteria are set forth for evaluating published works combining emphases on the fields of biblical studies and archaeology, especially as they relate to the study of the Ammonites. The term archaeological context is examined and differentiated from archaeological commentary. Chapter 3 tabulates all references to the Ammonites in the Hebrew Bible and compares key references to those in the LXX. A study of the familial relationships within the courts of David and Solomon suggests interesting possibilities for identifying a number of interrelationships which existed between the royal houses of Ammon and Israel. Many Ammonite references cluster around two important themes--tribal/kindred loyalty and honor for Yahweh\u27s temple (or a lack thereof). Chapter 4 gives a topographical and archaeological background for selected Ammonite references. Ammon\u27s heartland (near modern Amman) was centered around the head waters of the Jabbok River (Nahal Zarqa), strategically located along important trade corridors--the north-south King\u27s Highway and the east-west routes to Jerusalem and to the Canaanite coast. Districts of Ammonite control are identified, and an archaeological summary is given for each biblical site with Ammonite connections and for individuals identified as being Ammonites. Occupations of Ammonite people, the status of women in Ammonite society, and interrelations between Ammon and other contemporary states are explored. The comparative richness of Ammon\u27s cultural heritage and its rise to relative prosperity as a vassal state are chronicled. Evidence of Ammonite cult and religion--including the existence Ammonite deities Milkom and Astarte--is depicted on seals and figurines, and in the Amman Citadel Inscription which included Milkom\u27s divine oracle to be displayed publicly on the acropolis. Ammon\u27s inclusion in the Hebrew prophetic oracles is briefly mentioned. Chapter 5 summarizes the interrelationship between biblical references to Ammon and the results of archaeological research. The archaeological evidence is shown to be consistent with the biblical portrayal of Ammon in the Hebrew Bible. However, additional in-depth study of the importance of Ammon in Hebrew prophetic literature is recommended

A Multidisciplinary Design and Evaluation Framework for Explainable AI Systems

Nowadays, algorithms analyze user data and affect the decision-making process for millions of people on matters like employment, insurance and loan rates, and even criminal justice. However, these algorithms that serve critical roles in many industries have their own biases that can result in discrimination and unfair decision-making. Explainable Artificial Intelligence (XAI) systems can be a solution to predictable and accountable AI by explaining AI decision-making processes for end users and therefore increase user awareness and prevent bias and discrimination. The broad spectrum of research on XAI, including designing interpretable models, explainable user interfaces, and human-subject studies of XAI systems are sought in different disciplines such as machine learning, human-computer interactions (HCI), and visual analytics. The mismatch in objectives for the scholars to define, design, and evaluate the concept of XAI may slow down the overall advances of end-to-end XAI systems. My research aims to converge knowledge behind design and evaluation of XAI systems between multiple disciplines to further support key benefits of algorithmic transparency and interpretability. To this end, I propose a comprehensive design and evaluation framework for XAI systems with step-by-step guidelines to pair different design goals with their evaluation methods for iterative system design cycles in multidisciplinary teams. This dissertation presents a comprehensive XAI design and evaluation framework to provide guidance for different design goals and evaluation approaches in XAI systems. After a thorough review of XAI research in the fields of machine learning, visualization, and HCI, I present a categorization of XAI design goals and evaluation methods and show a mapping between design goals for different XAI user groups and their evaluation methods. From my findings, I present a design and evaluation framework for XAI systems (Objective 1) to address the relation between different system design needs. The framework provides recommendations for different goals and ready-to-use tables of evaluation methods for XAI systems. The importance of this framework is in providing guidance for researchers on different aspects of XAI system design in multidisciplinary team efforts. Then, I demonstrate and validate the proposed framework (Objective 2) through one end-to-end XAI system case study and two examples by analysis of previous XAI systems in terms of our framework. I present two contributions to my XAI design and evaluation framework to improve evaluation methods for XAI system

A study of case work at the United Prison Association of Massachusetts

Thesis (M.S.)--Boston University, 1944. This item was digitized by the Internet Archive