Towards Personalized Explanations for AI Systems: Designing a Role Model for Explainable AI in Auditing

Due to a continuously growing repertoire of available methods and applications, Artificial Intelligence (AI) is becoming an innovation driver for most industries. In the auditing domain, initial approaches of AI have already been discussed in scientific discourse, but practical application is still lagging behind. Caused by a highly regulated environment, the explainability of AI is of particular relevance. Using semi-structured expert interviews, we identified stakeholder specific requirements regarding explainable AI (XAI) in auditing. To address the needs of all involved stakeholders a theoretical role model for AI systems has been designed based on a systematic literature review. The role model has been instantiated and evaluated in the domain of financial statement auditing using focus groups of domain experts. The resulting model offers a foundation for the development of AI systems with personalized explanations and an optimized usage of existing XAI methods

An intuitionistic fuzzy based approach to resolve detected ambiguities in the user requirements document

Ambiguous user requirements are usually considered problematic in software engineering. Therefore, many studies have been conducted on its avoidance and detection. However, the detected ambiguities were resolved manually using interviews, brainstorming, and group discussion sessions among the elicitors and stakeholders for whom the software was developed. If not addressed efficiently, it gives rise to the explicit issues of additional time and cost involved and the stakeholders' availability to clarify them during multiple sessions. However, if appropriately addressed, it can reveal some implicit issues, such as tacit knowledge, hesitation, and terminological discrepancies. Identifying these implicit issues is not easy, as it requires expert elicitation skills that usually come with experience. In addition to the increasing demand for an automated approach to address these implicit issues, the recent COVID 19 pandemics has also amplified the demand to address the explicit issue of stakeholder availability. This paper proposes an implementable semi-automated approach to help elicitors address these demands. The proposed approach uses intuitionistic fuzzy logic to address hesitation and statistical functions to identify discordance and tacit knowledge. It also uses the heuristic knowledge gained in each iteration to improve itself. We implemented it in an online tool and conducted controlled experiments to evaluate our approach, and the results were compared. We achieved precision, recall, and F1 score of 0.769, 1, and 0.869, respectively, during our experiments. The results show that the proposed approach may minimize the explicit issues and help novice elicitors address the implicit issues discussed earlier

An Outcome-Based Approach for Ensuring Regulatory Compliance of Business Processes

In service industries, such as healthcare, catering, tourism, etc., there exist regulations that require organisations’ service comply with the regulations. More and more regulations in the service sector are, or are aimed to be, outcome-focused regulations. An outcome prescribed in the regulation is what users should experience or achieve when the regulated business processes are compliant. Service providers need to proactively ensure that the outcomes specified in the regulations have been achieved prior to conducting the relevant part of the business or prior to inspectors discovering noncompliance. Current approaches check system requirements or business processes, not outcomes, against regulations and thus this still leaves uncertain as to whether what the users actually experience are really achieved. In this thesis, we propose an approach for assessing the compliance of process outcomes and improve the noncompliance. The approach is designed through the U.K’s. CQC regulations in the care home environment

Design exploration: engaging a larger user population

Software designers must understand the domain, work practices, and user expectations before determining requirements or generating initial design mock-ups. Users and other stakeholders are a valuable source of information leading to that understanding. Much work has focused on design approaches that include users in the software development process. These approaches vary from surveys and questionnaires that garner responses from a population of potential users to participatory design processes where representative users are included in the design/development team. The Design Exploration approach retains the remote and asynchronous communication of surveys while making expression of feedback easier by providing users alternatives to textual communication for their suggestions and tacit understanding of the domain. To do this, visual and textual modes of expression are combined to facilitate communication from users to designers while allowing a broad user audience to contribute to software design. One challenge to such an approach is how software designers make use of the potentially overwhelming combination of text, graphics, and other content. The Design Exploration process provides users and other stakeholders the Design Exploration Builder, a construction kit where they create annotated partial designs. The Design Exploration Analyzer is an exploration tool that allows software designers to consolidate and explore partial designs. The Analyzer looks for patterns based on textual analysis of annotations and spatial analysis of graphical designs, to help identify interesting examples and patterns within the collection. Then software designers can use this tool to search and browse within the exploration set in order to better understand the task domain, user expectations and work practices. Evaluation of the tools has shown that users will often work to overcome expression constraints to convey information. Moreover, the mode of expression influences the types of information garnered. Furthermore, including more users results in greater coverage of the information gathered. These results provide evidence that Design Exploration is an approach that collects software and domain information from a large group of users that lies somewhere between questionnaires and face to face methods

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups