An investigation into a natural language interface for contact centers

Contact centres are the first point of contact between a company and a customer after the purchase of a product or service. These centres make use of contact centre agents to service customer queries. In the past contact centres hired as many agents as they could in order to service customers, which have led to an increase in personnel costs causing contact centres to become costly to run. Automation techniques were introduced to decrease personnel costs and one such technique is the Interactive Voice Response (IVR). The usability of IVR systems is, however, dismal. Customers would rather speak to a contact centre agent than navigate through the menu structure found in these systems. The menu structure has come under scrutiny because it is difficult to use and navigate, is often not aligned to caller usage patterns, and the menu options are long and vague. This research investigated whether a Natural Language Interface (NLI) could alleviate the problems inherent to IVR. NLIs, however, come with their own disadvantages of which the main ones are ambiguity and the loss of context of a conversation. Two prototypes were implemented, one of which resembled an IVR and the other an NLI (using ALICE concepts). An evaluation of two prototypes confirmed the advantages and disadvantages of these concepts in accordance to theory. A Hybrid prototype was proposed with the aid of two models. The model which proposed an NLI using a rule base was chosen for implementation. The Hybrid prototype was then evaluated against the NLI and IVR prototypes to deduce which prototype was the most effective, efficient and satisfying. The evaluation through the aid of descriptive and inferential statistics showed that the Hybrid prototype was the most usable prototype. The evaluation of the Hybrid prototype confirmed that a Hybrid approach could limit the shortcomings of IVR through the elimination of the menu structure found in these systems, thereby allowing users to state their queries in natural language. The incorporated rule base provided the Hybrid system with long term memory, eliminating one of the main disadvantages of NLIs

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

New Alterities and Emerging Cultures of Social Interaction

Globalization has generated increased societal heterogeneity and awakened interest of a new kind in social cohesion and integration. But globalization is not the only contemporary process to give rise to societal hybridization. Two other such processes - much less attended to in the theoretical debate but no less problematic as regards social integration - are societal ageing and robotization. Drawing on statistical estimates, this paper begins by assessing the relevance of these new processes of hybridization. The predictions in question indicate that in the near future, everyday interaction, not just with cultural strangers and 'intelligent' machines, but also with people suffering from dementia, will be an omnipresent phenomenon, confronting our societies with types and degrees of alterity never before encountered. Whereas contact with cultural strangers is to some extent familiar (though not yet taken as standard), interaction with intelligent technological devices and dementia sufferers represent new forms of alterity for which most societies have not yet established routines of conduct. This paper gives a detailed account of a number of empirical studies showing how new forms of hybrid interaction and cooperation evolve out of repeated contact with each of the three alterities. With this groundwork in place, the paper then attempts to identify not only the ways in which routines may develop out of interaction with the three alterities but also the trends towards, and prerequisites for, the emergence of a new culture of cooperation and interaction

Conversational affective social robots for ageing and dementia support

Socially assistive robots (SAR) hold significant potential to assist older adults and people with dementia in human engagement and clinical contexts by supporting mental health and independence at home. While SAR research has recently experienced prolific growth, long-term trust, clinical translation and patient benefit remain immature. Affective human-robot interactions are unresolved and the deployment of robots with conversational abilities is fundamental for robustness and humanrobot engagement. In this paper, we review the state of the art within the past two decades, design trends, and current applications of conversational affective SAR for ageing and dementia support. A horizon scanning of AI voice technology for healthcare, including ubiquitous smart speakers, is further introduced to address current gaps inhibiting home use. We discuss the role of user-centred approaches in the design of voice systems, including the capacity to handle communication breakdowns for effective use by target populations. We summarise the state of development in interactions using speech and natural language processing, which forms a baseline for longitudinal health monitoring and cognitive assessment. Drawing from this foundation, we identify open challenges and propose future directions to advance conversational affective social robots for: 1) user engagement, 2) deployment in real-world settings, and 3) clinical translation

PROTOTYPING RELATIONAL THINGS THAT TALK: A DISCURSIVE DESIGN STRATEGY FOR CONVERSATIONAL AI SYSTEMS

This practice-based research inquiry explores the implications of conversational Artificial Intelligence (AI) systems, ‘relational things that talk’, on the way people experience the world. It responds directly to the pervasive lack of ethical design frameworks for commercial AI systems, compounded by limited transparency, ubiquitous authority, embedded bias and the absence of diversity in the development process. The effect produced by relational things that talk upon the feelings, thoughts or intentions of the user is here defined as the ‘perlocutionary effect’ of conversational AI systems. This effect is constituted by these systems’ ‘relationality‘ and ‘persuasiveness’, propagated by the system’s embedded bias and ‘hybrid intentions’, relative to a user’s susceptibility. The proposition of the perlocutionary effect frames the central practice of this thesis and the contribution to new knowledge which manifests as four discursive prototypes developed through a participatory method. Each prototype demonstrates the factors that constitute and propagate the perlocutionary effect. These prototypes also function as instruments which actively engage participants in a counter-narrative as a form of activism. ‘This Is Where We Are’ (TIWWA), explores the persuasiveness and relationality of relational things powered through AI behavioural algorithms and directed by pools of user data. ‘Emoti-OS’, iterates the findings from TIWWA and analyses the construction of relationality through simulated affect, personality and collective (artificial) emotional intelligence. ‘Women Reclaiming AI’ (WRAI), demonstrates stereotyping and bias in commercial conversational AI developments. The last prototype, ‘The Infinite Guide’, synthesises and tests the findings from the three previous prototypes to substantiate the overall perlocutionary effect of conversational AI system. In so doing, this inquiry proposes the appropriation of relational things that talk as a discursive design strategy, extended with a participatory method, for new forms of cultural expression and social action, which activate people to demand more ethical AI systems

Modeling the user state for context-aware spoken interaction in ambient assisted living

Ambient Assisted Living (AAL) systems must provide adapted services easily accessible by a wide variety of users. This can only be possible if the communication between the user and the system is carried out through an interface that is simple, rapid, effective, and robust. Natural language interfaces such as dialog systems fulfill these requisites, as they are based on a spoken conversation that resembles human communication. In this paper, we enhance systems interacting in AAL domains by means of incorporating context-aware conversational agents that consider the external context of the interaction and predict the user's state. The user's state is built on the basis of their emotional state and intention, and it is recognized by means of a module conceived as an intermediate phase between natural language understanding and dialog management in the architecture of the conversational agent. This prediction, carried out for each user turn in the dialog, makes it possible to adapt the system dynamically to the user's needs. We have evaluated our proposal developing a context-aware system adapted to patients suffering from chronic pulmonary diseases, and provide a detailed discussion of the positive influence of our proposal in the success of the interaction, the information and services provided, as well as the perceived quality.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02- 02, CAM CONTEXTS (S2009/TIC-1485

“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts

An interdisciplinary concept for human-centered explainable artificial intelligence - Investigating the impact of explainable AI on end-users

Since the 1950s, Artificial Intelligence (AI) applications have captivated people. However, this fascination has always been accompanied by disillusionment about the limitations of this technology. Today, machine learning methods such as Deep Neural Networks (DNN) are successfully used in various tasks. However, these methods also have limitations: Their complexity makes their decisions no longer comprehensible to humans - they are black-boxes. The research branch of Explainable AI (XAI) has addressed this problem by investigating how to make AI decisions comprehensible. This desire is not new. In the 1970s, developers of intrinsic explainable AI approaches, so-called white-boxes (e.g., rule-based systems), were dealing with AI explanations. Nowadays, with the increased use of AI systems in all areas of life, the design of comprehensible systems has become increasingly important. Developing such systems is part of Human-Centred AI (HCAI) research, which integrates human needs and abilities in the design of AI interfaces. For this, an understanding is needed of how humans perceive XAI and how AI explanations influence the interaction between humans and AI. One of the open questions concerns the investigation of XAI for end-users, i.e., people who have no expertise in AI but interact with such systems or are impacted by the system's decisions. This dissertation investigates the impact of different levels of interactive XAI of white- and black-box AI systems on end-users perceptions. Based on an interdisciplinary concept presented in this work, it is examined how the content, type, and interface of explanations of DNN (black box) and rule-based systems (white box) are perceived by end-users. How XAI influences end-users mental models, trust, self-efficacy, cognitive workload, and emotional state regarding the AI system is the centre of the investigation. At the beginning of the dissertation, general concepts regarding AI, explanations, and psychological constructs of mental models, trust, self-efficacy, cognitive load, and emotions are introduced. Subsequently, related work regarding the design and investigation of XAI for users is presented. This serves as a basis for the concept of a Human-Centered Explainable AI (HC-XAI) presented in this dissertation, which combines an XAI design approach with user evaluations. The author pursues an interdisciplinary approach that integrates knowledge from the research areas of (X)AI, Human-Computer Interaction, and Psychology. Based on this interdisciplinary concept, a five-step approach is derived and applied to illustrative surveys and experiments in the empirical part of this dissertation. To illustrate the first two steps, a persona approach for HC-XAI is presented, and based on that, a template for designing personas is provided. To illustrate the usage of the template, three surveys are presented that ask end-users about their attitudes and expectations towards AI and XAI. The personas generated from the survey data indicate that end-users often lack knowledge of XAI and that their perception of it depends on demographic and personality-related characteristics. Steps three to five deal with the design of XAI for concrete applications. For this, different levels of interactive XAI are presented and investigated in experiments with end-users. For this purpose, two rule-based systems (i.e., white-box) and four systems based on DNN (i.e., black-box) are used. These are applied for three purposes: Cooperation & collaboration, education, and medical decision support. Six user studies were conducted for this purpose, which differed in the interactivity of the XAI system used. The results show that end-users trust and mental models of AI depend strongly on the context of use and the design of the explanation itself. For example, explanations that a virtual agent mediates are shown to promote trust. The content and type of explanations are also perceived differently by users. The studies also show that end-users in different application contexts of XAI feel the desire for interactive explanations. The dissertation concludes with a summary of the scientific contribution, points out limitations of the presented work, and gives an outlook on possible future research topics to integrate explanations into everyday AI systems and thus enable the comprehensible handling of AI for all people.Seit den 1950er Jahren haben Anwendungen der Künstlichen Intelligenz (KI) die Menschen in ihren Bann gezogen. Diese Faszination wurde jedoch stets von Ernüchterung über die Grenzen dieser Technologie begleitet. Heute werden Methoden des maschinellen Lernens wie Deep Neural Networks (DNN) erfolgreich für verschiedene Aufgaben eingesetzt. Doch auch diese Methoden haben ihre Grenzen: Durch ihre Komplexität sind ihre Entscheidungen für den Menschen nicht mehr nachvollziehbar - sie sind Black-Boxes. Der Forschungszweig der Erklärbaren KI (engl. XAI) hat sich diesem Problem angenommen und untersucht, wie man KI-Entscheidungen nachvollziehbar machen kann. Dieser Wunsch ist nicht neu. In den 1970er Jahren beschäftigten sich die Entwickler von intrinsisch erklärbaren KI-Ansätzen, so genannten White-Boxes (z. B. regelbasierte Systeme), mit KI-Erklärungen. Heutzutage, mit dem zunehmenden Einsatz von KI-Systemen in allen Lebensbereichen, wird die Gestaltung nachvollziehbarer Systeme immer wichtiger. Die Entwicklung solcher Systeme ist Teil der Menschzentrierten KI (engl. HCAI) Forschung, die menschliche Bedürfnisse und Fähigkeiten in die Gestaltung von KI-Schnittstellen integriert. Dafür ist ein Verständnis darüber erforderlich, wie Menschen XAI wahrnehmen und wie KI-Erklärungen die Interaktion zwischen Mensch und KI beeinflussen. Eine der offenen Fragen betrifft die Untersuchung von XAI für Endnutzer, d.h. Menschen, die keine Expertise in KI haben, aber mit solchen Systemen interagieren oder von deren Entscheidungen betroffen sind. In dieser Dissertation wird untersucht, wie sich verschiedene Stufen interaktiver XAI von White- und Black-Box-KI-Systemen auf die Wahrnehmung der Endnutzer auswirken. Basierend auf einem interdisziplinären Konzept, das in dieser Arbeit vorgestellt wird, wird untersucht, wie der Inhalt, die Art und die Schnittstelle von Erklärungen von DNN (Black-Box) und regelbasierten Systemen (White-Box) von Endnutzern wahrgenommen werden. Wie XAI die mentalen Modelle, das Vertrauen, die Selbstwirksamkeit, die kognitive Belastung und den emotionalen Zustand der Endnutzer in Bezug auf das KI-System beeinflusst, steht im Mittelpunkt der Untersuchung. Zu Beginn der Arbeit werden allgemeine Konzepte zu KI, Erklärungen und psychologische Konstrukte von mentalen Modellen, Vertrauen, Selbstwirksamkeit, kognitiver Belastung und Emotionen vorgestellt. Anschließend werden verwandte Arbeiten bezüglich dem Design und der Untersuchung von XAI für Nutzer präsentiert. Diese dienen als Grundlage für das in dieser Dissertation vorgestellte Konzept einer Menschzentrierten Erklärbaren KI (engl. HC-XAI), das einen XAI-Designansatz mit Nutzerevaluationen kombiniert. Die Autorin verfolgt einen interdisziplinären Ansatz, der Wissen aus den Forschungsbereichen (X)AI, Mensch-Computer-Interaktion und Psychologie integriert. Auf der Grundlage dieses interdisziplinären Konzepts wird ein fünfstufiger Ansatz abgeleitet und im empirischen Teil dieser Arbeit auf exemplarische Umfragen und Experimente und angewendet. Zur Veranschaulichung der ersten beiden Schritte wird ein Persona-Ansatz für HC-XAI vorgestellt und darauf aufbauend eine Vorlage für den Entwurf von Personas bereitgestellt. Um die Verwendung der Vorlage zu veranschaulichen, werden drei Umfragen präsentiert, in denen Endnutzer zu ihren Einstellungen und Erwartungen gegenüber KI und XAI befragt werden. Die aus den Umfragedaten generierten Personas zeigen, dass es den Endnutzern oft an Wissen über XAI mangelt und dass ihre Wahrnehmung dessen von demografischen und persönlichkeitsbezogenen Merkmalen abhängt. Die Schritte drei bis fünf befassen sich mit der Gestaltung von XAI für konkrete Anwendungen. Hierzu werden verschiedene Stufen interaktiver XAI vorgestellt und in Experimenten mit Endanwendern untersucht. Zu diesem Zweck werden zwei regelbasierte Systeme (White-Box) und vier auf DNN basierende Systeme (Black-Box) verwendet. Diese werden für drei Zwecke eingesetzt: Kooperation & Kollaboration, Bildung und medizinische Entscheidungsunterstützung. Hierzu wurden sechs Nutzerstudien durchgeführt, die sich in der Interaktivität des verwendeten XAI-Systems unterschieden. Die Ergebnisse zeigen, dass das Vertrauen und die mentalen Modelle der Endnutzer in KI stark vom Nutzungskontext und der Gestaltung der Erklärung selbst abhängen. Es hat sich beispielsweise gezeigt, dass Erklärungen, die von einem virtuellen Agenten vermittelt werden, das Vertrauen fördern. Auch der Inhalt und die Art der Erklärungen werden von den Nutzern unterschiedlich wahrgenommen. Die Studien zeigen zudem, dass Endnutzer in unterschiedlichen Anwendungskontexten von XAI den Wunsch nach interaktiven Erklärungen verspüren. Die Dissertation schließt mit einer Zusammenfassung des wissenschaftlichen Beitrags, weist auf Grenzen der vorgestellten Arbeit hin und gibt einen Ausblick auf mögliche zukünftige Forschungsthemen, um Erklärungen in alltägliche KI-Systeme zu integrieren und damit den verständlichen Umgang mit KI für alle Menschen zu ermöglichen