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Marisa Tschopp is a researcher at scip AG , associate researcher at IWM, Women in AI ambassador Switzerland. She is conducting research about AI and technology from a psychological perspective, with a wide range of questions related to psychological phenomena, governance and ethical implications. Co-Chairing the Trust and Agency in AIS Committee , subgroup of The IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (AIS).
The Titanium Trust Report is a mixed methods analysis with 111 participants from heterogeneous backgrounds and expertise to investigate trust and artificial intelligence. The qualitative section explores fear and skepticism towards artificial intelligence. 14 sub-categories were identified, like fear of robots or AI in healthcare. AI in warfare, lo...
Künstliche Intelligenz (KI) beeinflusst die Art und Weise, wie Organisationen Arbeit und Zusammenarbeit strukturieren. Unklar ist jedoch, welche Rolle die Führung bei der Gestaltung der digitalen Transformation übernehmen muss. In diesem Beitrag argumentieren wir für einen breit angelegten und transdisziplinären Dialog als Mittel der Wahl, um KI un...
No trust, no use? Oft wird Vertrauen als kritischer Erfolgsfaktor propagiert, wenn es um die Nutzung von neuen Technologien geht, vor allem wenn es sich um intelligente Systeme, sogenannte KI (Künstliche Intelligenz) handelt. Diese finden nämlich immer mehr Eingang in die heutige Gesellschaft, sowohl im privaten (z. B. Einkaufen mit Amazons KI-basi...
Trustworthy" artificial intelligence (AI) is a globally discussed topic. Trust, distinct but related to trustworthiness, is considered a critical success factor in human-AI-interactions. However, the role of the trusted "partner' remains unclear: Is an AI system or the designers of AI the trusted peer in this relation, and does it even matter? Sinc...
Background: Many cases of how AI-based technologies maintain and reproduce inequality caught much attention in research and practice lately. The justified focus on the dark side of AI may cause reluctance towards exploring how AI-based technologies can be applied to increase gender equality. This report aims to address the lack of systematic knowle...
There are different ways to develop voice user interfaces. People without programming skills can use Voiceflow to develop their first voice project. Voiceflow is suitable for simple and more complicated voice projects. It can be used for Alexa Skills and Google Actions.
Voice user interfaces are becoming increasingly popular. In addition to technical and functional aspects, brand personas play a role in the design process. A brand persona is a conceptual representation of an immaterial voice application and influences acceptance and user behavior. Embedded in the overall design process, the examination of brand pe...
Trust is often promoted as a success factor for new technologies. There are fundamentally wrong questions that try to implement a "Trust Washing". These include "Does the user trust our AI", "How can we increase trust" and "Should society trust AI". These questions must be asked differently to achieve the true goal.
Automation aids are increasingly used to increase safety or productivity. Faulty human-automation relationships have led to over-or under reliance on automation aids and thus to fatal consequences. Researchers propose the Perfect Automation Schema (PAS) as an individual difference variable influencing trust in and reliance on automation. PAS is def...
Due to the smoldering COVID-19 pandemic more and more meetings, trainings, and lectures are taking place online. Among others, Zoom has become a very popular solution. Attackers are increasingly targeting it. Various measures can be taken to prevent meetings from being compromised or taken over.
This report offers a theoretical background on trust, outcomes of a workshop on trust and personal reflections of the author. Confusion about terminologies (trust, reliance, trustworthiness) is prevailing and impedes progress. Trustworthiness as a property is distinct from but related to trust as an attitude. Interpersonal trust can be partially tr...
This is a brief review of artificial intelligence and leadership. it serves as a preparatory text for the Applied Machine Learning Days at EPFL in Lausanne (Switzerland, Jan 2020)
This research project aims to understand, measure, compare, and track changes in the capabilities of conversational AI. It integrates seven categories: Explicit Knowledge, Language Aptitude, Numerical and Verbal Thinking, Working Memory, Critical and Creative Thinking. The evaluation takes place through a multi-level system of response categories a...
Slides in English. Presentation at the IKM Update at HSLU (Luzern, Switzerland) Voice in der Customer Journey: Neue Chancen für das Marketing Inputreferat 3: Können wir Künstlicher Intelligenz vertrauen? Marisa Tschopp, Researcher scip AG, Ambassador Women in AI Switzerland
Lethal autonomous weapons systems (LAWS) are also called killer robots. LAWS should be able to detect, select and destroy a target without any human oversight (inherent AI). There are strong opinions for and against LAWS, no binding regulations exist so far. With greater technology, comes greater individual responsibility for each and every person.
AI is a dual-use technology that can be used for both civilian and military purposes or alternatively with good or bad intentions. Tech giants are in a precarious position where their technology is being used more and more for military purposes, against the values of users and employees. Responsibility grows on all sides, behavioral alternatives be...
The track AI & Leadership aims to discuss implications of artificial intelligence on organizational behavior, with a specific focus on leadership. How does AI influence leadership, human interactions and social relationships at the workplace? Session/ Goal: Goal of this session is twofold: First we aim to set a theoretical fundament to understand...
AI causes motivational conflicts in many people. Attitude to AI influences consumer and user behavior. Persuasion and compliance can influence attitudes and behavior. Reciprocity, obligation, scarcity, and modeling are crucial influencing techniques. Resistance to AI is mainly due to irritation and reactance.
KEYPOINTS Cognition comprises mental process of the human brain Artificial Intelligence tries to mimic these processes to solve complex problems and handle massive amounts of data Serial vs. parallel and controlled vs. automated processes are the basis of cognitive sciences Logic, probability theory and heuristics represent the pillars of theory fo...
Awake consciousness comprises the complexities of perception, attention and functions of information processing. Artificial intelligence integrates machine consciousness research regarding existence and framework development. Processes, functions, benefits as well as ethical and legal implications are in the center of scientific discourse. Demystif...
The Interdisciplinary Artificial Intelligence Quotient Scale (iA-IQS) is designed for testing AI solutions. A standardized procedure is used to test and measure performance in seven different categories. We have developed a sophisticated framework to automate this analysis. It can be used to test products extensively and efficiently to assess their...
Trust as one critical success factor for AI adoption. Hype, complexity, and monopolism prevent trust in AI. A systemic approach investigates the concepts of trust from various perspectives: person, process, environment and time. Competence and reliability as most important trust factors. Development of the A-IQ to foster trust in AI. The A-IQ is an...
2019 will be the year of (AI) enlightenment, which will produce fact-based statements and clearer definitional demarcations. The focus will be on ethics, privacy, security, open science, interdisciplinarity, internationality and narrowly defined, practical applications. The role of privacy and security, and their demarcation criteria in the context...
Humanity is more than ever confronted with Artificial Intelligence (AI), yet it is still challenging to find common ground. By adopting the term intelligence, AI has inherited a myriad of issues from the history of psychological intelligence research. Our study aims to understand, measure, compare and track changes of AI capabilities. From that, an...
Massive rise of AI research and other channels. AI research inevitably requires an interdisciplinary approach. Psychology and AI are inseparable. AI strikes humanity where it hurts: vulnerability, replacement. Further series will include: brain, conscience, behavior, ethics, practice.
We are examining the role of trust in Artificial Intelligence.
I would like to compile various scales to measure trust in different contexts (not only Human - Machine context) - I would be very happy for any recommendations or experiences with the scales!
I have started gathering these: _Trust Scales_
* CTF Culture Technology Fit ( Lee et al, 2007)
* Empirically Derived ED, (Jian et al 2000)
* HCT HUman Computer Trust (Madsen et al 2000)
* ICTA Internatina Comparisaon of Technology Adoption (Im et al 2012)
* OTB Online Trust Belief (Giollau et al 2003)
* TAS Technology Addoptiveness Scale (Halüert et al, 2008)
* TIST Trust in specific Technology (MCknight et al 2011)
* Interpersonal Trust Questionnaire (ITQ; Forbes & Roger, 1999)
* Human-Robot Trust Scale (HRTS) (Schaefer, 2013)
* Trust in Automation Scale (TAS) (Jian, Bisantz, & Drury, 2000)
* Negative Attitudes towards Robots Scale (NARS; Nomura, et. al 2006)
* Development of a Human-Robot-Interaction Trust Scale (Yagoda, Gillan 2012)
* SDCAS Self driving Car Acceptance Scale (Nees 2016)
*Human- Computer Trust Rating (Kauppinen et al. 2002)
*SHAPE Automation Trust Index (cited in Langan-Fox 2009)
*(Validated) Interpersonal Trust scale (Rotter 1971)
* TAM- Technology Acceptance Model (Davis, 1989)
* Orgnizational Trust Scale (Santosh Dhar 2015)
* Development and validation of a brand trust scale (E Delgado-Ballester 2003)
* Development and Validation of a Propensity to Trust Scale (M. Lance Frazier,Paul Johnson,Stav Fainshmidt)
This project compiles readings related to very basic and profound concepts and theories of psychology as a science integrated into the AI environment. Relevant psychological constructs will be explained and how they connect to AI. For example, what is human perception and attention: reality, ambiguity and deception and how does AI resemble these processes? How much perception bias is in AI? What are the implications? For example, if you use an AI system in a recruiting of personnel process (Human Resources), how can you make sure that applicants of specific races or sex are not discriminated against? The topics have a wide range, yet they are chosen based on relevance and use in day-to-day practice. Goal is to create general understanding, find a common ground and therefore reduce complexity. Key questions and answers about the understanding, measurement and comparison of human and AI skills will be the focus of this series, which incorporates both, invisible processes (so called black box of human brain) as well as visible behaviors.
Applied Machine Learning Days 2020 // EPFL Lausanne The track AI & Leadership aims to discuss implications of artificial intelligence on organizational behavior, with a specific focus on leadership. How does AI influence leadership, human interactions and social relationships at the workplace? Artificial Intelligence is already part of our lives and will continue its impact on management and the workplace. Yet currently, research and practice have been more focused on the question how to make AI applicable for organizational solutions and to raise user trust in AI. Relatively little is known, however, how AI will in turn change human interactions for instance leadership processes or organizational cultural aspects. Hence in our session we ask how various forms of AI/ML incl. robotics, whether it is performance control mechanisms in HR or robots in healthcare, change our everyday social communication and behavior patterns. Possible questions addressed are: How will human behavior and interaction at the workplace change in the AI era? How can AI make leaders more empathetic and/or more efficient? But also could AI have a negative impact on leader behavior and attitudes, for instance by reducing leaders attention span? Are we underestimating the critical thinking skills needed to supervise an AI and how would these have to look like? Will AI help us to be more humane at the workplace or could AI make organization culture and team coherence potentially worse by leading to a higher work intensification? How will AI/ML, including automation, influence empathy, trust, and human relationships at the workplace? Can we reinvent leadership or in the worst case, lose the human touch?