Personas for Artificial Intelligence (AI) an Open Source Toolbox

Holzinger, Andreas; Kargl, Michaela; Kipperer, Bettina; Regitnig, Peter; Plass, Markus; Müller, Heimo

doi:10.1109/access.2022.3154776

Cited by 44 publications

(10 citation statements)

References 80 publications

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“…However, with the accelerated application of AI solutions and their limitations ("black boxes"), new approaches need to be considered and developed to account for these requirements. Notable contributions such as Personas for AI [66] demonstrate practical solutions in the domain of mental models and AI and are expected to receive further attention in the future.…”

Section: Discussionmentioning

confidence: 99%

Visualizing Critical Objectives in Omnichannel Management Through Mental Models: The Application of an Assortment Integration Context

Cakir

Bezbradica

Helfert

2022

Communications in Computer and Information Science

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Channel Integration is a crucial task for retailers in order to generate a seamless customer experience. This is particularly the case for assortments. Customers expect an identical assortment along all channels or customer confusion can occur, potentially leading to purchase abandonment or postponement and thus to losses in sales. Mental models can help to simplify complex systems and behaviour and can support managers in decision-making processes. This paper builds on a mental model approach to visualize the dynamics and trade-offs of the three critical objectives in omnichannel assortment management: assortment integration, customer confusion reduction, and purchase postponement/abandonment reduction, to assist retailers in decision-making through the depiction of different scenarios and objective achievements. Recommendations for actions are provided for each scenario eventually helping retail managers in improving understanding and decision-making for omnichannel assortment decisions.

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Section: Discussionmentioning

confidence: 99%

Visualizing Critical Objectives in Omnichannel Management Through Mental Models: The Application of an Assortment Integration Context

Cakir

Bezbradica

Helfert

2022

Communications in Computer and Information Science

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“…In order to compromise on the vast demand for AI while providing users with the best possible experience, the design of AI technologies and interfaces is crucial. Holzinger et al (2022) [187] showed the continuous changes in users' profiles and requirements. The use of AI technologies illustrated that some users might want to try new technologies, whereas others might not.…”

Section: Human-centered Designmentioning

confidence: 99%

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

Holzinger

Saranti

Angerschmid

et al. 2022

Sensors

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The main impetus for the global efforts toward the current digital transformation in almost all areas of our daily lives is due to the great successes of artificial intelligence (AI), and in particular, the workhorse of AI, statistical machine learning (ML). The intelligent analysis, modeling, and management of agricultural and forest ecosystems, and of the use and protection of soils, already play important roles in securing our planet for future generations and will become irreplaceable in the future. Technical solutions must encompass the entire agricultural and forestry value chain. The process of digital transformation is supported by cyber-physical systems enabled by advances in ML, the availability of big data and increasing computing power. For certain tasks, algorithms today achieve performances that exceed human levels. The challenge is to use multimodal information fusion, i.e., to integrate data from different sources (sensor data, images, *omics), and explain to an expert why a certain result was achieved. However, ML models often react to even small changes, and disturbances can have dramatic effects on their results. Therefore, the use of AI in areas that matter to human life (agriculture, forestry, climate, health, etc.) has led to an increased need for trustworthy AI with two main components: explainability and robustness. One step toward making AI more robust is to leverage expert knowledge. For example, a farmer/forester in the loop can often bring in experience and conceptual understanding to the AI pipeline—no AI can do this. Consequently, human-centered AI (HCAI) is a combination of “artificial intelligence” and “natural intelligence” to empower, amplify, and augment human performance, rather than replace people. To achieve practical success of HCAI in agriculture and forestry, this article identifies three important frontier research areas: (1) intelligent information fusion; (2) robotics and embodied intelligence; and (3) augmentation, explanation, and verification for trusted decision support. This goal will also require an agile, human-centered design approach for three generations (G). G1: Enabling easily realizable applications through immediate deployment of existing technology. G2: Medium-term modification of existing technology. G3: Advanced adaptation and evolution beyond state-of-the-art.

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“…To manage such variations, it is essential to have a centralized and predictive command and control system that can manage large and complicated data sets and continually learn through experience by optimizing the algorithm used to make predictions. Moreover, the human–AI interfaces gains some degree of human-like cognitive, self-executing, and self-adaptive capabilities and autonomy, and generating unexpected outputs that require non-deterministic interactions [ 11 ]. A good example is UCHealth in Colorado, which is a health system that delivers a high-quality patient care as well as a high-quality patient experience.…”

Section: Research Study Motivationmentioning

confidence: 99%

“…The toolbox contains guidelines and material supporting persona development for AI as well as templates and pictures for persona visualization. It is ready to use and freely available to the international research and development community [ 11 ]. Cognitive walkthrough (CW) is an efficient method that allows obtaining feedback on an innovative service at early stages, by walking some stakeholders through the new desired experience and asking them to comment.…”

Section: Research Study Motivationmentioning

confidence: 99%

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Systematic review of co-design in digital health for COVID-19 research

Nusir

Rekik

2022

Univ Access Inf Soc

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Improving the quality of digital health care through information and communication technology can mainly contribute to the clinical, social, financial, and economic systems’ success, especially during the COVID-19 pandemic period. The co-design approach, which unleashes the end-user power, can contribute actively in improving the healthcare systems. It deals with understanding the user behaviors, requirements, and motivations through observation, inspection, task analysis, and feedback techniques. Consequently, both the co-design and digital technologies might empower the management of patients’ health and that of their families. The research strategy is based on a systematic literature review and meta-analysis to summarize how the co-design methodologies handled the existing technology-based health systems for their improvement. Based on the findings, we establish the following hypotheses: (i) A user-centered methodology for service implementation might offer a promising tool to enhance the healthcare services quality before they be launched; (ii) Several limitations can affect the co-design approach in digital health, such as a bias for a patients’ group. Efforts have been made to reduce this risk by identifying bias at an early stage, or different groups should be included in the test phase for example; (iii) Use decision-making devices that handle technologies for patient and clinical healthcare solution

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Personas for Artificial Intelligence (AI) an Open Source Toolbox

Abstract: Holzinger et al.: Personas for AI: An Open Source ToolboxThis article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

Cited by 44 publications

References 80 publications

Visualizing Critical Objectives in Omnichannel Management Through Mental Models: The Application of an Assortment Integration Context

Visualizing Critical Objectives in Omnichannel Management Through Mental Models: The Application of an Assortment Integration Context

Digital Transformation in Smart Farm and Forest Operations Needs Human-Centered AI: Challenges and Future Directions

Systematic review of co-design in digital health for COVID-19 research

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