# An Innovative Method to Study the Social Impact of AI on the Work Environment Based on a Multi-dimensional Human-Centred Analysis of the Worker-AI Team

Simona D'attanasio, Sara De Martino, Yann Ferguson

► To cite this version:

Simona D'attanasio, Sara De Martino, Yann Ferguson. An Innovative Method to Study the Social Impact of AI on the Work Environment Based on a Multi-dimensional Human-Centred Analysis of the Worker-AI Team. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.45-56, 10.1007/978-3-031-34107-6_4. hal-05498089

HAL Id: hal-05498089

https://inria.hal.science/hal-05498089v1

Submitted on 6 Feb 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
<figure><img src="image_3.png" /></figure>

# An Innovative Method to Study the Social Impact of AI on the Work Environment Based on a Multi-dimensional Human-Centred Analysis of the Worker-AI Team

Simona D'Attanasio✉, Sara De Martino, and Yann Ferguson ID

Icam, Toulouse, France

simona.dattanasio@icam.fr

**Abstract.** This paper presents an innovative method to analyse the impact of artificial intelligence (AI) technologies in the work environment. These technologies are becoming more and more widespread in workplaces, integrating the cognitive processes of workflows, provoking an important disruption in the worker environment. At present, the state of the art lacks tools and methods to understand what we call the worker-AI team. The method presented in the paper is based on a four-dimensional model considering the social, the strategic, the structural, the core framework dimensions that contribute to determine the dynamics of this team within an organisation. Each dimension is related to one or more involved stakeholders, providers, workers, managers, policy-makers and social partners. An experimental protocol has been derived from the model and deployed to retrieve data from use-cases in different fields of activity and for different specific tasks. The analysis of the initial set of data allowed the identification of a preliminary taxonomy that is presented in the conclusions. The eight worker profiles that have been identified can help to provide a human-centred integration of AI technologies in the workplace.

**Keywords:** Social impact of AI · taxonomy · worker-AI team

## 1 Introduction

It is widely recognised that artificial intelligence (AI) technologies can provide economic and societal benefits for organisations<fnref n="1" />. The condition for a successful integration of such technologies in the workplace is that all stakeholders in the AI environment - AI providers, AI users, managers integrating AI, policy-makers and social partners - provide a safe framework for all workers, from the technological, strategical, social and ethical points of view, based on a human-centred approach [1]. The “shape” of this framework is not well defined yet. Models and clear guidelines need to be established to lead the

<footnote n="1">¹ With the term “organisation” we refer here to public and private organisations, including companies and small and medium enterprises.</footnote>
development and the deployment of AI. Shneiderman [2] proposes a two-dimensional framework of HCAI Human-Centred Artificial Intelligence, with the aim of producing new and more complete guidelines for the design of computer applications which are Reliable, Safe and Trustworthy, RST [3]. To achieve this goal, Shneiderman brilliantly discusses the need of “technical practices that support reliability”, “management strategies that create cultures of safety”, and “independent oversight structures that support trust”. This approach suggests a three-party contribution leading to RST AI applications: product providers that implement “good” technical practices, managers that implement strategies and structures that support trust. The AI final user, the worker at the workplace, is part of the technical practices through audit trails, but doesn’t constitute a party itself. The European Union [4, 5] on the other hand clearly states that human agency and oversight are some of the main key requirements for a trustworthy AI: “the overall wellbeing of the user should be central to the system’s functionality”. The report points out the need for transparency, i.e. the “explainability of the algorithmic decision-making process, adapted to the persons involved, should be provided to the extent possible”. In addition, “it is important to adequately communicate the AI system’s capabilities and limitations to the different stakeholders involved in a manner appropriate to the use case at hand”. In the most part of the literature the impact of AI on work has been analysed and measured just in terms of job loss and risk of automation. In this direction goes The impact of Artificial Intelligence on Work report from Frontier Economics [6] that focuses on the forecast of the labour market (job suppression and creation). McKinsey’s analysis targets more than 400 use-cases to identify the applications that have the greatest potential to create economic value [7]. Hence the recent public debate has tried to outline the future of the work and the evolution of work and working relations in the light of the digitalization waves [8, 9]. Notwithstanding, the way in which the working relations and the work itself change under the influence of the introduction of AI solutions, is still not deeply explored. For these reasons, the paper aims at understanding the impact of AI technologies on working environment in the following way:

- By identifying the main features that have a role in the dynamics of the worker-AI team and worker-worker interactions.

- By taking into consideration the interactions with the whole constellation of stakeholders and social-territorial partners.

The paper is organised as follows. The next section defines the context of the research and the use-case selection presented in the paper. The multi-dimensional model section provides a description of the model that we implemented to describe the worker-AI team dynamics. The research methodology and use-cases selection section details the use-case list and the experimental protocol deployed. The analysis of the results presents the taxonomy derived from the verbatim of the interviews. The conclusion and future work section will conclude the paper illustrating the next steps of the research.
## 2 The Research Context

The starting hypothesis of this research is that the building of the worker-AI team is not only finalised to perform a working task more efficiently and better, as stated by the majority of AI designers, but also to enhance the workers' expertise. Since the human counterpart is significantly impacted by the introduction of AI solutions in the workplace, it is important to focus the analysis on the workers' experience evolution at several levels. More specifically we propose to analyse the diverse features emerging from different working contexts in order to identify common patterns that could allow the definition of a generalised framework of worker-AI team typologies at work. The ultimate related objective is to use the different observed working experiences and workers' perspectives to provide guidelines for future design of more human-centred AI systems. To define the context of our research, we refer to the definition given by the European Union [10]: "Artificial intelligent systems are software (and possibly also hardware) systems designed by humans that, given a complex goal, act in the physical or digital dimension by perceiving their environment through data acquisition, interpreting the collected structured or unstructured data [...] to achieve the given goal". This definition explicitly mentions the possible double nature of AI systems, hardware and software, thus including robots. The use-cases that we consider do not include fully automated systems, like robot manipulators acting in a production line, as we focus on worker-AI teams, showing interaction and/or synergy between them. More in particular, we consider AI systems that are integrated in a workplace and play a role in the workflow and/or in the task planning, execution or decision strategy of the worker. In other words, we consider AI-systems that change the "ordinary way of doing a job".

As the main objective is to focus on the human partner of the team, the research is conducted by an interdisciplinary team, including researchers in human sciences, such as sociology, psychology, ergonomics, design, semiotics, political science, and in technical sciences, such as robotics and computer science. The research mainly focuses on social impacts and does not consider other important aspects of AI, such as explicability and cybersecurity. Since the research is financed by the Occitanie region in France in the framework of the ICENTRA project<fnref n="2" />, we only focused on use-cases in this area. The limit of the study lies within the mono-cultural representativity of our experimental data, as we believe that observed behaviours are typical of occidental democratic countries. Nevertheless, we believe that the study provides architecture and analysis that can be generally applied.

## 3 The Multi-dimensional Model

The goal of our research is to identify the main features of the worker-AI team that impact human work mainly under the worker perspective. Economic benefits are in most of the cases well defined and easily accessible. Intelligent automation can in fact provide metrics to evaluate their performance: for example, increase in quantity (faster,

<footnote n="2">² ICENTRA project. "Intelligence artificielle CENtrée sur le TRAvail. Work-centered artificial intelligence." Funded in the framework of the Research and Society call of the Occitanie region (France). Toulouse and Nîmes (France), 2022–2023.</footnote>
more output in less time) and quality (better, identification of poor results). On the other hand, it is difficult to evaluate social impacts, positive or negative, of the introduction of the AI system in the work environment. Workers' well-being and thus engagement and motivation are considered essential to the health and productivity of an organisation and strongly contribute to a virtuous circle [11]. When talking about well-being, motivation, engagement, indicators become qualitative, hardly measurable, application dependent and strongly correlated among them. Ferguson et al. propose a tool to measure the impact of AI systems on well-being at work [12]. The tool is declined in 3 criteria: acknowledgement, social relationships, and monitoring and control. A questionnaire allows quantitative evaluation in the workplace of the risk of undermining acknowledgement, relational disengagement, and monitoring feeling. This research was highly inspiring for the social dimension of our model. We also took inspiration from the key high-level dimensions of the Organisation for Economic Co-operation and Development, OECD, Framework [13]. The dimensions and their interaction are depicted in Fig. 1.

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** The OECD dimensions for classification of AI systems.</figcaption></figure>

The dimension People & Planet at the centre of the picture clearly (graphically) defines the human-centred approach of the framework. This dimension considers among other criteria the impacted stakeholders. The Economic Context refers to the economic environment and sector in which the AI system is implemented. Data & Input refers to the data/input used in the AI system. The AI model is the computational representation of the system. Task & Output refers to system tasks and evaluation methods. To build our model, we extracted several criteria adapting them to our purpose. Our final model is depicted in Fig. 2 and consists of four dimensions, each with well-defined features. The human-machine (worker/AI system) team is at the centre. The dimensions are described in the following paragraphs.
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The four-dimensional architecture of our research.</figcaption></figure>

## 3.1 System Dimension

The system dimension contributes to describe the main characteristics and attributes of the technological solution, in other words what the AI system has been designed for and how it works. It offers the solution provider point of view. We also call it “value offer”, because the provider of an AI system has some goals and problems to solve in mind for the designed solution. The system dimension includes five features. Application concerns the field of the AI system, e.g. financial, human resources, maintenance or medical among others<fnref n="3" />. It also concerns the specific purpose of the application and the particular task in which the AI system is involved. For example, an AI system in the transportation field, can be used in maintenance to organise maintenance operations (AI purpose), generating operator sequence of interventions (task). Interaction modes define the modality of the human-machine interface: visual, vocal, tactile or other. Data helps to understand some non-technical issues about the data used by the AI system [13]: the way of collecting them, manually or autonomously; their origin, who or what inputs them; their nature with respect to time, real-time or static. Interaction dynamics defines who does what, when and how, enhancing the interaction point of view. The stakeholder architecture describes the interaction among stakeholders in the workflow. The interaction flow allows the description of the flow of information in terms of sequence of interactions (worker-AI, worker-worker, AI-AI). Task evolution provides a comparison of flows before and after the introduction of the AI system.

<footnote n="3">³ A full list of fields of application is given by the International Standard Industrial Classification of All Economic Activities Autonomy https://unstats.un.org/unsd/publication/seriesm/seriesm_4rev4e.pdf</footnote>
## 3.2 Social and Human Dimension

The social and human dimension offers the worker's point of view of the worker-machine team. We also call it a "value system", because humans have values that motivate them and give meaning to their actions and choices in the workplace. The social and human dimension includes three features, mainly inspired by [12]. Well-being is a general concept concerning how workers feel at work. For our research we focalise on three main aspects: acknowledgment from the hierarchy and from colleagues from the competence point of view, relationship and exchanges with colleagues, control that can be exerted by the management. Engagement is the strength of the mental and emotional connection employees feel toward the work they do. This feature is strongly connected to motivation and its core components, goal choice, goal striving and self-belief in goal attainment [11]. Therefore, contributions to engagement at work are: the level of autonomy that workers have in their job, the responsibility that they are given from the hierarchy and the know-how that they need to mobilise in that job. Work value shift expresses the new added (or lost) value derived by the integration of the AI system: the mental load describes the change in the cognitive effort involved in managing the task; the competence evolution is an indicator on the impact on the skills in terms of upskilling, deskilling – increasing or decreasing skill level - or reskilling – learning new skills.

## 3.3 Strategic Dimension

The strategic dimension offers the opportunity to understand the framework in which the managers decided to introduce a specific AI system. More specifically the strategic dimension allows, on one hand, to describe the reasons and the modalities through which the integration process of the AI technology happened within the organisation, and on the other, to better understand the working relational system. The strategic dimension includes three features. Benefit aims to identify the reason for the integration. The AI system may allow improved efficiency, in terms of time – doing a task faster – and/or in terms of resources – doing a task with less resources. The AI system may also provide a better quality or allow the development of new products and/or services, thus improving diversification of the organisation. Governance describes the modality through which an organisation decides to implement the IA solution and explains the mechanisms needed to balance the knowledge and the competencies required among the different actors. More in depth the governance focuses on the integration process of the AI system explaining if it has been vertically imposed (top-down) or horizontally discussed with and proposed by the workers (bottom-up). This allows the understanding of the degree of the workers' involvement in the decisions concerning the introduction of a "new (AI-based) working tool" within the organisation and consequently the degree of appropriation of it. Synergies aim at measuring the involvement of territorial and social actors and other stakeholders within the decisional process that has led to the introduction in a specific organisation. This feature helps in describing the extension of the decisional process and it strengthens the diverse governance approach of the managers (decision-makers).
## 3.4 Core Framework

The core framework includes the overall normative, regulatory and ethical context in which are embedded the cases-studies. More specifically it offers the opportunity to evaluate the integration of an AI solution within an organisation, in the light of the evolution of national and supranational strategies [14, 15]. This dimension helps to understand to what extent the decisions taken by the different organisations and stakeholders are in line with the current regulatory framework and if these respect the officially defined main principles and values of AI. The core framework is still under development and for this reason it has been really complicated to analyse this dimension in our case-studies. There is still a lack of a proper awareness concerning all the risks and opportunities deriving from the integration of AI at the workplace.

# 4 The Research Methodology and Use-Cases Selection

Starting from the abovementioned model we have constructed a semi-directive questionnaire whose objective is to investigate the four dimensions. A second related step has been to categorise the stakeholders to be interviewed: the designer/provider of the solution, the managers and the users (workers).

<table><caption>**Table 1.** The table shows nine use-cases, grouped according to the field of application. For each field, we interviewed different stakeholders belonging to diverse hierarchical levels. The code is the following M = manager, SW = senior worker, W = worker, P = provider. The People column indicated the number of people interviewed.</caption><thead><tr><th>Field</th><th>Stakeholders</th><th>Purpose</th><th>People</th><th>Type of organisation</th></tr></thead><tbody><tr><td rowspan="2">Transportation</td><td>M, SW, W</td><td>Maintenance</td><td>3</td><td>Public</td></tr><tr><td>W</td><td>Design</td><td>1</td><td>Private</td></tr><tr><td>Banking</td><td>SW, W, P</td><td>Advisor support</td><td>5</td><td>Private</td></tr><tr><td>Public Administration</td><td>M, W</td><td>Advisor support</td><td>2</td><td>Public</td></tr><tr><td>Spatial</td><td>SW, P</td><td>Recruitment</td><td>2</td><td>Public</td></tr><tr><td>Marketing</td><td>M</td><td>Design</td><td>1</td><td>Private</td></tr><tr><td>Manufacturing</td><td>W</td><td>Maintenance</td><td>1</td><td>Private</td></tr><tr><td rowspan="2">Justice</td><td>SW, P</td><td>Support</td><td>2</td><td>Private</td></tr><tr><td>P</td><td></td><td>1</td><td>Public</td></tr></tbody></table>

This allows us to adapt the questionnaire in function of the different stakeholders involved in the research. In total we have done 18 qualitative in-depth interviews conducted at least by 3 experts, 2 in the field of social sciences (semiotics, political science, sociology, design), 1 in the technological field (robotics and AI). As a consequence of the deployment of the questionnaire, an adjustment of the model has been iteratively performed. The Table 1 summarises the use-cases interviewed.
A two-steps methodological approach is proposed. The first step is descriptive and consists in extracting and structuring data from the verbatim of the interviews. The goal of this step is to highlight the structure and dynamics of the interactions among stakeholders and within the worker-AI team, and the integration process within the organisation. In particular, a focus on the comparison between the period before and after the introduction of the solution has allowed a better understanding of its impact on the work. This descriptive analysis can be conducted using graphical architectures and comparative tables, whose introduction is out of the scope of this article. The second step, which is analytical and demands a use-case oriented interpretation of verbatim, is presented and discussed in detail in the following paragraphs.

# 5 The Analysis of the Results: The Preliminary Taxonomy

From the analysis of verbatims, three main axes seem to emerge, each one corresponding to a dimension of the model (excluding the Core framework dimension). These three axes allow the identification of a three-dimensional space that defines 8 areas characterising the worker attitude in teaming with an AI system, as shown in Fig. 3.

<figure><img src="image_6.png" /><figcaption>**Fig. 3.** The three-dimensional taxonomy. As an example, the point A is in the area having high management consistency, high worker's adherence and high system understanding. The point C is in the area having high management consistency, low work awareness and low system understanding.</figcaption></figure>

The axes of the taxonomy are described in the following paragraphs, where some extracts of the interviews expressed in quotations will be introduced to support the research results. In order to respect the confidentiality of the interviewed actors the exact sources of quotations are omitted.

## 5.1 Management Consistency

The “Management Consistency” axis is related to the role of the management in the deployment of the AI system, imposing a use that can be more or less consistent with
the recommendations of the solution provider. When an AI tool is deployed, in fact, recommendations and rules are often provided to be able to achieve the best benefits from the solution. Concerning this aspect, we have observed two main kinds of behaviours presented in different case-studies. The first behaviour is the answer to a management that imposes the tool but that still allows a certain degree of freedom to the worker in its deployment. Some of the interviewed workers state "I made my own method", "I don't have to report the results to my hierarchy", "The tool provides enormous flexibility but only because we allow it" "I trust the AI solution but I do not use this at the beginning of the process but only after in a second step". The second behaviour derives from a management that imposes not only the use of the AI solution but also the information flow and the exact sequencing of use. Hence "the control is there [...] we are tracked daily". A provider states that the worker can "project his/her intuition on the model [...] that can distort the results", "our main recommendation is to use our tool as early as possible in the [...] process, since the models are much more reliable and [...] much more relevant".

## 5.2 System Understanding

The "System Understanding" axis is the understanding of the system "intelligence", in terms of data sources, and the workers' contribution (if allowed) to it. Independently from the specific technology at the base of the intelligent solution (e.g. chatbot, deep neural networks for data classification, optimization algorithms, physical robots), we have observed that the understanding of the fundamental principles of the solution (e.g. parameters used and their meaning, associated database, basic principles) has an impact on the worker-AI team dynamics. "We spend a lot of time with users to explain what the robot can't do, because there is a real difference between what people expect a robot to do and what a robot actually can do". "I never use their models, I only use the model we create", "[the provider] has studied its algorithms very well", "the tool is very thoughtful. There are algorithms and people who think about it, that's why it can put my opinion in perspective". The deeper the understanding the more positive is the dynamics that is created and the adoption rate of the solution. "It is a system that we have shaped at our image", "it's a system imposed but we do not understand the working mechanisms behind, we stop at what it shows us, we do not try to go further!".

## 5.3 Worker's Adherence

The "Worker's Adherence" is the adherence of the worker to the values offered by the AI system. In other words, it represents the match between the value offer and the value system, as described in the model. All workers interviewed were committed to doing a good job. They are in the best position to know how. A good job is "when you have a real feeling of choice". Workers demonstrate mastery of the skills and of the workflow associated with their job. If the AI system meets their needs, it is adopted and supported. "You need this motivation, this involvement, because a tool (...) if it is not kept up to date if it is not managed it won't be used and it will be obsolete". Its use can also be diverted: "I don't read the result [...], because it introduces a bias for me", "it influences our judgement". Adherence can influence the trust of workers, even if it is not always
based on a real understanding, as pointed out before. This trust can be instrumentalized to serve the worker's convenience and "develop sometimes complicated discussions by saying - it's not me, it's neutral, it's the tool. What do you think?". "I know the physics, the machine just validates my previous intuitions".

## 5.4 Worker's Profiling

The structure highlighted with the introduction of the three axes can lead to the identification of different worker's profiles as illustrated in Fig. 4.

<figure><img src="image_7.png" /></figure>

**Fig. 4.** The worker's profiling consists of 8 profiles. In the two-dimensional diagram, 4 profiles are shown according to the level (high or low) of manager consistency and of system understanding. The 4 profiles written in bold characters refer to a strong worker's adherence. The 4 profiles written in normal characters refer to a weak worker's adherence.

Since the research is still ongoing, the taxonomy is based on a limited set of interviews and cannot allow us to adequately define all the different worker profiles. Nevertheless, even if the collected data are related to field-dependent, individual, and specific work practices, we could highlight common characteristics of the worker-AI teaming dynamics and similar worker attitude. It is important to point out that within the same organisation, different attitudes can coexist depending on the worker teaming perception. This perception can evolve and can be influenced by human-centred management and AI design practices. Whenever the worker's adherence is met, for a high level of understanding of the AI system, the Expert is aware of its benefits and fully endorses its deployment. Whenever freedom is allowed, the Co-designer is capable of truly exploiting it by adapting the system to a specific task. If the level of understanding is low, the Creative has the freedom to modify the decisional flow, introducing alternative working modes. The Follower, unlike the Creative, hasn't this freedom and applies the workflow suggested by recommendations. In the absence of worker's adherence, the risk of a failure in the deployment of the AI system can be important. For a high level of conformity imposed by the management, the Sceptical can understand the AI system but the cooperation to the deployment can be minimal. This low level of involvement becomes obstructive if
there is a lack of understanding of the system (Obstructor profile). The worker's attitude can be less adversarial, but still far from constructive, if more freedom is given. In this context, the Critical clearly "does" otherwise and uses shortcuts whenever possible. The Passive doesn't care at all and often doesn't use the system.

# 6 Conclusions and Future Work

A method to evaluate the impact of AI systems on workers has been proposed. The method is based on a four-dimensional model derived from what we believe to be the most relevant features that impact work from the human perspective, thus in a human-centred approach. We studied nine use-cases and interviewed 18 stakeholders, workers, managers and providers. The analysis of the verbatims allowed the introduction of a preliminary taxonomy and the identification of 8 worker profiles, that describe the worker's attitude in teaming with the AI system. The taxonomy reflects the impactful features of each dimension of the model, system, social and strategic. The taxonomy is an attempt to synthesise the data and it will certainly evolve and gain in pertinence and consistency as more use-cases will be analysed in the future. Nevertheless, the preliminary results highlight common trends and reveal some points of attention in the design and deployment of an AI system. Firstly, the understanding of the AI system by the worker and, if possible, the involvement in the system design is critical to promote the successful adoption of an AI solution by workers. Secondly, management strategy and decisions in the deployment of AI is mandatory to promote motivation and engagement. Providing clear and motivated guidelines is important but another factor should be considered. Allowing creativity and freedom to workers in the decisional process to modulate the AI implementation according to the context of the workplace, thus choosing the teaming mode with AI, can be a winning strategy for an even more successful implementation. This can contribute to finding self-motivation and personal fulfilment in the workplace. Finally, coherence between the value offer and value system can create trust, establishing a positive dynamic in the worker-AI team. Providers can have a huge impact on the worker-AI team: to obtain a constructive acceptance of the AI system, understanding and creativity must be stimulated and not inhibited. Together with managers, they are thus responsible to build a motivated trust on the technology deployed. Finally, this first part of the research revealed the fundamental importance of applying a human-centred approach when integrating AI systems in the work environment.

By considering the overall results of the research, we can conclude that the main weakness of the model consists in the difficulty to reach and to include in the analysis the effective beneficiaries of the study: the workers. Solution providers are often unable to give us customers' contact information. As in the case of managers, their point of view can be easily biased and does not always provide a clear insight of the impact of the AI solution on workers. Hence, particular attention must be payed when analysing data.

In contrast, the main strength of the model is the capability to provide a general framework to observe and analyse the impacts of AI tools on the work environment, regardless of the specificity of the technical solution deployed, the field of application and the particular context of a use-case. The insight offered by the interdisciplinary
approach of the research, allowed to observe patterns in worker's behaviours with respect to the introduction of an AI tool in the workflow. The model has therefore the potential to be generally applied, and useful to shift towards a positive impact of AI tool in the workplace.

## References

1. Auernhammer, J.: Human-centered AI: the role of human-centered design research in the development of AI. In: Boess, S., Cheung, M. and Cain, R. (eds.) Synergy - DRS International Conference 2020, 11–14 August, Held online (2020)

2. Shneiderman, B.: Human-centered artificial intelligence: realiable, safe & trustworthy. Int. J. Hum.-Comput. Interact. 495–504 (2020)

3. Shneiderman, B.: Human-Centered AI. Oxford University Press, Oxford (2022)

4. European Commission: Building Trust in Human-Centric Artificial Intelligence. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions, Brussels (2019)

5. High-Level Expert Group on Artificial Intelligence: The Assessment List for Trustworthy Artificial Intelligence (ALTAI). Report, European Commission, Brussels (2020)

6. Frontier Economics: The Impact of Artificial Intelligence on Work. Report (2018)

7. McKinsey Global Institute - McKinsey & Company: Notes from the AI Frontier Insights from Hundreds of Use Cases. Report (2018)

8. Villani, C.: Donner un sense à l'intelligence artificielle: pour une stratégie nationale et européenne. Report of a parliamentary mission, September 2017–March 2018 (2018)

9. GPAI (Global Partnership on Artificial Intelligence): Future of work AI observatory at workplace. Report, Paris Summit (2021)

10. Independent High-Level Expert Group on Artificial Intelligence set up by the European Commission: A Definition of AI: Main Capabilities and Disciplines. Brussels (2019)

11. Hughes, C., Robert, L.P., Frady, K., Arroyos, A.: Artificial intelligence, employee engagement, fairness, and job outcomes. In: Managing Technology and Middle- and Low-Skilled Employees, pp. 61–68 (2019)

12. Ferguson, Y., Pecoste, C.: L'IA au travail: proposition pour outiller la confiance. In: CNIA National Conference on Artificial Intelligence. Saint-Etienne, France, pp. 71–78 (2022)

13. OECD: OECD Framework for the Classification of AI Systems. OECD Publishing (2022)

14. European Commission: White Paper on Artificial Intelligence - A European Approach to Excellence and Confidence (2020)

15. European Commission: Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain legislative acts (2021)