Ever Heard of Ethical AI? Investigating the Salience of Ethical AI Issues among the German Population

Kieslich, Kimon; Lünich, Marco; Dosenovic, Pero

doi:10.1080/10447318.2023.2178612

Cited by 8 publications

(3 citation statements)

References 50 publications

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“…Their results reveal a rather indifferent attitude of German citizens regarding AI with educational level and interest in the topic being predictors of concerns with AI. Issues such as fairness, accountability, or transparency, although frequently addressed in political and academic discourse, do not seem to resonate with the general population (Kieslich et al, 2023). Against this backdrop, our data shows a puzzling variety of ethical concerns regarding social inequality, transparency, or autonomy in the context of ADM. One explanation might be the circumstance that the textual responses we examined were provided as direct responses to the vignette scenarios presented, which were a steady point of reference.…”

Section: Public Awareness Regarding Aimentioning

confidence: 60%

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“The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making

Szafran,

Bach

2024

Minds & Machines

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The increasing use of algorithms in allocating resources and services in both private industry and public administration has sparked discussions about their consequences for inequality and fairness in contemporary societies. Previous research has shown that the use of automated decision-making (ADM) tools in high-stakes scenarios like the legal justice system might lead to adverse societal outcomes, such as systematic discrimination. Scholars have since proposed a variety of metrics to counteract and mitigate biases in ADM processes. While these metrics focus on technical fairness notions, they do not consider how members of the public, as most affected subjects by algorithmic decisions, perceive fairness in ADM. To shed light on subjective fairness perceptions of individuals, this study analyzes individuals’ answers to open-ended fairness questions about hypothetical ADM scenarios that were embedded in the German Internet Panel (Wave 54, July 2021), a probability-based longitudinal online survey. Respondents evaluated the fairness of vignettes describing the use of ADM tools across different contexts. Subsequently, they explained their fairness evaluation providing a textual answer. Using qualitative content analysis, we inductively coded those answers (N = 3697). Based on their individual understanding of fairness, respondents addressed a wide range of aspects related to fairness in ADM which is reflected in the 23 codes we identified. We subsumed those codes under four overarching themes: Human elements in decision-making, Shortcomings of the data, Social impact of AI, and Properties of AI. Our codes and themes provide a valuable resource for understanding which factors influence public fairness perceptions about ADM.

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Section: Public Awareness Regarding Aimentioning

confidence: 60%

“…The last point we highlight is AI awareness by the public. A recent paper by Kieslich et al (2023) investigated Germans' public opinion towards AI. Their results reveal a rather indifferent attitude of German citizens regarding AI with educational level and interest in the topic being predictors of concerns with AI.…”

Section: Public Awareness Regarding Aimentioning

confidence: 99%

“The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making

Szafran,

Bach

2024

Minds & Machines

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“…The broader applications of artificial intelligence offer numerous possibilities, but on the other hand, they can raise societal concerns (Ikkatai, Hartwig, Takanashi, Yokoyama, 2022;Kieslich, Lünich, & Došenović, 2023;Wach et al, 2023). The dark side of).…”

Section: Introductionmentioning

confidence: 99%

Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics

Ejdys,

Czerwińska,

Ginevičius

2024

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The aim of the conducted research was to assess the attitude of the Polish society towards the use of artificial intelligence in medical diagnostics. In the research process, we sought answers to three research questions: how trust in the use of AI for medical diagnostics can be measured; if societal openness to technology determines trust in the use of AI for medical diagnostics purposes; and if a higher level of trust in the use of AI for medical diagnostics influences the potential improvement in the quality of medical diagnostics as perceived by Poles. The authors' particular focus was on the following three constructs and the relationships between them: openness to new technologies (OP), willingness to trust AI in medical diagnostics (T), and perceived impact of AI application on the quality of medical diagnostic services (PI). A survey was conducted on a representative sample of 1063 Polish respondents to seek answers to the above questions. The survey was conducted using the CATI technique.

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Anticipating impacts: using large-scale scenario-writing to explore diverse implications of generative AI in the news environment

Kieslich,

Diakopoulos,

Helberger

2024

AI Ethics

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The tremendous rise of generative AI has reached every part of society—including the news environment. There are many concerns about the individual and societal impact of the increasing use of generative AI, including issues such as disinformation and misinformation, discrimination, and the promotion of social tensions. However, research on anticipating the impact of generative AI is still in its infancy and mostly limited to the views of technology developers and/or researchers. In this paper, we aim to broaden the perspective and capture the expectations of three stakeholder groups (news consumers; technology developers; content creators) about the potential negative impacts of generative AI, as well as mitigation strategies to address these. Methodologically, we apply scenario-writing and use participatory foresight in the context of a survey (n = 119) to delve into cognitively diverse imaginations of the future. We qualitatively analyze the scenarios using thematic analysis to systematically map potential impacts of generative AI on the news environment, potential mitigation strategies, and the role of stakeholders in causing and mitigating these impacts. In addition, we measure respondents' opinions on a specific mitigation strategy, namely transparency obligations as suggested in Article 52 of the draft EU AI Act. We compare the results across different stakeholder groups and elaborate on different expected impacts across these groups. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment.

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Ever Heard of Ethical AI? Investigating the Salience of Ethical AI Issues among the German Population

Cited by 8 publications

References 50 publications

“The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making

“The Human Must Remain the Central Focus”: Subjective Fairness Perceptions in Automated Decision-Making

Social acceptance of artificial intelligence (AI) application for improving medical service diagnostics

Anticipating impacts: using large-scale scenario-writing to explore diverse implications of generative AI in the news environment

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