A Granular Computing Approach to Provide Transparency of Intelligent Systems for Criminal Investigations

We live in an information society that strongly relies on information retrieval systems, such as search engines and conversational assistants. Consequently, the trustworthiness of these systems is of critical importance, and has attracted a significant research attention in recent years. In this work, we perform a systematic literature review of the field of fairness, accountability, transparency, and ethics in information retrieval. In particular, we investigate the definitions, approaches, and evaluation methodologies proposed to build trustworthy information retrieval systems. This review reveals the lack of standard definitions, arguably due to the multi-dimensional nature of the different notions. In terms of approaches, most of the work focuses on building either a fair or a transparent information retrieval system. As for evaluation, fairness is often assessed by means of automatic evaluation, while accountability and transparency are most commonly evaluated using audits and user studies. Based on the surveyed literature, we develop taxonomies of requirements for the different notions, and further use these taxonomies to propose practical definitions to quantify the degree to which an information retrieval system satisfies a given notion. Finally, we discuss challenges that have yet to be solved for information retrieval systems to be trustworthy.

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Configurations of human-centered AI at work: seven actor-structure engagements in organizations

Wilkens,

Lupp,

Langholf

2023

Front. Artif. Intell.

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PurposeThe discourse on the human-centricity of AI at work needs contextualization. The aim of this study is to distinguish prevalent criteria of human-centricity for AI applications in the scientific discourse and to relate them to the work contexts for which they are specifically intended. This leads to configurations of actor-structure engagements that foster human-centricity in the workplace.Theoretical foundationThe study applies configurational theory to sociotechnical systems’ analysis of work settings. The assumption is that different approaches to promote human-centricity coexist, depending on the stakeholders responsible for their application.MethodThe exploration of criteria indicating human-centricity and their synthesis into configurations is based on a cross-disciplinary literature review following a systematic search strategy and a deductive-inductive qualitative content analysis of 101 research articles.ResultsThe article outlines eight criteria of human-centricity, two of which face challenges of human-centered technology development (trustworthiness and explainability), three challenges of human-centered employee development (prevention of job loss, health, and human agency and augmentation), and three challenges of human-centered organizational development (compensation of systems’ weaknesses, integration of user-domain knowledge, accountability, and safety culture). The configurational theory allows contextualization of these criteria from a higher-order perspective and leads to seven configurations of actor-structure engagements in terms of engagement for (1) data and technostructure, (2) operational process optimization, (3) operators’ employment, (4) employees’ wellbeing, (5) proficiency, (6) accountability, and (7) interactive cross-domain design. Each has one criterion of human-centricity in the foreground. Trustworthiness does not build its own configuration but is proposed to be a necessary condition in all seven configurations.DiscussionThe article contextualizes the overall debate on human-centricity and allows us to specify stakeholder-related engagements and how these complement each other. This is of high value for practitioners bringing human-centricity to the workplace and allows them to compare which criteria are considered in transnational declarations, international norms and standards, or company guidelines.

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A Granular Computing Approach to Provide Transparency of Intelligent Systems for Criminal Investigations

Cited by 3 publications

References 37 publications

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review

A Systematic Review of Fairness, Accountability, Transparency and Ethics in Information Retrieval

Configurations of human-centered AI at work: seven actor-structure engagements in organizations

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