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A systemic perspective on bridging the principles-to-practice gap in creating ethical artificial intelligence solutions  – a critique of dominant narratives and proposal for a collaborative way forward

Christian Herzog*, Sabrina Blank

*Corresponding author for this work

Abstract

We critique demands in artificial intelligence and technology development for bridging the so-called principles-to-practice gap that are voiced for instrumental reasons, such as accelerating adoption or creating trust, via clearly actionable ethical rules or via outsourced guidance offered by ethics ‘experts.’ We contend that these views are prone to amount to a simple reconfiguration of technical implementation. We support inclusive and philosophically grounded ethical reflection as key to bridging the gap. However, we acknowledge that this requires assistance, e.g., in the form of platform structures as part of ecosystem governance. Such platforms should facilitate inclusive discourse to disaggregate ethical principles on different levels of abstraction, and contextual framings, as well as support the considerable interdisciplinary work for unpacking them. Translating ethical principles into practice involves indispensable collaborative processes of (self-)reflection, including those with epistemic privilege on the relevant subject matter. We propose that ecosystems should provide the corresponding infrastructure.

Original languageEnglish
Article number2431350
JournalJournal of Responsible Innovation
Volume11
Issue number1
ISSN2329-9460
DOIs
Publication statusPublished - 2024

Funding

KI-SIGS is a German acronym that stands for AI spaces for intelligent health systems. The project was funded by the German Ministry for Economic Affairs and Climate Action with about 11 Mio. €, consisted of nine translational research projects to bring AI technology into medical products as well as four ecosystem platform structures geared towards collaboration, technological exchange, regulatory affairs and responsible innovation.

FundersFunder number
German Ministry for Economic Affairs and Climate Action

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 9 - Industry, Innovation, and Infrastructure
      SDG 9 Industry, Innovation, and Infrastructure
    2. SDG 16 - Peace, Justice and Strong Institutions
      SDG 16 Peace, Justice and Strong Institutions

    Research Areas and Centers

    • Centers: Center for Artificial Intelligence Luebeck (ZKIL)

    DFG Research Classification Scheme

    • 4.43-04 Artificial Intelligence and Machine Learning Methods

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