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Mapping value sensitive design onto AI for social good principles.

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Summary
This summary is machine-generated.

Value sensitive design (VSD) is adapted for artificial intelligence (AI), addressing challenges like machine learning (ML) transparency and value disembodiment. The modified VSD approach integrates principles, distinguishes promoted vs. respected values, and spans the AI lifecycle for ethical outcomes.

Keywords:
AI4SGArtificial intelligenceCOVID-19Sustainable development goalsVSDValue sensitive design

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Area of Science:

  • Computer Science
  • Ethics
  • Human-Computer Interaction

Background:

  • Value sensitive design (VSD) is a method for integrating human values into technology design.
  • VSD has been applied to various technologies and is increasingly relevant for artificial intelligence (AI).
  • AI, particularly machine learning (ML), presents unique challenges to traditional VSD.

Purpose of the Study:

  • To propose a modified Value Sensitive Design (VSD) approach tailored for Artificial Intelligence (AI).
  • To address specific challenges posed by AI, such as lack of transparency and potential value disembodiment in ML systems.
  • To ensure ethical AI development by integrating values throughout the AI lifecycle.

Main Methods:

  • A threefold modified VSD approach is proposed for AI.
  • Integration of a defined set of VSD principles (AI4SG) as design norms.
  • Distinguishing between promoted and respected values and extending VSD across the AI technology lifecycle.

Main Results:

  • The modified VSD approach addresses AI-specific challenges like the "black box" nature of ML and value drift.
  • Proposed VSD principles (AI4SG) provide a framework for deriving specific design requirements.
  • The extended VSD process facilitates monitoring and redesign to mitigate unintended value consequences.

Conclusions:

  • The modified VSD approach offers a robust framework for ethical AI design and development.
  • Addressing transparency, accountability, and value alignment is crucial for trustworthy AI.
  • Continuous monitoring and adaptation throughout the AI lifecycle are essential for responsible innovation.