Trustworthy artificial intelligence

Достоверный искусственный интеллект
Scott Thiebes, Sebastian Lins, Ali Sunyaev
2020-10-01

AI ethicsAI governanceDistributed ledger technologyExplicabilityTrustworthy artificial intelligence
Abstract Artificial intelligence (AI) brings forth many opportunities to contribute to the wellbeing of individuals and the advancement of economies and societies, but also a variety of novel ethical, legal, social, and technological challenges. Trustworthy AI (TAI) bases on the idea that trust builds the foundation of societies, economies, and sustainable development, and that individuals, organizations, and societies will therefore only ever be able to realize the full potential of AI, if trust can be established in its development, deployment, and use. With this article we aim to introduce the concept of TAI and its five foundational principles (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. We further draw on these five principles to develop a data-driven research framework for TAI and demonstrate its utility by delineating fruitful avenues for future research, particularly with regard to the distributed ledger technology-based realization of TAI.
1
The authors develop a data-driven research framework for trustworthy AI based on the five foundational principles.
2
The framework identifies future research opportunities, especially for implementing trustworthy AI through distributed ledger technology.
3
The paper defines five foundational principles of trustworthy AI: beneficence, non-maleficence, autonomy, justice, and explicability.
4
These principles address ethical, legal, social, and technological challenges arising from AI development, deployment, and use.
5
Trustworthy AI is presented as necessary for realizing AI’s full potential in individual, economic, and societal contexts.

Trustworthy artificial intelligence (Trustworthy AI, TAI) as a concept and framework

The foundational ethical principles and data-driven research framework for establishing trust in AI, including beneficence, non-maleficence, autonomy, justice, and explicability

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Publication Date
2020-10-01
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Authors
Scott Thiebes
Sebastian Lins
Ali Sunyaev
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