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allows for computing on encrypted data, where the outcomes or result is still encrypted and unknown to those performing the computation, but can be deciphered by the original encryptor. It is often developed with the goal of enabling use in jurisdictions different from the data creation (under e.g.
440:, accountable, and robust. Transparency in AI involves making the processes and decisions of AI systems understandable to users and stakeholders. Accountability ensures that there are protocols for addressing adverse outcomes or
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that may arise, with designated responsibilities for oversight and remediation. Robustness and security aim to ensure that AI systems perform reliably under various conditions and are safeguarded against malicious attacks.
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moved online in 2020, the
TrustworthyAI seminar series was initiated to start discussions on such work, which eventually led to the standardization activities.
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standardization meetings, which has developed a standard on homomorphic encryption. The 5th homomorphic encryption meeting was hosted at ITU HQ in
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in the preservation of privacy was examined at several of the "Day 0" machine learning workshops at AI for Good Global
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for privacy preservation are used extensively in the multimedia standards of
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The concept of trustworthy AI also encompasses the need for AI systems to be
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has been conducted in the ITU-T Focus Group on
Digital Ledger Technologies.
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ITU-WHO Focus Group on
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ITU has been collaborating since the early stage of the
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277:Friendly AI
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732:2022-10-24
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663:2022-10-24
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608:References
538:) such as
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306:Regulation
260:Philosophy
215:Healthcare
210:Government
112:Approaches
774:1364-8535
685:CC BY 4.0
336:AI winter
237:Military
100:AI safety
687:license.
657:Archived
586:See also
359:Glossary
353:Glossary
331:Progress
326:Timeline
286:Takeover
247:Projects
220:Industry
183:Finance
173:Deepfake
123:Symbolic
95:Robotics
70:Planning
783:4092631
341:AI boom
319:History
242:Physics
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516:Geneva
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291:Ethics
554:(aka
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