AI Act, Artikkel 15, SE
1. High-risk AI systems shall be designed and developed in such a way that they achieve an appropriate level of accuracy, robustness, and cybersecurity, and that they perform consistently in those respects throughout their lifecycle.
2. To address the technical aspects of how to measure the appropriate levels of accuracy and robustness set out in paragraph 1 and any other relevant performance metrics, the Commission shall, in cooperation with relevant stakeholders and organisations such as metrology and benchmarking authorities, encourage, as appropriate, the development of benchmarks and measurement methodologies.
3. The levels of accuracy and the relevant accuracy metrics of high-risk AI systems shall be declared in the accompanying instructions of use.
4. High-risk AI systems shall be as resilient as possible regarding errors, faults or inconsistencies that may occur within the system or the environment in which the system operates, in particular due to their interaction with natural persons or other systems. Technical and organisational measures shall be taken in this regard.
The robustness of high-risk AI systems may be achieved through technical redundancy solutions, which may include backup or fail-safe plans.
High-risk AI systems that continue to learn after being placed on the market or put into service shall be developed in such a way as to eliminate or reduce as far as possible the risk of possibly biased outputs influencing input for future operations (feedback loops), and as to ensure that any such feedback loops are duly addressed with appropriate mitigation measures.
5. High-risk AI systems shall be resilient against attempts by unauthorised third parties to alter their use, outputs or performance by exploiting system vulnerabilities.
The technical solutions aiming to ensure the cybersecurity of high-risk AI systems shall be appropriate to the relevant circumstances and the risks.
The technical solutions to address AI specific vulnerabilities shall include, where appropriate, measures to prevent, detect, respond to, resolve and control for attacks trying to manipulate the training data set (data poisoning), or pre-trained components used in training (model poisoning), inputs designed to cause the AI model to make a mistake (adversarial examples or model evasion), confidentiality attacks or model flaws.
Versjon: 6e3c5fb2e0b90c4f
SHA-256: sha256:6e3c5fb2e0b90c4f40ba493d9cb5708d1e940f21047fce23cfb994373412201f
- Artikkel 1
- Artikkel 2
- Artikkel 3
- Artikkel 4
- Artikkel 5
- Artikkel 6
- Artikkel 7
- Artikkel 8
- Artikkel 9
- Artikkel 10
- Artikkel 11
- Artikkel 12
- Artikkel 13
- Artikkel 14
- Artikkel 16
- Artikkel 17
- Artikkel 18
- Artikkel 19
- Artikkel 20
- Artikkel 21
- Artikkel 22
- Artikkel 23
- Artikkel 24
- Artikkel 25
- Artikkel 26
- Artikkel 27
- Artikkel 28
- Artikkel 29
- Artikkel 30
- Artikkel 31
- Artikkel 32
- Artikkel 33
- Artikkel 34
- Artikkel 35
- Artikkel 36
- Artikkel 37
- Artikkel 38
- Artikkel 39
- Artikkel 40
- Artikkel 41
- Artikkel 42
- Artikkel 43
- Artikkel 44
- Artikkel 45
- Artikkel 46
- Artikkel 47
- Artikkel 48
- Artikkel 49
- Artikkel 50
- Artikkel 51
- Artikkel 52
- Artikkel 53
- Artikkel 54
- Artikkel 55
- Artikkel 56
- Artikkel 57
- Artikkel 58
- Artikkel 59
- Artikkel 60
- Artikkel 61
- Artikkel 62
- Artikkel 63
- Artikkel 64
- Artikkel 65
- Artikkel 66
- Artikkel 67
- Artikkel 68
- Artikkel 69
- Artikkel 70
- Artikkel 71
- Artikkel 72
- Artikkel 73
- Artikkel 74
- Artikkel 75
- Artikkel 76
- Artikkel 77
- Artikkel 78
- Artikkel 79
- Artikkel 80
- Artikkel 81
- Artikkel 82
- Artikkel 83
- Artikkel 84
- Artikkel 85
- Artikkel 86
- Artikkel 87
- Artikkel 88
- Artikkel 89
- Artikkel 90
- Artikkel 91
- Artikkel 92
- Artikkel 93
- Artikkel 94
- Artikkel 95
- Artikkel 96
- Artikkel 97
- Artikkel 98
- Artikkel 99
- Artikkel 100
- Artikkel 101
- Artikkel 102
- Artikkel 103
- Artikkel 104
- Artikkel 105
- Artikkel 106
- Artikkel 107
- Artikkel 108
- Artikkel 109
- Artikkel 110
- Artikkel 111
- Artikkel 112
- Artikkel 113
Source: NovaCopilot