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Page 11: Ministry of Internal Affairs and Communications4.Decisions referred to in paragraph 2 shall not be based on special categories of personal data referred to in Article 9(2)1), unless

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Page 12: Ministry of Internal Affairs and Communications4.Decisions referred to in paragraph 2 shall not be based on special categories of personal data referred to in Article 9(2)1), unless

�� EU��� ������GDPR�n GDPR-221. The data subject shall have the right not to be subject to a decision based

solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her.

2. Paragraph 1 shall not apply if the decision: is necessary for entering into, or performance of, a contract between the data subject and a data controller; is authorised by Union or Member State law to which the controller is subject and which also lays down suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests; or is based on the data subject’s explicit consent.

3. In the cases referred to in points (a) and (c) of paragraph 2, the data controller shall implement suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests, at least the right to obtain human intervention on the part of the controller, to express his or her point of view and to contest the decision.

4. Decisions referred to in paragraph 2 shall not be based on special categories of personal data referred to in Article 9(2)1), unless point (a) or (g) of Article 9(2) applies and suitable measures to safeguard the data subject’s rights and freedoms and legitimate interests are in place.

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Page 14: Ministry of Internal Affairs and Communications4.Decisions referred to in paragraph 2 shall not be based on special categories of personal data referred to in Article 9(2)1), unless

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https://arxiv.org/abs/1606.08813

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Peeking inside the black-box: A survey on Explainable Artificial Intelligence (XAI)

https://ieeexplore.ieee.org/document/8466590/

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system”, “Machine learning”, “deep learning”,

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Why Should I Trust You?: Explaining the Predictions of Any Classifier, KDD’16.

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Understanding Black-box Predictions via Influence Functions, ICML’17.

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���� whenRelationship ≠ Not-in-family, WifeCapital Gain < 7370

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Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach, AISTATS’18.

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we developed and tested a machine learning based system called Prescience that predicts

real-time hypoxemia risk and presents an explanation of factors contributing to that risk

Prescience improved anesthesiologists' performance when providing interpretable hypoxemia risks with contributing factors.

The results suggest that if anesthesiologists currently anticipate 15% of events, then with Prescience assistance they could anticipate

30% of events

https://www.biorxiv.org/content/early/2017/10/21/206540

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