第 1 頁:Section I Use of English |
第 2 頁:Section II Reading Comprehension |
第 6 頁:Part B |
第 7 頁:Part C |
第 8 頁:Section III Writing |
Text 3
Any fair-minded assessment of the dangers of the deal between Britain's National Health Service (NHS) and DeepMind must start by acknowledging that both sides mean well. DeepMind is one of the leading artificial intelligence (AI) companies in the world. The potential of this work applied to healthcare is very great, but it could also lead to further concentration of power in the tech giants. It Is against that background that the information commissioner, Elizabeth Denham, has issued her damning verdict against the Royal Free hospital trust under the NHS, which handed over to DeepMind the records of 1.6 million patients In 2015 on the basis of a vague agreement which took far too little account of the patients' rights and their expectations of privacy.
DeepMind has almost apologized. The NHS trust has mended its ways. Further arrangements- and there may be many-between the NHS and DeepMind will be carefully scrutinised to ensure that all necessary permissions have been asked of patients and all unnecessary data has been cleaned. There are lessons about informed patient consent to learn. But privacy is not the only angle in this case and not even the most important. Ms Denham chose to concentrate the blame on the NHS trust, since under existing law it “controlled” the data and DeepMind merely “processed" it. But this distinction misses the point that it is processing and aggregation, not the mere possession of bits, that gives the data value.
The great question is who should benefit from the analysis of all the data that our lives now generate. Privacy law builds on the concept of damage to an individual from identifiable knowledge about them. That misses the way the surveillance economy works. The data of an individual there gains its value only when it is compared with the data of countless millions more.
The use of privacy law to curb the tech giants in this instance feels slightly maladapted. This practice does not address the real worry. It is not enough to say that the algorithms DeepMind develops will benefit patients and save lives. What matters is that they will belong to a private monopoly which developed them using public resources. If software promises to save lives on the scale that dugs now can, big data may be expected to behave as a big pharm has done. We are still at the beginning of this revolution and small choices now may turn out to have gigantic consequences later. A long struggle will be needed to avoid a future of digital feudalism. Ms Denham's report is a welcome start.
相關推薦:
2018年考研答案 ※ 2018年考研真題 ※ 考研萬題庫估分
· | 2022考研復試聯系導師有哪些注意事 | 04-28 |
· | 2022考研復試面試常見問題 | 04-28 |
· | 2022年考研復試面試回答提問方法有 | 04-28 |
· | 2022考研復試怎么緩解緩解焦慮心態(tài) | 04-27 |
· | 2022年考研復試的訣竅介紹 | 04-27 |
· | 2022年考研復試英語如何準備 | 04-26 |
· | 2022年考研復試英語口語常見句式 | 04-26 |
· | 2022年考研復試的四個細節(jié) | 04-26 |
· | 2022考研復試準備:與導師及時交流 | 04-26 |
· | 2022考研復試面試的綜合技巧 | 04-26 |