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The news that the Home Office is sorting applications for visas with secret algorithms (computing) applied to online applications is a reminder of one of Theresa May’s more toxic and long-lasting legacies: her immigration policies as home secretary. Yet even if the government’s aims in immigration policy were fair and balanced, there would still be serious issues of principle involved in digitizing the process.
Handing over life-changing decisions to machine-learning algorithms is always risky. Small biases in the data become large biases in the outcome, but these are difficult to challenge because the use of software covers them in clouds of confusion and supposed objectivity, especially when its workings are described as “artificial intelligence”. This is not to say they are always harmful, or never any use: with careful training and well-understood, clearly defined problems, and when they are operating on good data, software systems can perform much better than humans ever could.
This isn’t just a problem of software. There is a sense in which the whole of the civil service, like any other bureaucracy, is an algorithmic machine: it deals with problems according to a set of determinate rules. The good civil servant, like a computer program, executes their instructions faithfully and does exactly what they are told. The civil servant is supposed to have a clearer grasp of what their instructing human means and really wants than any computer could. But when the instructions are clear, the machinery of government—a telling metaphor—is meant to put them into action.
Digital algorithms make it easy to make bigger mistakes, faster, and with less accountability. One key difference between the analogue bureaucracy of the traditional civil service and the digitized bureaucracy of artificial intelligence is that it is very much easier to hold a human organization to account and to retrace the process by which it reached a decision. The workings of neural networks are usually vague even to their programmers.
Yet the technology promises so much to governments that they will certainly set up it. This does not mean we are powerless. There are simple, non-technical tests that can be applied to government technology: we can ask easily whether digitizing any particular service makes life better for the public who must use it or merely more convenient for the civil service. In some cases these aims are directly opposed. Forcing applicants for universal credit to apply online directly harms anyone without an internet connection, and such people are likely to be most in need of it. Similarly, the digitization of immigration services means it is more difficult and more expensive to get a face-to-face interview; again, something that is likely to damage those who need it most.
36. According to Paragraph 1, secret algorithms ______.
[A] are used to apply for visas efficiently
[B] cause principle problems for immigration policy
[C] are fair and balanced for immigration policy
[D] belong to one of Theresa’s long-lasting legacies
37. It’s hard to find small data biases in that .
[A] algorithms prefer large biases to small ones
[B] they are covered up by the use of software
[C] they are not clearly defined in criteria
[D] people haven’t been well trained on this
38. In part, the civil service is similar to algorithmic machine because ______.
[A] civil service is set up by algorithmic machine
[B] civil service can identify rules of algorithmic machine
[C] civil servants have the same comprehension with machine
[D] civil servants carry out instructions by certain rules
39. Which one is the feature of digital algorithms?
[A] Certainty of instruction and responsibility.
[B] Uselessness for traditional civil service.
[C] Non-transparency to programmers and civil servants.
[D] Convenience of retracing the decision-making process.
40. The author’s attitude toward digital algorithms in immigration services can be described as ______.
[A] critical
[B] supportive
[C] indifferent
[D] uncertain
