19 March 2021
Three applicants v Ola Netherlands B.V. C/13/689705 / HA RK 20-258, District Court, Amsterdam (11 March 2021)
An Amsterdam Court has ordered Ola (a smartphone-hailing taxi organisation like Uber) to be more transparent about the data it uses as the basis for decisions on suspensions and wage penalties, in a ruling that breaks new ground on the rights of workers subject to algorithmic management.
James Farrarr and Yaseen Aslam, who won the landmark victory in the UK Supreme Court in February, led the action by a group of UK drivers and a Portuguese driver, who bought three separate cases against Ola and Uber seeking fuller access to their personal data.
The following is a summary of the case against Ola taxis. Anton Ekker (assisted by AI expert Jacob Turner, whom we interviewed on Law Pod UK here) represented the drivers. He said that this case was the first time, to his knowledge, that a court had found that workers were subject to automated decision-making (as defined in Article 22 of the GDPR) thus giving them the right to demand human intervention, express their point of view and appeal against the decision.
Ola is a company whose parent company is based in Bangalore, India. Ola Cabs is a digital platform that pairs passengers and cab drivers through an app. The claimants are employed as ‘private hire drivers’ (“drivers”) in the United Kingdom. They use the services of Ola through the Ola Driver App and the passengers they transport rely on the Ola Cabs App.
Proceedings are pending in several countries between companies offering services through a digital platform and drivers over whether an employment relationship exists.
By separate requests dated 23 June 2020, the first two claimants requested Ola to disclose their personal data processed by Ola and make it available in a CSV file. The third claimant made an access request on 5 August 2020. Ola provided the claimants with a number of digital files and copies of documents in response to these requests.
Ola has a “Privacy Statement” in which it has included general information about data processing.
All references in this judgment is to the AVG, which is Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of such data (GDPR).
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18 November 2019
In the latest Henry Brooke Lecture (12th November, hosted by BAILII and Freshfields Bruckhaus Deringer), Supreme Court Justice Lord Sales warned that the growing role of algorithms and artificial intelligence in decision making poses significant legal problems.
He cited as an example a recent case in Singapore. The judge had to decide on mistake in contract – except that the two contracting parties were both algorithms. In that instance the judge was able to identify the human agents behind the programmes, but that will soon not be the case.
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4 November 2019
The use of algorithms in public sector decision making has
broken through as a hot topic in recent weeks. The Guardian recently ran the “Automating
Poverty” series on the use of algorithms in the welfare state. And on 29
October 2019 it was reported
that the first known legal challenge to the use of algorithms in the UK, this
time by the Home Office, had been launched. It was timely, then, that the
Public Law Project’s annual conference on judicial review trends and forecasts
was themed “Public law and technology”.
Basic tech for lawyers
The conference helpfully opened with a lawyer-friendly run down of algorithms and automation. Dr. Reuben Binns (ICO Postdoctoral Research Fellow in AI) drew a number of useful distinctions.
The first was between rule-based and statistical machine learning systems. In rule-based systems, the system is programmed to apply a decision-making tree. The questions asked and the path to a particular outcome, depending on the answers given, can be depicted by way of flow-chart (even if that flow-chart might be very large, involving numerous branches). In contrast, statistical machine learning involves a computer system training itself to spot patterns and correlations in data sets, and to make predictions based on those patterns and correlations. The computer system is first trained on data sets provided by the system designer. Once trained, it can be used to infer information and make predictions based on new data. These systems might be used, for example, to assess the risk of a person re-offending, where the system has been trained on existing data as to re-offending rates. It has long been known that machine-learning systems can be biased, not least because the data on which they are trained is often biased.
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18 April 2018
A report from the UK House of Lords Select Committee on Artificial Intelligence has made a number of recommendations for the UK’s approach to the rise of algorithms. The report ‘AI in the UK: ready, willing and able?’ suggests the creation of a cross-sector AI Code to help mitigate the risks of AI outstripping human intelligence.
The main recommendation in the report is that autonomous power to hurt, destroy or deceive human beings should never be vested in artificial intelligence. The committee calls for the Law Commission to clarify existing liability law and considers whether it will be sufficient when AI systems malfunction or cause harm to users. The authors predict a situation where it is possible to foresee a scenario where AI systems may
malfunction, underperform or otherwise make erroneous decisions which cause harm. In particular, this might happen when an algorithm learns and evolves of its own accord.
The authors of the report confess that it was “not clear” to them or their witnesses whether “new mechanisms for legal liability and redress in such situations are required, or whether existing mechanisms are sufficient”. Their proposals, for securing some sort of prospective safety, echo Isaac Asimov’s three laws for robotics.
- A robot may not injure a human being or, through inaction, allow a human being to come to harm.
- A robot must obey orders given it by human beings except where such orders would conflict with the First Law.
- A robot must protect its own existence as long as such protection does not conflict with the First or Second Law.
But these elaborations of principle may turn out to be merely semantic. The safety regime is not just a question of a few governments and tech companies agreeing on various principles. This is a global problem – and indeed even if Google were to get together with all the other giants in this field, Alibaba, Alphabet, Amazon, Apple, Facebook, Microsoft and Tencent, it may not be able to anticipate the consequences of building machines that can self-improve.
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1 September 2017
We have just posted a discussion here between 1 Crown Office Row recruit Thomas Beamont and Rosalind English on the reach of Artificial Intelligence into the legal world: click on Episode 10 of our podcast series.
Law Pod UK is freely available for download on iTunes
22 January 2017
Artificial intelligence … it’s no longer in the future. It’s with us now.
I posted a review of a book about artificial intelligence in autumn last year. The author’s argument was not that we might find ourselves, some time in the future, subservient to or even enslaved by cool-looking androids from Westworld. His thesis is more disturbing: it’s happening now, and it’s not robots. We are handing over our autonomy to a set of computer instructions called algorithms.
If you remember from my post on that book, I picked out a paragraph that should give pause to any parent urging their offspring to run the gamut of law-school, training contract, pupillage and the never never land of equity partnership or tenancy in today’s competitive legal industry. Yuval Noah Harari suggests that the everything lawyers do now – from the management of company mergers and acquisitions, to deciding on intentionality in negligence or criminal cases – can and will be performed a hundred times more efficiently by computers.
Now here is proof of concept. University College London has just announced the results of the project it gave to its AI researchers, working with a team from the universities of Sheffield and Pennsylvania. Its news website announces that a machine learning algorithm has just analysed, and predicted, “the outcomes of a major international court”:
The judicial decisions of the European Court of Human Rights (ECtHR) have been predicted to 79% accuracy using an artificial intelligence (AI) method.
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28 September 2016
Not only is God dead, says Israeli professor Yuval Noah Harari, but humanism is on its way out, along with its paraphernalia of human rights instruments and lawyers for their implementation and enforcement. Whilst they and we argue about equality, racism, feminism, discrimination and all the other shibboleths of the humanist era, silicon-based algorithms are quietly taking over the world.
His new book, Homo Deus, is the sequel to Homo Sapiens, reviewed on the UKHRB last year. Sapiens was “a brief history of mankind”, encompassing some seventy thousand years. Homo Deus the future of humankind and whether we are going to survive in our present form, not even for another a thousand years, but for a mere 200 years, given the rise of huge new forces of technology, of data, and of the potential of permissive rather than merely preventative medicine.
We are suddenly showing unprecedented interest in the fate of so-called lower life forms, perhaps because we are about to become one.
Harari’s message in Sapiens was that the success of the human animal rests on one phenomenon: our ability to create fictions, spread them about, believe in them, and then cooperate on an unprecedented scale. These fictions include not only gods, but other ideas we think fundamental to life, such as money, human rights, states and institutions. In Homo Deus he investigates what happens when these mythologies meet the god-like technologies we have created in modern times.
In particular, he scrutinises the rise and current hold of humanism, which he regards as no more secure than the religions it replaced. Humanism is based on the notion of individuality and the fundamental tenet that each and everybody’s feelings and experiences are of equal value, by virtue of being human. Humanism cannot continue as a credible thesis if the concept of individuality is constantly undermined by scientific discoveries, such as the split brain, and pre-conscious brain activity that shows that decisions are not made as a result of conscious will (see the sections on Gazzaniga’s and Kahneman’s experiments in Chapter 8 “The Time Bomb in the Laboratory”).
…once biologists concluded that organisms are algorithms, they dismantled the wall between the organic and inorganic, turned the computer revolution from a purely mechanical affair into a biological cataclysm, and shifted authority from individual networks to networked algorithms.
… The individual will not be crushed by Big Brother; it will disintegrate from within. Today corporations and governments pay homage to my individuality, and promise to provide medicine, education and entertainment customised to my unique needs and wishes. But in order to do so, corporations and governments first need to break me up into biochemical subsystems, monitor these subsystems with ubiquitous sensors and decipher their working with powerful algorithms. In the process, the individual will transpire to be nothing but a religious fantasy.
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