Big data in public administration rewards, risk amazing ways to make money and responses ukaji

In april 2019, I was at the socio-legal studies association conference at the university of leeds, presenting a work in progress, “artificial administration: administrative justice in the age of machines”. In this post, I explain my interest in this important issue. A version of this piece was initially posted at the amazing ways to make money administrative law matters blog here . Readers interested in the human rights implications of big data amazing ways to make money are encouraged to review the university of essex big data amazing ways to make money and human rights project (‘ HRBDT’ ).

We live in the era of big data, a term that “triggers both utopian and dystopian rhetoric”, [i] as it carries a “kind of novelty that is productive and empowering yet constraining amazing ways to make money and overbearing”. [ii] and we are now entering the world of artificial administration, where governmental bodies will replace or displace human decision-makers with information technology. A clash of value systems is or will soon be amazing ways to make money upon us, between technologists who insist on the glories of correlation and amazing ways to make money lawyers who refuse to yield on the time-honoured fundamentality of causation.

Artificial administration is the “sociotechnical ensemble” that combines technology and process “to mine a large volume of digital data to find amazing ways to make money patterns and correlations within that data, distilling the data into predictive analytics, and applying the analytics to new data”. [iii] my term evokes the replacement or displacement of human decision-makers by automated procedures. Techniques such as algorithms, neural nets and predictive analytics certainly have some place in amazing ways to make money the machinery of government, but clarity is needed about the settings in which they amazing ways to make money can properly be used.

Under the general rubric of “machine learning”, sophisticated algorithms, neural nets and other technologies carry great potential to improve amazing ways to make money governmental decision-making, by using automated systems to reduce cost, [iv] and, further, by reducing or removing the influence of fallible human beings, beset by various cognitive biases, on decisional processes. [v] the proposition that technology can improve the lot of the amazing ways to make money human race is not fanciful either, as technology makes possible new modes of deliberation and engagement, which can be assisted by government decision-makers committed to human flourishing. [vi] artificial administration seems destined to play a key role in amazing ways to make money day-to-day public administration in the twenty-first century.

Indeed, use of information technology has long been a feature of amazing ways to make money governmental decision-making. Section 2 of the social security act 1998 already made amazing ways to make money express provision for decision-making by computers (albeit those for which an official was responsible). [vii] automation of systems is also familiar. [viii] algorithms, too, are nothing especially mysterious – they are, after all, no more than recipes, specified steps to be taken to arrive at a desired amazing ways to make money conclusion. Primary school places in cambridge are allocated by algorithm, uncontroversially and straightforwardly. [ix] leveraging new technologies to further improve automated and algorithmic governmental amazing ways to make money decision-making, decreasing cost and increasing efficiency is an attractive proposition.

As is now notorious, however, machine learning is deliberately opaque. Paul dourish describes three layers of opacity: first, trade-secret protection, which ensures that private parties’ algorithms are shielded from public view, second, that “the ability to read or understand algorithms is a highly amazing ways to make money specialized skill, available only to a limited professional class”; third, with machine learning, no one can “reveal what the algorithm knows, because the algorithm knows only about inexpressible commonalities in millions amazing ways to make money of pieces of training data”. [x] indeed, “as these systems become more sophisticated (and as they begin to learn, iterate, and improve upon themselves in unpredictable or otherwise unintelligible ways), their logic often becomes less intuitive to human onlookers”. [xi]

When such techniques are used in a coercive fashion, [xii] to interfere with individuals’ rights, interests and privileges the stakes are self-evidently high: “it is the capacity of these systems to predict future amazing ways to make money action or behavior” based on machine learning techniques which is “widely regarded as the ‘holy grail’ of big data”, [xiii] but the use of such “anticipatory governance” means that “a person’s data shadow does more than follow them; it precedes them, seeking to police behaviours that may never occur”. [xiv]

Even when these techniques are used non-coercively, to gather information for the purposes of informing governmental decision-making, the implications may be troubling. [xv] first, in general, information technologies “carry the biases and errors of the people who wrote amazing ways to make money them”. [xvi] as such, if the data inputted is flawed, the outcomes will also be flawed. [xvii] second, those who are experts in machine-learning techniques will have much greater knowledge than elected representatives amazing ways to make money and members of the general public about how artificial administration amazing ways to make money operates. Whereas, while the internal workings of, say, the legislative process are extremely complex there is no reason amazing ways to make money that an outsider armed with a pot of strong coffee amazing ways to make money and a copy of erskine may cannot master the minutiae. Machine-learning experts will not necessarily include some (and certainly not all) of those tasked with operating systems of artificial administration; they could well form a separate and superior caste. [xviii] third, the phenomenon of automation bias is now well recognised: individuals taking decisions on the basis of recommendations produced by amazing ways to make money a computer are more likely to follow the recommendations than amazing ways to make money to exercise independent judgment. [xix] even if humans are kept ‘in the loop’ in theory, they may nonetheless become reliant in practice on technologies which, of course, they may not even understand. Fourth, the privacy implications of acquiring the information to feed into amazing ways to make money systems of artificial administration are obvious [xx] and, on some views, deeply troubling. [xxi]

Weighing up risks and rewards requires governmental decision-makers to engage in a difficult balancing act, to profit from the efficiency and accuracy gains of artificial amazing ways to make money administration whilst avoiding or minimising the potential costs of sophisticated amazing ways to make money information technology systems. Human are hardly powerless in this balancing act. At both the collection and analysis stage choices have to amazing ways to make money be made by humans about the framework for the use amazing ways to make money of big data and artificial administration. [xxii] there are “design choices” and they are “reflective of normative values”, [xxiii] as data is “never raw but always cooked to some recipe by chefs amazing ways to make money embedded within institutions that have certain aspirations and goals and amazing ways to make money operate within wider frameworks”. [xxiv]

Artificial administration does not stand alone: these technologies fall to be woven into existing norms of amazing ways to make money administrative law and administrative justice; they may, too, disrupt these norms. The norms of administrative law and administrative justice are not amazing ways to make money carved on tablets of stone. Just as they have evolved over the centuries they may amazing ways to make money well evolve again. Indeed, they may evolve in response to and in light of amazing ways to make money new norms, introduced by artificial administration. Even within governmental decision-making structures, the effects of artificial administration are likely to be uncertain. Managers can introduce technological modes of decision-making but are unlikely to be able to control the amazing ways to make money exercise of these modes by front-line decision-makers, who “can use information about that process available in the system amazing ways to make money to learn more about the process” and maybe even “learn more than their manager”. [xxv]

There is thus a serious question as to whose norms amazing ways to make money will ultimately triumph, or at least, shape the other more than they are shaped by it amazing ways to make money – to put the point provocatively, those of the lawyers, or those of the technologists? Make no mistake about the potential for a clash of amazing ways to make money value systems. Central to administrative law and administrative justice are norms of amazing ways to make money justification, transparency and intelligibility. [xxvi] in recent decades, courts and commentators alike have emphasised the virtues of a amazing ways to make money culture of justification in governmental decision-making, and there has been a strong focus on “the justice inherent in decision making” [xxvii] and “those qualities of a decision process that provide arguments for amazing ways to make money the acceptability of its decisions”. [xxviii] by contrast, silicon valley has been dominated by believers in solutionism; [xxix] notwithstanding the “philosophically tortuous” relationship between data and knowledge, [xxx] “big data is portrayed as a gradual evolution of the amazing ways to make money possibilities that now exist to interconnect different data sources situated amazing ways to make money on multiple geographical scales, and to process and analyse the hence generated data in amazing ways to make money increasingly automated ways”. [xxxi] causal relationships between individuals and events – central to governmental decision-making – are displaced or replaced by reliance on correlation:

This is a world where massive amounts of data and amazing ways to make money applied mathematics replace every other tool that might be brought amazing ways to make money to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity. With enough data, the numbers speak for themselves. [xxxii]

[iii] karen yeung, “algorithmic regulation: A critical interrogation” (2018) 12 regulation & governance 505, at p. 505. See similarly paul dourish, “algorithms and their others: algorithmic culture in context” (2016) (july-december) big data & society 1; karen yeung, “‘hypernudge’: big data as a mode of regulation by design” (2017) (20) information, communication & society 118, at p. 119.

[ix] my children’s school has the following admission criteria: “1. Children in care, also known as looked after children (LAC). 2. Children living in the catchment area with a sibling at amazing ways to make money the school at the time of admission. 3. Children living in the catchment area. 4. Children living outside the catchment area who have a sibling amazing ways to make money at the school at the time of the admission. 5. Children living outside the catchment area who have been unable amazing ways to make money to gain a place at their catchment area school because amazing ways to make money of oversubscription. 6. Children living outside the catchment area, but nearest the school measured by a straight line. In cases of equal merit in each set of criteria, priority will go to children living nearest the school as amazing ways to make money measured by a straight line”.

[xii] for example, the canada border services agency “uses a scenario based targeting (SBT) system to identify potential security threats, using algorithms to process large volumes of personal information (such as age, gender, nationality, and travel routes) to profile individuals”. The citizen lab, bots at the gate: A human rights analysis of automated decision-making in canada’s immigration and refugee system (toronto, university of toronto, 2018), at p. 21.

[xxv] andrew burton-jones, “what have we learned from the smart machine?” (2014) 24 information and organization 71, at p. 77. See e.G. Jennifer raso, “unity in the eye of the beholder? Reasons for decision in theory and practice”, public law conference 2016, at p. 12. Though this can lead in turn to reactions from management, which is “likely to turn to another tactic – leveraging the informating potential of an IT not for learning amazing ways to make money and improvement but for control and enforcement”. Id.

[xxxii] chris anderson, “end of theory: the data deluge makes the scientific method obsolete”, wired magazine, june 23, 2008. See also viktor mayer-schonberger and kenneth cukier, big data: the essential guide to work, life and learning in the age of insight (john murray, london, 2013), at pp. 8-9: “the era of big data challenges the way we live amazing ways to make money and interact with the world. Most strikingly, society will need to shed some of its obsession for amazing ways to make money causality in exchange for simple correlations: not knowing why but only what. This overturns centuries of established practices and challenges our most amazing ways to make money basic understanding of how to make decisions and comprehend reality”.

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