Socializing Data - Specialized Research AI at MIT

Socializing Data

Diane Coyle

Will the proliferation of data enable AI to deliver progress? An ever-growing swath
of life is available as digitally captured and stored data records. Effective govern-
ment, business management, and even personal life are increasingly suggested to
be a matter of using AI to interpret and act on the data. This optimism should be
tempered with caution. Data cannot capture much of the richness of life, and while
AI has great potential for beneficial uses, its delivery of progress in any human sense
will depend on not using all the data that can be collected. Moreover, the more dig-
ital technology rewires society, creating opportunities for the use of big data and AI,
the greater the need for trust and human deliberation.

D ata have always been important for government and policy. Statistics are,

as the name suggests, categorized data useful for states.1 States have col-
lected and collated data for centuries, not least for the purposes of taxa-
tion. Censuses too are ancient, defining the boundaries of power, though they are
likely to be replaced by other government-collected data sets about individuals.2
The purpose of governmental measurement is to create conceptual order, to clas-
sify the vast array of possible data points into meaningful categories, enabling bet-
ter decisions. Over the quarter-millennium of modern economic growth, the scope
of data collection and processing into statistics has become increasingly extensive.
In Seeing like a State (1998), political scientist James Scott argues that modern
states classify reality to improve the legibility of what they govern, to better control
it. He writes: “Legibility implies a viewer whose place is central and whose vision
is synoptic. . . . This privileged vantage point is typical of all institutional settings
where command and control of complex human activities is paramount.”3 Many
of his examples of states bending reality into order concern economic activities
such as forestry or agriculture, with reality conforming increasingly to the clas-
sifications devised to understand it. There is a feedback loop whereby statistics
collect and classify data points found in the wild, then subsequently influence ac-
tivities and shape reality over time, so that future data will be more likely to fit into
the predefined categories. This has been described by statistician André Vanoli
as “the dialectic of appearance and reality.”4 Or as historian Theodore Porter put
it, “The quantitative technologies used to investigate social and economic life al-
ways work best if the world they aim to describe can be remade in their image.”5

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For example, the principal measure of economic progress since the early 1940s
has been gross domestic product (GDP).6 Governments gear their policies toward
increasing GDP, and people duly respond to the incentives created by policies
such as tax breaks, subsidies, public infrastructure investment, or cheaper meals
out.7 Disappointing statistics can topple governments, as they did with the UK La-
bour government of the late 1970s, paving the way for the Thatcherite revolution.
GDP has not been a terrible metric for progress: compared with previous genera-
tions, our living standards are without doubt higher. We have better health, more
leisure, more comfortable homes, and the convenience of many new technolo-
gies. Yet even at the dawn of GDP’s invention, some realities had to be bent to fit
the statistical framework. Some were rendered invisible, defined as being outside
“the economy,” such as household work and nature. Without nature, there is no
economy and yet the consequences for sustainability of this fateful definitional
choice are becoming all too clear, and the progress we thought we had is at least
partly illusory.

Reality and the statistical picture also diverge when reality is changing. As stat-
istician Alain Desrosières has written, “In its innovative phase, industry rebels
against statistics because, by definition, innovation distinguishes, differentiates
and combines resources in an unexpected way. Faced with these ‘anomalies,’ the
statistician does not know what to do.”8 At present, for official statisticians, life is
one damned anomaly after another. For just as agriculture’s share was overtaken
by manufacturing in the industrial revolution, the material economy is smaller
now relative to the dematerializing economy of digitally enabled services.9 The
statistical categories no longer fit well. Paradoxically, in the economy of ever more
data, it is proving increasingly difficult to bring informational order, for the state
to gain that desired legibility.

T his is a paradox because the promise of big data and its use in AI has in-

spired renewed visions inside government of enhanced legibility. Such vi-
sions are not new. From the late 1950s onward, computers have seemed to
promise a clearer, synoptic understanding of society.10 One ambitious 1970s proj-
ect was Project Cybersyn in Salvador Allende’s Chile, administered by cyberneti-
cist Stafford Beer, which was intended to implement an efficiently planned econ-
omy.11 A similar vision of data-enabled, improved legibility has revived in the big
data digital era. On the left of UK politics it found expression as “fully automat-
ed luxury communism.”12 In the UK Conservative government elected in 2019, it
took physical shape as a control room at the heart of government, and a UK Strate-
gic Command contract with tech firm Improbable to build a “digital twin,” a sim-
ulation of the whole of Britain.13 The fact that both ends of the political spectrum
envision data-driven efficiency suggests a big data rerun of the 1930s socialist cal-
culation debate.14

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151 (2) Spring 2022Diane Coyle

The thing that is seen in seeing rooms of these kinds–physical rooms with dis-
plays of information to inform decision-makers–is ordered data. There is a kind
of commodity fetishism regarding the mechanics of displaying the data. The tech-
nology of data has long been glamorous, arousing intense public and political in-
terest. The great exhibitions and world’s fairs of the nineteenth and early twenti-
eth centuries had popular displays of high-tech data management artifacts such as
filing cabinets and cards.15 The same is true of digital technology and Silicon Val-
ley, which have inspired numerous nonfictional and fictional accounts. Databases
have changed form over time as physical hardware and computational power have
evolved, so the embodiment and the usability (searchability) of data have not
been constant, and the technologies of display combined with the classification
and conceptual framework organizing the data affect the way decision-makers
understand the world. The emphasis on the synoptic view–through a computer
simulation, through a room kitted out with the latest screens and data feeds–is
an assertion of political control through greater legibility. Then–UK government
adviser Dominic Cummings presented it as a matter of public interest:

There is very powerful feedback between: a) creating dynamic tools to see complex
systems deeper (to see inside, see across time, and see across possibilities), thus mak-
ing it easier to work with reliable knowledge and interactive quantitative models,
semi-automating error-correction etc, and b) the potential for big improvements in
the performance of political and government decision-making.16

In other words, the claim is that data science and AI, suitably embodied in a seeing
room, can be the vehicle for delivering “high performance” by government.

However, the emphasis is on the technologies of cognition and management,
rather than the construction of the data going into the process, or the assessment
of what constitutes improvement. The implicit assumption is that this is a determi-
nation made by the center, by those in the seeing room. This assumption is exactly
why an ambition to use data for progress can embed biases, create ambiguity about
accountabilities, or appear to be part of the surveillance society.17 There is certain-
ly nothing new about state attempts to exercise comprehensive surveillance. East
Germany’s Stasi offers an extreme recent example. Its data took analog form with a
technological infrastructure turning data into seeing: card records with a bespoke
filing cabinet technology, photographs, steam irons for opening mail, tape record-
ings, and computers. Despite the existence of formal regulations controlling ac-
cess to this data, a citizen of the former German Democratic Republic was a gläserne
Mensch, a transparent being. Perhaps we are all becoming transparent now. Digital
technology makes the amassing of data records trivially cheap and easy by compar-
ison with the 1980s, and security agencies have been doing this at scale.

Big tech companies, not just security agencies, have been amassing the biggest
and best databases and the know-how to use them for a purpose. Their purpose is

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Dædalus, the Journal of the American Academy of Arts & SciencesSocializing Data

profit, rather than public good, and their market power ensures they do not need
to serve the interests of their users or the public in general. Big Tech’s success vis-
à-vis state power is amply evident in the erosion of national tax bases as ever more
economic activity goes online. It is not clear how much governments can limit
this.18 As being able to raise tax revenues is a core state function, there can be lit-
tle question about the power of the biggest digital companies. If the synoptic view
of what is happening anywhere is available to anybody, it is Google or Facebook.
They, not officials or politicians, are collecting, categorizing, and using the new
proliferation of data.

As long as data are seen as individual property amenable to normal market ex-
change, that will continue to be the case, despite recent regulatory moves in sev-
eral jurisdictions to enforce some data sharing by the tech giants. The reason big
tech companies have been able to acquire their power is the prevailing concep-
tual framework, crystallized into law, for understanding data as property. Rather
than the appreciation that data reflect constructed categories, a particular lens or
framework measuring and shaping reality, data are seen as the collection of nat-
ural objects: the classifications codified and programmed into data feeds just are
what they are. These constructed data records are then subject to legal rules of
ownership. Data are presumed to be transferred and owned by corporations as
soon as the user of a service has accepted its terms and conditions.

The consequences of this property rights concept applied to data, or informa-
tion, illuminate why it is so pernicious. For example, John Deere and General Mo-
tors (as corporate persons) have claimed in U.S. copyright courts that farmers or
drivers who thought they were purchasing their vehicles do not in fact own them
and have no right to repair them. The company’s reasoning is that a tractor is no
longer mainly a metal object whose ownership as a piece of property is transferred
from John Deere to the farmer, but rather an intangible data-fed software service
licensed from the company, which just happens to have a tractor attached.19 Indeed,
screens with data about weather, soil conditions, and seed flow proliferate inside
tractor cabins and feed into the diagnostic software installed by the manufacturer,
which provides information to enable decisions raising crop yields. The John Deere
claim to ownership of the intangible dominates the farmer’s claim to ownership of
the physical vehicle it is bundled with. To date, the courts have been largely sympa-
thetic to the corporations and to the strong ownership claims made by Amazon over
e-books, by makers of games on consoles, as well as by vehicle manufacturers.

One response to such corporate ownership over data and data processing claims
has been the demand for corporations to pay for “data as labor.”20 With this, each
data point an online business collects from users’ activities would be rewarded
with a small financial payment. However, as economist Zoë Hitzig and colleagues
point out, this remedy also considers data as a transferrable, individual item of
property, and implicitly as a natural object “given” by the underlying reality.21

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151 (2) Spring 2022Diane Coyle

The data-as-property perspective assumes data are an object in the world, with
an independent reality, differing from other givens only in being intangible. Yet
not only are data nonrival (their use does not deplete them so many people can use
them), but they are also inherently relational. Data are social. Even when it comes
to data that are seemingly ultrapersonal–for example, that I passed a particular
facial recognition camera at a given moment–the information content and use-
fulness of the data are always relational.22 A facial image needs to be compared
with a police database. Even then its utility for the purpose of detecting suspected
criminals depends on the quality of the training data used to build the machine
learning algorithm, including its biases, the product of a long history of unequal
social relations. The relational character of data means they are both constructed
by social relations and a collective resource for which market exchange will not be
the best form of organization.23 Indeed, this is why there are few markets for data;
where data are sold–for example, by credit rating agencies–the market is gener-
ally thin, with no standardized, posted prices. The use value of data–their infor-
mation content enabling decisions to be made–is highly heterogeneous.

T hat markets are a poor organizational model for the optimal societal use

of data is Economics 101. Does that make government the right vehi-
cle to use big data and AI for the public good? Can and should govern-
ments aim to beat big tech at the seeing game? The promise of automating policy
through seeing rooms and use of AI is greater efficiency and, potentially, better
outcomes. Yet there is increasing use of algorithmic processes in arenas in which
decisions could have a large impact on people’s lives, such as criminal justice or
social security.

Much of the literature on the informational basis of organizations focuses on
complexity as the constraint on effective information-processing, given an objec-
tive function.24 Automation is superior in routine contexts: more reliable, more
accurate, faster, and cheaper. What is more, machines deal more effectively with
data complexity than humans do, given our cognitive limitations. This is a key ad-
vantage of machine learning systems as the data environment grows more com-
plex. The system is better able than any human to discern patterns and statisti-
cal relationships in the data, and indeed the more complex the environment, the
greater the AI advantage over human-scale methods. However, whenever there is
uncertainty, the advantage tips back to humans. The more frequently the environ-
ment changes in unexpected ways, or the more dramatic the scale of change, the
greater the benefits of applying human judgment. The statistical relationships on
which automated decision rules are based will break down in such circumstances
(in economics this is known as the Lucas critique).25 The selection of a machine
or human to make decisions is generally presented as a trade-off. However, it has
long been argued, or hoped, that AI can improve the terms of this trade-off.26

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There are several reasons to doubt this. One is the well-known issue of bias
in training data sets, the inevitable product of unfair societies in the way data are
classified, constructed, collected, and ordered.27 Any existing data set reflects
both the classification framework used and the way that framework has shaped
the underlying reality over time (that is, André Vanoli’s dialectic referred to ear-
lier). The data science community has become alert to this challenge and many
researchers are actively working on overcoming the inevitable problems raised by
data bias. But bias is not the only issue.

Another less well-recognized issue (at least in the policy world) is that deci-
sions based on machine learning need an explicitly coded objective function. Yet
in many areas of human decision-making–particularly the most sensitive, such
as justice or welfare–objectives are often left deliberately implicit. Politics in de-
mocracies requires compromise on high-level issues so that low-level actions can
be taken. These “incompletely theorized agreements” are not amenable to be-
ing encoded in machine learning (ML) systems, in which precision about the re-
ward function is needed even if conflicting objectives are combined with different
weights.28 The further deployment of ML in applied policy practice may require
more explicit statements of objectives or trade-offs, which will be challenging in
any domain where people’s views diverge.29 There could be very many of these,
even in policy areas that seem straightforward. For example, how should public
housing be allocated? There has been a pendulum swinging over time between
allocation based on need and allocation based on likelihood to pay rent. These are
conflicting objectives, and yet many of the same families would be housed under
either criterion.

The extensive discussions of value alignment in the AI literature tussle with
how to combine the brutally consequentialist nature of AI with ambiguity or con-
flicts about values. Given any objective or reward function, ML systems will game
their targets far more effectively than any bureaucrat ever did. All the critiques of
target setting in the public management literature, on the basis that officials game
these for their personal objectives, apply with extra force to systems automating
target delivery. This has led to concerns–albeit overstated–about runaway out-
comes far from what the human operators of the system wanted.30 One possible
avenue is inverse reinforcement learning–that is, when ML systems try to infer
what they should optimize for–which can accommodate uncertainty about the
objective, but takes the existing environment as the desired state of affairs.31 Polit-
ical theorist and ethicist Iason Gabriel rightly emphasizes the need for legitimate
societal processes to enable value alignment; but we do not have these yet.32

M arket arrangements based on the concept of private property transac-

tions are inappropriate for data, given their relational characteristics.
In economic terms, there are large externalities, whereby one individ-

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151 (2) Spring 2022Diane Coyle

ual’s provision of data can have either negative (loss of privacy) or positive (use-
ful information) implications for other people.33 Rather than being considered as
property amenable to market exchange, data instead need to be subject to gover-
nance arrangements of permitted access and use. Online, the offline norms of so-
ciologist Georg Simmel’s concept of “privacy in public” do not exist.34 This con-
cept refers to the norms people adopt limiting what they know about each other
in their different roles. Even publicly available information (such as where some-
body lives) is not made known in a specific context (such as the marking of an
exam paper by their lecturer). These voluntary informational restraints and social
relations of trust play an important role in sustaining desirable outcomes such as
fairness, privacy, or self-esteem.35 Similar norms do not exist online. Big tech joins
up too many data about each of us. People can reasonably be concerned about
government seeing rooms doing the same.

At the same time, some joining up of data for some uses could without question
lead to improved outcomes for individuals. So we have ended up in the worst of
all worlds: a “surveillance state” or “surveillance economy” in which valid priva-
cy concerns about certain data uses prevent other uses of “personal” data for col-
lective and individual good. Consider the successful argument that governments
should not use data from COVID-19 apps to trace individuals’ contacts during the
pandemic, leading almost all governments to adopt the Google and Apple appli-
cation programming interfaces (API) with privacy enforced, all the while as per-
sonal liberty was infringed through lockdowns tougher than would have been
needed with effective contact tracing. Meanwhile, governments and researchers
have been able to use big data and machine learning to inform policies during the
pandemic but could have done much more to avert unequal health outcomes with
linked data about individuals’ health status, location, employment, ethnicity, and
housing.

The debate about privacy has become overly focused on individual consent
and data protection. It should be a debate about social norms and what is accept-
able in different contexts, translated into rights of access and use for limited, spe-
cific purposes.36 In both the commercial and the public sphere, the promise of AI
for decision-making will not be realized unless the kind of information norms
that operate offline are created online. The control of access and use is not just a
technical issue but a social and political one.

A s the world gets both more complex and more uncertain, big data and AI

will need to socialize in another way, by combining with human judg-
ment more often. The experiences of 2020, or the impact of extreme
climate-related events from California burning to Texas freezing, are suggestive
of the prospect that “radical uncertainty” will characterize the twenty-first centu-
ry.37 Anybody with any knowledge of forecasting (no matter how small or big the

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Dædalus, the Journal of the American Academy of Arts & SciencesSocializing Data

data set) will know that uncertainty about future outcomes multiplies over time.
“Further computational power doesn’t help you in this instance, because uncer-
tainty dominates. Reducing model uncertainty requires exponentially greater
computation.”38

As radical uncertainty increases, the digital transformation is meanwhile
expanding the domain of human judgment and trust. Institutional econom-
ics has generally considered two modes of organization: the market, in which
price allocates resources, and the hierarchy, in which authority and contract
apply. But neither price nor authority function well as allocation mechanisms
when knowledge-based assets are important in production.39 That the market
is a poor vehicle for the use and provision of public goods such as knowledge
is a standard piece of economic theory. Similarly, a large body of management
literature notes that knowledge is hoarded at the top of hierarchical organiza-
tions, which are consequently good at routine activities but not at adaptation or
innovation.

Trust is a more effective mechanism than either market exchange or com-
mand-and-control for coordinating knowledge-intensive activities, both within
organizations and between them. The economics literature has long recognized
the challenge of asymmetric information and tacit knowledge.40 In the digital
knowledge economy, tacit or hard-to-codify knowledge is increasingly impor-
tant. For example, the advantage of high productivity firms over others is encap-
sulated in the concept of their “organizational capital.” It reflects their ability to
manage a complex and uncertain environment, make use of data and software,
and employ skilled people who have the authority to make decisions. The gap be-
tween firms with high organizational capital and others is growing.41 Trust net-
works or communities need to join market and hierarchy as a standard organiza-
tional form. Trust is also essential when questions of accountability are blurred,
as is the case with hard-to-audit automated-decision systems; the alternative is
costly insurance and/or litigation to assign responsibility for outcomes.

The desire for the seeing room view rests on an assumption about the possi-
bility of classifying the world and ordering data as statistical inputs for that syn-
optic view. Big data does not help overcome the limitations of having to impose
a classification: AI techniques involve the aggregation of the vast quantities of
raw, irregular, often by-product data into lower dimensional constructs. The ma-
chine is doing the classification in ways not legible to humans, but it is doing so
nonetheless. But there is much useful knowledge that is tacit rather than explicit
and therefore impossible to classify. There is much that is highly locally heteroge-
nous such that population averages mislead. Nor does having big data and AI over-
come the inevitable clash of values or interests that arise in any specific decision-
making context. Algorithms cannot adjudicate trade-offs and conflicts; only hu-
mans can do so with any legitimacy.

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151 (2) Spring 2022Diane Coyle

We should think of machines and humans as complements. As societal com-
plexity and uncertainty increase, and as the zone of automated decisions expands,
this requires more use of human judgment, not less. Otherwise, we will end up
with Scott’s disasters of modernism, fully automated. Practical, tacit, improvisa-
tional knowledge and informal decision-making processes are always essential for
actions to deliver better outcomes locally: even setting aside the point that peo-
ple might have different and irreconcilable views about what constitutes “better,”
there are limits to classifiable knowledge, and limits to data.

T he use of AI in society must reflect the social nature of data. Although big

data offers great potential for progress, any data set is a limited, encod-
ed representation of reality, embedding biases and assumptions, and ig-
noring information that cannot be codified. A synoptic view of society from a
data-enabled seeing room is impossible because no authority can stand outside
the reality their decisions will in fact shape. For the promise of AI to be realized,
three things are needed: new norms (as well as laws and technologies) governing
access and use of data, embedding offline limits online; effective organizations
empowering human judgment alongside automated decisions; and legitimate
processes to shape the collective decisions being coded into AI. Adopting AI first
and reflecting on these needs later is the wrong way to go about socializing data.

author’s note

My thanks to the following colleagues for their helpful comments on an early draft:
Vasco Carvalho, Verity Harding, Bill Janeway, Michael Kenny, Neil Lawrence, and
Claire Melamed. I am entirely responsible for any errors or infelicities. Thanks also
to Annabel Manley for research assistance.

about the author

Diane Coyle is the Bennett Professor of Public Policy at the University of Cam-
bridge. She is the author of Cogs and Monsters: What Economics Is, and What It Should Be
(2021), Markets, State, and People: Economics for Public Policy (2020), GDP: A Brief but Af-
fectionate History (2014), and The Economics of Enough (2011).

endnotes

1 Theodore M. Porter, Trust in Numbers: The Pursuit of Objectivity in Science and Public Life

(Princeton, N.J.: Princeton University Press, 1995).

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2 Andrew Whitby, The Sum of the People: How the Census Has Shaped Nations, from the Ancient

World to the Modern Age (New York: Hachette, 2020).

3 James C. Scott, Seeing like a State: How Certain Schemes to Improve the Human Condition Have

Failed (New Haven, Conn.: Yale University Press, 2020), 79.

4 André Vanoli, A History of National Accounting (Amsterdam: IOS Press, 2005), 158.

5 Porter, Trust in Numbers, 43.

6 Diane Coyle, GDP: A Brief but Affectionate History (Princeton, N.J.: Princeton University

Press, 2014).

7 Philipp Lepenies, The Power of a Single Number: A Political History of GDP (New York: Co-
lumbia University Press, 2016); and “Get a Discount with the Eat Out to Help Out
Scheme,” Gov.uk, July 15, 2020, updated September 1, 2020, https://www.gov.uk/
guidance/get-a-discount-with-the-eat-out-to-help-out-scheme (accessed October 29,
2020).

8 Alain Desrosières, The Politics of Large Numbers: A History of Statistical Reasoning (Cambridge,

Mass.: Harvard University Press, 1998), 252.

9 Diane Coyle, The Weightless World (Cambridge, Mass.: MIT Press, 1997).
10 Jill Lepore, If Then: How One Data Company Invented the Future (New York: Hachette, 2020).
11 Eden Medina, Cybernetic Revolutionaries: Technology and Politics in Allende’s Chile (Cam-

bridge, Mass.: MIT Press, 2014).

12 Aaron Bastani, Fully Automated Luxury Communism (New York: Verso Books, 2019).
13 “Covid-19 Crisis Accelerates UK Military’s Push into Virtual War Gaming,” Financial Times,
August 19, 2020, https://www.ft.com/content/ab767ccf-650e-4afb-9f72-2cc84efa0708.
14 Diane Coyle and Stephanie Diepeveen, “Creating and Governing Value from Data”

(2021), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3973034.

15 Shannon Mattern, “The Spectacle of Data: A Century of Fairs, Fiches, and Fantasies,”

Theory, Culture & Society 37 (7–8) (2020): 133–155.

16 Dominic Cummings, “On the Referendum #33: High Performance Government, ‘Cog-
nitive Technologies,’ Michael Nielsen, Bret Victor, and ‘Seeing Rooms,’” Dominic
Cummings’s Blog, June 26, 2019, https://dominiccummings.com/2019/06/26/on-the
-referendum-33-high-performance-government-cognitive-technologies-michael
-nielsen-bret-victor-seeing-rooms/.

17 Shoshana Zuboff, The Age of Surveillance Capitalism: The Fight for a Human Future at the New

Frontier of Power (London: Profile Books, 2019).

18 Organisation for Economic Co-operation and Development, “Statement on a Two-Pillar
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my,” OECD/G20 Base Erosion and Profit Shifting Project, July 1, 2021, https://www
.oecd.org/tax/beps/statement-on-a-two-pillar-solution-to-address-the-tax-challenges
-arising-from-the-digitalisation-of-the-economy-july-2021.pdf.

19 Darin Bartholomew, “Long Comment Regarding a Proposed Exemption under 17 U.S.C.
1201,” https://copyright.gov/1201/2015/comments-032715/class%2021/John_Deere_
Class21_1201_2014.pdf.

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20 Imanol Arrieta-Ibarra, Leonard Goff, Diego Jiménez-Hernández, et al., “Should We
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21 Zoë Hitzig, Lily Hu, and Salomé Viljoen, “The Technological Politics of Mechanism De-
sign,” University of Chicago Law Review 87 (1) (2019), https://ssrn.com/abstract=3638585.
22 Diane Coyle, Stephanie Diepeveen, Julia Wdowin, et al., The Value of Data: Policy Implica –
tions–Main Report (Cambridge: Bennett Institute for Public Policy Research, 2020),
https://www.bennettinstitute.cam.ac.uk/publications/value-data-policy-implications/.
23 Salomé Viljoen, “Democratic Data: A Relational Theory for Data Governance,” Yale Law

Journal 131 (2020), https://ssrn.com/abstract=3727562.

24 Herbert A. Simon, “A Behavioral Model of Rational Choice,” The Quarterly Journal of Eco-

nomics 69 (1) (1955): 99–118.

25 Robert E. Lucas, “Econometric Policy Evaluation: A Critique,” Carnegie-Rochester Confer-

ence Series on Public Policy 1 (1) (1976): 19–46.

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nal of Operational Research 18 (1984): 293–303.

27 Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al., “A Survey on Bias and Fair-
ness in Machine Learning,” arXiv (2019), https://arXiv:1908.09635v2; and Xavier Fer-
rer, Tom van Nuenen, José M. Such, et al., “Bias and Discrimination in AI: A Cross-
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28 Cass R. Sunstein, “Incompletely Theorized Agreements,” Harvard Law Review 108 (7)

(1995): 1733–1772.

29 Diane Coyle and Adrian Weller, “‘Explaining’ Machine Learning Reveals Policy Chal-

lenges,” Science 368 (6498) (2020): 1433–1434.

30 See, for example, Thomas Arnold, Daniel Kasenberg, and Matthias Scheutz, “Value
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31 Stuart Russell, Human Compatible: Artificial Intelligence and the Problem of Control (New York:

Penguin, 2019).

32 Gabriel, “Artificial Intelligence, Values, and Alignment.”
33 Coyle and Diepeveen, “Creating and Governing Value from Data.”
34 Georg Simmel, “The Sociology of Secrecy and of Secret Societies,” American Journal of So-

ciology 11 (4) (1906): 441–498.

35 Richard Warner and Robert H. Sloan, “The Self, the Stasi, and NSA: Privacy, Knowledge,
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(2016): 347.

36 Linnet Taylor, “The Ethics of Big Data as a Public Good: Which Public? Whose Good?”
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37 John Kay and Mervyn King, Radical Uncertainty: Decision-Making Beyond the Numbers (New

York: W. W. Norton & Company, 2020).

38 Neil Lawrence, “Future of AI 6. Discussion of ‘Superintelligence: Paths, Dangers, Strat-
egies,’” Inverseprobability blog, May 9, 2016, https://inverseprobability.com/2016/
05/09/machine-learning-futures-6.

39 Paul S. Adler, “Market, Hierarchy, and Trust: The Knowledge Economy and the Future
of Capitalism,” Organization Science 12 (2) (2001): 215–234, https://doi.org/10.1287/
orsc.12.2.215.10117.

40 See, for example, Sanford Grossman and Joseph E. Stiglitz, “Information and Compet-
itive Price Systems,” The American Economic Review 66 (2) (1976): 246–253; Bengt Hol-
strom, “The Firm as a Subeconomy,” Journal of Law, Economics and Organization 15 (1)
(1999): 74–102, https://doi.org/10.1093/jleo/15.1.74; and Luis Garicano and Esteban
Rossi-Hansberg, “Organization and Inequality in a Knowledge Economy,” The Quarterly
Journal of Economics 121 (4) (2006): 1383–1435.

41 Lorin M. Hitt, Shinkyu Yang, and Erik Brynjolfsson, “Intangible Assets: Computers
and Organizational Capital,” Brookings Papers on Economic Activity 1 (2002): 137–181; and
Prasanna Tambe, Lorin Hitt, Daniel Rock, and Erik Brynjolfsson, “Digital Capital and
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reau of Economic Research, 2020).

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