AI for a Generative Economy: The Role of Intelligent Systems in Sustaining
Unalienated Labor, Environment, and Society
Ron Eglash (firstname.lastname@example.org), Professor, School of Information,
University of Michigan, 4389 North Quad, 105 S. State St., Ann Arbor, MI 48109-1285. ORCID#
Lionel P. Robert Jr.(email@example.com); Associate Professor, UM School of Information.
Audrey Bennett (firstname.lastname@example.org), Professor, UM School of Art and Design.
Kwame Porter Robinson (email@example.com), Graduate Student, UM School of Information.
Michael Lachney (firstname.lastname@example.org), Assistant Professor, Michigan State University, Department of
Counseling, Educational Psychology and Special Education.
ORCID# 0000- 0003-3310-8707
William Babbitt (email@example.com), Research Associate, Rensselaer, Department of Science and
Technology Studies. ORCID# 0000-0002-2684-4901
Extractive economies pull value from a system without restoring it. Unsustainable extraction of
ecological value includes over-fishing, clear-cut logging, etc. Extraction of labor value is similarly
objectionable: assembly line jobs for example increase the likelyhood of cardiovascular disease,
depression, suicide and other problems. Extraction of social value--vacuuming up online
personal information, commodification of the public sphere, and so on-- constitutes a third form.
But all three domains--ecological value, labor value, and social value--can thrive in unalienated
forms if we can create a future of work that replaces extraction with generative cycles. AI is a
key technology in developing these alternative economic forms. This paper describes some
initial experiments with African, African American, and Native American artisans who were
willing to experiment with the introduction of computational enhancements to their work.
Following our report on these initial results, we map out a vision for how AI could scale up labor
that sustains “heritage algorithms”, ecologically situated value chains and other hybrid forms
that prevent value alienation while flourishing from its robust circulation.
Keywords: human-machine collaboration; artisanal economy; generative justice; industrial
Questions such as “what jobs will remain after AI is sufficiently advanced” are implying a rather
passive stance. True, the potential disruptions created by AI can cause trepidation. But they are
also an opportunity for re-fashioning the future of work in ways that optimize environmental
sustainability, sustain enjoyable labor, and enhance the public sphere. In the experiments
described in this paper we investigate artisanal labor as the basis for such forms. Traditional
cultural forms of production such as Native American wood crafting often embody all three
domains: harvesting practices that are ecologically sustainable; labor practices that are deeply
satisfying; and social networks in which both equity and creative expression can flourish
(Ostrom and Ahn 2009; Corntassel 2012; Eglash 2016a,b; Liu et al 2018). If AI and related
automation technologies can help to “translate” these forms into modes of production that fit
contemporary needs and contexts, the social and environmental ills that were created by
centuries of mass production--the extractive economy--could be addressed, and a generative
economy--one in which value remains in an unalienated form; circulated rather than extracted--
could be achieved.
In this paper we will briefly review the problems created by mass production, and the principles
of generative economies in their traditional form. We then present some initial experiments with
what we might term “artisanal cyborgs” -- a synthesis between traditional work practices and
contemporary automation technologies. We conclude with a vision for how this research
trajectory could enable the development of computational forms that merge artificial intelligence
with a generative economy.
2. Social and environmental destruction in the mass production economy
Collaborative robots (“cobots”), where humans and robots work together side by side, are often
proposed as a potential solution to the fear of massive job losses due to automation; the
emphasis is on their ability to accomplish shared work goals (Colgate et al. 1996; Peshkin and
Colgate 1999; You et al. 2018). But these goals are typically those of the mass production
economy. Amplifying them with AI is not addressing the three problems that value extraction
a. Alienation of labor value: monotony, limited worker agency, and a failure to allow a sense of
pride in the fruits of our activity are typical of mass production. These are correlated with
cardiovascular disease (Karasek et al 1981); work-related depression (Michelsen and Bildt,
2003); suicide (Woo and Postolache 2011), and other disorders.
b. Alienation of ecological value: millions of tons of plastic are entering the ocean annually; and
heavy metals, pesticides, cleaning agents, organochlorides, and other toxins continue
increasingly contaminate land and air (Kannan 1991; Jambeck et al 2015; Coccia 2017). Global
warming; ocean acidification; and mass extinction (Vallero 2015; de Souza Machado, 2016;
Dirzo et al 2014) are all consequences of mass production in an extractive economy.
c. Alienation of social value: mass production creates a demand for mass consumption.
Increasingly AI is applied in the development of consumption accelerating techniques: adware,
spyware, targeted social media marketing, and so on. Rather than satisfying needs, purchases
in this “hedonic treadmill” increase buying aspirations (Chancellor and Lyubomirsky 2011).
Consumption-driven social media platforms are linked to loneliness and depression (Hunt et al
2018); focus onto extrinsic rather than intrinsic goals (Kasser and Ryan 1996); and a decrease
in academic achievement for consumption-obsessed youth (Bunce et al 2017).
A typical objection to proposals for a generative economy is that artisanal production--especially
that tied to ecologically sustainable sources-- is incapable of generating the massive streams of
consumer goods we currently produce. That is precisely the point: a generative economy,
empowered by AI and other automation forms, would be decreasing social alienation, and (in a
bi-directional, co-evolutionary process) decreasing consumer demand. In short: with more
meaningful forms of production comes less need to find meaning in consumption.
3. The principles of generative justice
The phrase “alienated labor value” comes from Marx (1844); to fully understand that concept,
we need to clarify the word “alienated”. Today that is interpreted as a psychological condition, “I
feel alienated”. But Marx was using the word to mean “something that has been taken from
you”. Marx and Engles had carefully read von Helmholtz, Carnot, Boltzmann and others in the
new science of thermodynamics (Bellamy and Burkett 2008), and accordingly reconceptualized
work in terms of “labor power”. Thus “alienation of labor value” was analogous to energy
transfer in their framework: from the original source of energy generation (people) to
somewhere else (capital). This labor value could then be stored (banks) and repurposed (capital
investments in machines). By moving extracted value to the communist state, a utopian society
would be born.
This vision for a top-down technocratic communism
utterly failed, creating poverty, human rights abuse
and environmental destruction to rival that of
capitalism (Graham 1993; Peterson 2019). That is
because both centralized communism and
corporate capitalism depend on the same
extractive modes of production (figure 1). Whether
the “owner” is state or corporate is irrelevant. The
self-generating source of value in Nature, Labor,
and Social expression is reduced to “resources”.
Note that at bottom, the self-generating character
of nature, labor and society is shown as a looping pipe. Nature attempts to sustain itself even if
overharvested; labor replenishes itself at home even if the factory drains it; society maintains
communication even if privacy is violated. But that value is extracted and carried off elsewhere.
In contrast, Indigeous societies (figure 2) keep value in unalienated forms: the network is all
pipe! Value generation is under control of the generators. A traditional artisan can take pride in
her craft; maintain respectful two-way exchanges with nature, and relish a social network of
solidarity and creative expression.
Figure 1: Extractive modes of
Figure 2 shows how Adinkra textile production in Ghana begins with Badie tree bark. Soaking
bark in water produces an anti-dysentery medication (“aduru”), and is available in the commons
to anyone. Further cooking the bark produces ink, which creates the adinkra symbols for
“sharing” (two crocodiles who have the same stomach need not fight over food), and “earth in
balance” (log spirals representing things in nature). Bark drained of its tannins is sent back to
nature as compost. The sacred forest is small, but maintains a “hot spot” of biodiversity that
feeds other areas, where the Badie tree grows. The value circulates in ways that are highly
productive but have little alienation.
4. Initial experiments in artisanal cyborgs
Figure 3 shows our experiment with Ghanaian textile makers using a batik wax-resist method.
The latex sponges they used were not compostable, so a trash heap of used sponges was
growing. We had already created simulations of adinkra symbols, and shown statistically
significant improvement for students learning math and computing through this indigenous
knowledge (Babbitt et al 2015). So it was a simple matter to extend that to a 3D printed mold in
which we grew mushroom-based foam. This merger of traditional artisanal practice and
computational modeling is only the first step. By replacing wood fires with solar heat to produce
inks and waxes; monitoring forests via GIS to prevent over-harvesting of Badie tree bark;
bringing in additional sustainable and locally owned plant harvests (e.g. the coconut husk
building materials in Lokko and Eglash 2017), a network of physical computing can both monitor
and optimize these generative systems, leveraging traditional sustainability and equity with
Figure 3: from Indigenous geometric form, to code, to 3D print, to mycofoam stamp
Further experiments yielded similar opportunities (Eglash et al. forthcoming; Lachney et al.
forthcoming). In the case of African American braiding salons, these “heritage algorithms” were
used for STEM education (Lachney et al 2018), and later rendered by 3D printing into store
mannequin heads (figure 4). However the 2D patterns produced by students did not map neatly
onto the heads; AI is currently being investigated as a possible means to automate what our
postdocs have been doing by human expertise. Finally, in the case of Native American artisans,
their use of parabolic arcs (wigwaams, canoe ribs, etc.) provided the heritage algorithm. But
unlike the African American case, Native American students insisted on hand crafting the fibers
to board with holes determined by the simulation they made. The African American hair stylists
went on to investigate healthier hair products, testing locally produced plant products with digital
pH meters. The Native American group extended the experiments into the use of arcs to create
an aquaponics system, also with computational monitoring of water, fish and plant conditions. In
both cases, like the Ghanaian textiles, the potential clearly exists for using AI to help the next
generation sustain and innovate with heritage algorithms, bringing value back to those who
generate it, and building those computation links out into generative network with reciprocal
relations to nature, labor and society.
Figure 4: African American and Native American students merging traditional and
Gombolay et al (2015) conducted experiments on automating mass production. They
reported that workers preferred to ceed task control to automated machines. Our experiments
with African American, African, and Native American artisans in human-machine collaboration
traditions (Eglash et al. forthcoming; Lachney et al. forthcoming) show distinctly different
preferences depending on the context.
Artisanal labor, in a careful synthesis with AI, robotics and other automation technologies, could
potentially help to democratize the economy, improve environmental sustainability, and allow
lifeways that find more meaning and satisfaction in creative production than mindless
consumption. AI has the potential to aid us in replacing extraction with a generative network in
which value circulates in unalienated forms: hence the need for “artisanal cyborgs” that can
scale up these generative alternatives.
Acknowledgement: The authors would like to acknowledge NSF grants DRL-1640014 and DGE-
0947980 in support of this work.
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