@article {EganadelSol2020,
	title = {The Future of Work in Developing Economies},
	journal = {MIT Sloan Management Review},
	volume = {61},
	number = {2},
	year = {2020},
	pages = {1{\textendash}3},
	abstract = {<p>Much has been written about the rise of automation in developed countries. Economists have been busily creating models seeking to quantify the likely impact of automation on employment. However, far less has been written about the potential effects on work in developing nations. This is surprising, given that automation may be especially troublesome for developing economies. Here, del Sol and Joyce examine the effects of large-scale automation on workers in developing countries.</p>},
	keywords = {Armenia, Asia, Austria, automation, Bolivia, Business And Economics{\textendash}Management, China, Developing countries{\textendash}LDCs, Employment, future, Georgia (country), Ghana, Impact analysis, Kenya, Kuala Lumpur Malaysia, Laos, Republic of North Macedonia, South Korea, Sri Lanka, United States{\textendash}US, Vietnam, Workers},
	issn = {15329194},
	author = {Egana del Sol, Pablo and Joyce, Connor and Del Sol, Pablo Ega{\~n}a and Joyce, Connor}
}
@article {Clifton2020,
	title = {When machines think for us: The consequences for work and place},
	journal = {Cambridge Journal of Regions, Economy and Society},
	volume = {13},
	number = {1},
	year = {2020},
	pages = {3{\textendash}23},
	abstract = {The relationship between technology and work, and concerns about the displacement effects of technology and the organisation of work, have a long history. The last decade has seen the proliferation of academic papers, consultancy reports and news articles about the possible effects of Artificial Intelligence (AI) on work-creating visions of both utopian and dystopian workplace futures. AI has the potential to transform the demand for labour, the nature of work and operational infrastructure by solving complex problems with high efficiency and speed. However, despite hundreds of reports and studies, AI remains an enigma, a newly emerging technology, and its rate of adoption and implications for the structure of work are still only beginning to be understood. The current anxiety about labour displacement anticipates the growth and direct use of AI. Yet, in many ways, at present AI is likely being overestimated in terms of impact. Still, an increasing body of research argues the consequences for work will be highly uneven and depend on a range of factors, including place, economic activity, business culture, education levels and gender, among others. We appraise the history and the blurry boundaries around the definitions of AI. We explore the debates around the extent of job augmentation, substitution, destruction and displacement by examining the empirical basis of claims, rather than mere projections. Explorations of corporate reactions to the prospects of AI penetration, and the role of consultancies in prodding firms to embrace the technology, represent another perspective onto our inquiry. We conclude by exploring the impacts of AI changes in the quantity and quality of labour on a range of social, geographic and governmental outcomes.},
	keywords = {artificial intelligence, automation, bias in machine learning, geography of technology, job displacement and growth},
	issn = {17521386},
	doi = {10.1093/cjres/rsaa004},
	author = {Clifton, Judith and Clifton, Judith and Glasmeier, Amy and Gray, Mia}
}
@article {2019,
	title = {Global commission on the future of work},
	year = {2019},
	institution = {International Labour Organization },
	keywords = {government reports},
	url = {https://www.ilo.org/global/topics/future-of-work/WCMS_569528/lang--en/index.htm},
	author = {Cyril  Ramaphosa and Stefan Lofven}
}
@article {2018,
	title = {Artificial intelligence, jobs,  inequality and productivity:  Does aggregate demand matter},
	number = {ISSN 1871-9872},
	year = {2018},
	publisher = {Maastricht University},
	keywords = {artificial intelligence, economics of automation, growth theory, innovation, labour demand, productivity, technology},
	url = {http://ftp.iza.org/dp12005.pdf},
	author = {Thomas Gries and Wim Naude}
}
@article {2018,
	title = {Automation, taxes and transfers with international rivalry},
	year = {2018},
	publisher = {Australian National University},
	keywords = {automation, global modelling, income distribution, taxes, transfers},
	issn = {2206-0332},
	url = {https://cama.crawford.anu.edu.au/sites/default/files/publication/cama_crawford_anu_edu_au/2018-09/44_2018_tyers_zhou.pdf},
	author = {Rod Tyers and Yixiao Zhou}
}
@article {2017,
	title = {Seeing Like a Tesla: How Can We Anticipate Self-Driving Worlds?},
	journal = {Glocalism},
	volume = {3},
	year = {2017},
	abstract = {In the last five years, investment and innovation in self-driving cars has accelerated dramatically. Automotive autonomy, once seen as impossible, is now sold as inevitable. Much of the governance discussion has centred on risk: will the cars be safer than their human-controlled counterparts? As with conventional cars, harder long-term questions relate to the future worlds that self-driving technologies might enable or even demand. The vision of an autonomous vehicle {\textendash} able to navigate the world{\textquoteright}s complexity using only its sensors and processors {\textendash} on offer from companies like Tesla is intentionally misleading. So-called {\textquotedblleft}autonomous{\textquotedblright} vehicles will depend upon webs of social and technical connectivity. For their purported benefits to be realised, infrastructures that were designed around humans will need to be upgraded in order to become machine-readable. It is vital to anticipate the politics of self-driving worlds in order to avoid exacerbating the inequalities that have emerged around conventional cars. Rather than being dazzled by the Tesla view, policymakers should start seeing like a city, from multiple perspectives. Good governance for self-driving cars means democratising experimentation and creating genuine collaboration between companies and local governments.

 },
	keywords = {automotive autonomy, governance, risk, self-driving cars, Tesla},
	doi = {10.12893/gjcpi.2017.3.2},
	author = {Jack Stilgoe}
}
