Toward a Minor Tech:Luchs5000 - Revision history

Simoon at 20:30, 21 July 2023

2023-07-21T20:30:48Z

← Older revision		Revision as of 20:30, 21 July 2023
Line 1:		Line 1:
	__NOTOC__		__NOTOC__
	~~= AI for All? Challenging the Democratization of Machine Learning =~~
	'''Inga Luchs'''		'''Inga Luchs'''
			= AI for All?<br>Challenging the Democratization of Machine Learning =

			<span class="running-header">AI for All? Challenging the Democratization of Machine Learning</span>

	== Abstract ==		== Abstract ==

Manetta: /* AI for All? Challenging the Democratization of Machine Learning */

2023-07-21T18:35:56Z

AI for All? Challenging the Democratization of Machine Learning

← Older revision		Revision as of 18:35, 21 July 2023
Line 92:		Line 92:

	== Works cited ==		== Works cited ==
			<div class="workscited">
	Apprich, Clemens. ''Technotopia. A Media Genealogy of Net Cultures.'' Rowman & Littlefield, 2017.		Apprich, Clemens. ''Technotopia. A Media Genealogy of Net Cultures.'' Rowman & Littlefield, 2017.

Line 177:		Line 178:

	West, Sarah Myers, Whittaker, Meredith and Kate Crawford. ''Discriminating Systems. Gender, Race, and Power in AI''. AI Now Institute, 2019.		West, Sarah Myers, Whittaker, Meredith and Kate Crawford. ''Discriminating Systems. Gender, Race, and Power in AI''. AI Now Institute, 2019.
			</div>

	[[Category:Toward a Minor Tech]]		[[Category:Toward a Minor Tech]]
	[[Category:5000 words]]		[[Category:5000 words]]

Simoon: /* AI for All? Challenging the Democratization of Machine Learning */

2023-07-17T14:32:32Z

AI for All? Challenging the Democratization of Machine Learning

← Older revision		Revision as of 14:32, 17 July 2023
Line 4:		Line 4:

	== Abstract ==		== Abstract ==
	Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.		Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.

			<div class="page-break"></div>

	== Introduction ==		== Introduction ==

Simoon at 13:16, 22 June 2023

2023-06-22T13:16:54Z

← Older revision		Revision as of 13:16, 22 June 2023
Line 1:		Line 1:
			__NOTOC__
	~~[[Category:Toward a Minor Tech]]~~
	~~[[Category:5000 words]]~~

	~~= Inga Luchs =~~

	= AI for All? Challenging the Democratization of Machine Learning =		= AI for All? Challenging the Democratization of Machine Learning =
			'''Inga Luchs'''

	== Abstract ==		== Abstract ==
Line 179:		Line 175:

	West, Sarah Myers, Whittaker, Meredith and Kate Crawford. ''Discriminating Systems. Gender, Race, and Power in AI''. AI Now Institute, 2019.		West, Sarah Myers, Whittaker, Meredith and Kate Crawford. ''Discriminating Systems. Gender, Race, and Power in AI''. AI Now Institute, 2019.

			[[Category:Toward a Minor Tech]]
			[[Category:5000 words]]

Simoon: /* AI for All? Challenging the Democratization of Machine Learning */

2023-06-20T13:50:02Z

AI for All? Challenging the Democratization of Machine Learning

Show changes

Geoffcox: /* Works cited */

2023-06-16T12:33:41Z

Works cited

Geoffcox: /* Works cited */

2023-06-16T12:30:31Z

Works cited

← Older revision		Revision as of 12:30, 16 June 2023
Line 82:		Line 82:
	Apprich, Clemens. “Introduction.” ''Pattern Discrimination'', edited by Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer and Hito Steyerl. meson press/Minnesota Press, 2018, pp. ix-xii.		Apprich, Clemens. “Introduction.” ''Pattern Discrimination'', edited by Clemens Apprich, Wendy Hui Kyong Chun, Florian Cramer and Hito Steyerl. meson press/Minnesota Press, 2018, pp. ix-xii.

	Barbrook, Richard and Andy Cameron. “The Californian Ideology.” ''Mute'' vol. 1, no. 3, September 1995. ~~<nowiki>~~https://www.metamute.org/editorial/articles/californian-ideology~~</nowiki>~~.		Barbrook, Richard and Andy Cameron. “The Californian Ideology.” ''Mute'' vol. 1, no. 3, September 1995. https://www.metamute.org/editorial/articles/californian-ideology.

	Bathgate, Rory. “AWS and Hugging Face Partner to ‘Democratise’ ML, AI Models.” ''ITPro'', 22 February 2023. <nowiki>https://www.itpro.co.uk/cloud/370113/aws-and-hugging-face-partner-to-democratise-ml-ai-models</nowiki>.		Bathgate, Rory. “AWS and Hugging Face Partner to ‘Democratise’ ML, AI Models.” ''ITPro'', 22 February 2023. <nowiki>https://www.itpro.co.uk/cloud/370113/aws-and-hugging-face-partner-to-democratise-ml-ai-models</nowiki>.

Geoffcox: /* Works Cited */

2023-06-16T12:29:30Z

Works Cited

← Older revision		Revision as of 12:29, 16 June 2023
Line 77:		Line 77:
	I would like to thank the participants of the APRJA workshop “Towards a Minor Tech”, the members of the Groningen “Data Infrastructures and Algorithmic Practices” research group and the anonymous peer reviewer for their extensive feedback on previous drafts as well as for the inspiring conversations.		I would like to thank the participants of the APRJA workshop “Towards a Minor Tech”, the members of the Groningen “Data Infrastructures and Algorithmic Practices” research group and the anonymous peer reviewer for their extensive feedback on previous drafts as well as for the inspiring conversations.

	== Works ~~Cited~~ ==		== Works cited ==
	Apprich, Clemens. ''Technotopia. A Media Genealogy of Net Cultures.'' Rowman & Littlefield, 2017.		Apprich, Clemens. ''Technotopia. A Media Genealogy of Net Cultures.'' Rowman & Littlefield, 2017.

Christianvonand at 10:39, 16 June 2023

2023-06-16T10:39:13Z

← Older revision		Revision as of 10:39, 16 June 2023
Line 2:		Line 2:
	[[Category:Toward a Minor Tech]]		[[Category:Toward a Minor Tech]]
	[[Category:5000 words]]		[[Category:5000 words]]

			= Inga Luchs =

	= AI for All? Challenging the Democratization of Machine Learning =		= AI for All? Challenging the Democratization of Machine Learning =
	~~Keywords: AI democratization, machine learning, AI industry, big tech, community-led AI, open-source~~

	Abstract: Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.

	Author Bio: Inga Luchs is a PhD candidate in Media Studies at the University of Groningen. Inga has obtained her B.A. and M.A. in cultural studies and digital culture at Leuphana University, Lüneburg. Departing from the problem of algorithmic discrimination, she seeks to investigate the key technical principles of machine learning to uncover underlying assumptions and beliefs.

	~~ORCID ID: 0000~~-~~0002~~-~~2731~~-~~0549~~		== Abstract ==
			Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.

	== Introduction ==		== Introduction ==

Ingaluchs: changed keywords

2023-06-14T09:22:28Z

changed keywords

← Older revision		Revision as of 09:22, 14 June 2023
Line 4:		Line 4:

	= AI for All? Challenging the Democratization of Machine Learning =		= AI for All? Challenging the Democratization of Machine Learning =
	Keywords: AI democratization, machine learning, AI industry, big tech, ~~the commons~~, open-source		Keywords: AI democratization, machine learning, AI industry, big tech, community-led AI, open-source

	Abstract: Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.		Abstract: Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems.

← Older revision		Revision as of 12:33, 16 June 2023
Line 84:		Line 84:
	Barbrook, Richard and Andy Cameron. “The Californian Ideology.” ''Mute'' vol. 1, no. 3, September 1995. https://www.metamute.org/editorial/articles/californian-ideology.		Barbrook, Richard and Andy Cameron. “The Californian Ideology.” ''Mute'' vol. 1, no. 3, September 1995. https://www.metamute.org/editorial/articles/californian-ideology.

	Bathgate, Rory. “AWS and Hugging Face Partner to ‘Democratise’ ML, AI Models.” ''ITPro'', 22 February 2023. ~~<nowiki>~~https://www.itpro.co.uk/cloud/370113/aws-and-hugging-face-partner-to-democratise-ml-ai-models~~</nowiki>~~.		Bathgate, Rory. “AWS and Hugging Face Partner to ‘Democratise’ ML, AI Models.” ''ITPro'', 22 February 2023. https://www.itpro.co.uk/cloud/370113/aws-and-hugging-face-partner-to-democratise-ml-ai-models.

	Benjamin, Ruha. ''Race after Technology. Abolitionist Tools for the New Jim Code.'' Polity Press, 2019.		Benjamin, Ruha. ''Race after Technology. Abolitionist Tools for the New Jim Code.'' Polity Press, 2019.

	Boudier, Jeff, Schmid, Philipp and Julien Simon. “Hugging Face and AWS Partner to Make AI More Accessible.” ''Hugging Face Blog'', 21 February 2023. ~~<nowiki>~~https://huggingface.co/blog/aws-partnership~~</nowiki>~~.		Boudier, Jeff, Schmid, Philipp and Julien Simon. “Hugging Face and AWS Partner to Make AI More Accessible.” ''Hugging Face Blog'', 21 February 2023. https://huggingface.co/blog/aws-partnership.

	Burkhardt, Marcus. “Mapping the Democratization of AI on GitHub. A First Approach.” ''The Democratization of Artificial Intelligence. Net Politics in the Era of Learning Algorithms'', edited by Andreas Sudmann. transcript, 2019, pp. 209-221''.''		Burkhardt, Marcus. “Mapping the Democratization of AI on GitHub. A First Approach.” ''The Democratization of Artificial Intelligence. Net Politics in the Era of Learning Algorithms'', edited by Andreas Sudmann. transcript, 2019, pp. 209-221.

	Costanza-Chock, Sasha. “Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice.” ''Proceedings of the Design Research Society'', 2018.		Costanza-Chock, Sasha. “Design Justice: Towards an Intersectional Feminist Framework for Design Theory and Practice.” ''Proceedings of the Design Research Society'', 2018.

	Crunchbase. “Hugging Face.” 2023. ~~<nowiki>~~https://www.crunchbase.com/organization/hugging-face~~</nowiki>~~.		Crunchbase. “Hugging Face.” 2023. https://www.crunchbase.com/organization/hugging-face.

	D’Ignazio, Catherine and Lauren F. Klein. ''Data Feminism''. MIT Press, 2020.		D’Ignazio, Catherine and Lauren F. Klein. ''Data Feminism''. The MIT Press, 2020.

	Dillet, Romain. “Hugging Face Wants to Become Your Artificial BFF.” ''TechCrunch'', 9 March 2017. ~~<nowiki>~~https://techcrunch.com/2017/03/09/hugging-face-wants-to-become-your-artificial-bff/~~</nowiki>~~.		Dillet, Romain. “Hugging Face Wants to Become Your Artificial BFF.” ''TechCrunch'', 9 March 2017. https://techcrunch.com/2017/03/09/hugging-face-wants-to-become-your-artificial-bff/.

	Dyer-Witheford, Nick, Kjøsen, Atle Mikkola and James Steinhoff. ''Inhuman Power. Artificial Intelligence and the Future of Capitalism''. Pluto Press, 2019.		Dyer-Witheford, Nick, Kjøsen, Atle Mikkola and James Steinhoff. ''Inhuman Power. Artificial Intelligence and the Future of Capitalism''. Pluto Press, 2019.
Line 104:		Line 104:
	Eubanks, Virginia. ''Automating Inequality''. St. Martin’s Press, 2017.		Eubanks, Virginia. ''Automating Inequality''. St. Martin’s Press, 2017.

	Goldman, Sharon. “Fresh Off $2B Valuation, ML Platform Hugging Face Touts ‘Open and Collaborative Approach.’” ''Venture Beat'', 9 May 2022. ~~<nowiki>~~https://venturebeat.com/ai/fresh-off-2b-valuation-machine-learning-platform-hugging-face-highlights-open-and-collaborative-approach/~~</nowiki>~~.		Goldman, Sharon. “Fresh Off $2B Valuation, ML Platform Hugging Face Touts ‘Open and Collaborative Approach.’” ''Venture Beat'', 9 May 2022. https://venturebeat.com/ai/fresh-off-2b-valuation-machine-learning-platform-hugging-face-highlights-open-and-collaborative-approach/.

	Google AI. ''Official Website''. 2023. ~~<nowiki>~~https://ai.google/~~</nowiki>~~.		Google AI. ''Official Website''. 2023. https://ai.google/.

	Google AI. “Principles.” ''Google AI Official Website'', 2023. ~~<nowiki>~~https://ai.google/principles<~~/nowiki>~~.		Google AI. “Principles.” ''Google AI Official Website'', 2023. https://ai.google/principles<.

	Google AI. “Tools.” ''Official Website'', 2023. ~~<nowiki>~~https://ai.google/tools~~</nowiki>~~.		Google AI. “Tools.” ''Official Website'', 2023. https://ai.google/tools.

	Google Developers. “Google I/O Keynote (Google I/O '17).” Uploaded on 17 May 2017. ~~<nowiki>~~https://www.youtube.com/watch?v=Y2VF8tmLFHw~~</nowiki>~~.		Google Developers. “Google I/O Keynote (Google I/O '17).” Uploaded on 17 May 2017. https://www.youtube.com/watch?v=Y2VF8tmLFHw.

	Hugging Face. ''Official Website'', 2023. ~~<nowiki>~~https://huggingface.co~~</nowiki>~~.		Hugging Face. ''Official Website'', 2023. https://huggingface.co.

	Hugging Face. “Hugging Face Hub Documentation.” ''Official Website'', 2023. ~~<nowiki>~~https://huggingface.co/docs/hub/index~~</nowiki>~~.		Hugging Face. “Hugging Face Hub Documentation.” ''Official Website'', 2023. https://huggingface.co/docs/hub/index.

	Ito, Joichi. “Resisting Reduction: A Manifesto.” ''Journal of Design and Science'', 2018		Ito, Joichi. “Resisting Reduction: A Manifesto.” ''Journal of Design and Science'', 2018

	Kratz, Jeff. “Accelerating and democratizing research with the AWS Cloud.” ''AWS Public Sector Blog'', 14 September 2022. ~~<nowiki>~~https://aws.amazon.com/blogs/publicsector/accelerating-democratizing-research-aws-cloud/~~</nowiki>~~.		Kratz, Jeff. “Accelerating and democratizing research with the AWS Cloud.” ''AWS Public Sector Blog'', 14 September 2022. https://aws.amazon.com/blogs/publicsector/accelerating-democratizing-research-aws-cloud/.

	Luchs, Inga, Apprich, Clemens and Marcel Broersma. “Learning Machine Learning. On the Political Economy of AI Online Courses.” ''Big Data & Society'', vol. 10, no. 1, 2023.		Luchs, Inga, Apprich, Clemens and Marcel Broersma. “Learning Machine Learning. On the Political Economy of AI Online Courses.” ''Big Data & Society'', vol. 10, no. 1, 2023.

	Meta AI. “About.” ''Official Website,'' 2023. ~~<nowiki>~~https://ai.facebook.com/about/~~</nowiki>~~.		Meta AI. “About.” ''Official Website,'' 2023. https://ai.facebook.com/about/.

	Metz, Cade. “Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine.” ''WIRED'', 9 November 2015. ~~<nowiki>~~https://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/~~</nowiki>~~.		Metz, Cade. “Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine.” ''WIRED'', 9 November 2015. https://www.wired.com/2015/11/google-open-sources-its-artificial-intelligence-engine/.

	Microsoft News Center. “Democratizing AI. For Every Person and Every Organization.” ''Microsoft Features'', 26 September 2016. ~~<nowiki>~~https://news.microsoft.com/features/democratizing-ai/~~</nowiki>~~		Microsoft News Center. “Democratizing AI. For Every Person and Every Organization.” ''Microsoft Features'', 26 September 2016. https://news.microsoft.com/features/democratizing-ai/.

	O’Neil, Cathy. ''Weapons of Math Destruction''. Penguin Random House, 2016.		O’Neil, Cathy. ''Weapons of Math Destruction''. Penguin Random House, 2016.

	Osman, Luqman and Dawson Sewell. “Hugging Face.” ''Contrary Research'', 14 September 2022. ~~<nowiki>~~https://research.contrary.com/reports/hugging-face~~</nowiki>~~.		Osman, Luqman and Dawson Sewell. “Hugging Face.” ''Contrary Research'', 14 September 2022. https://research.contrary.com/reports/hugging-face.

	Popkin, Helen A.S., Ohsman, Alan and Kenrik Cai. “The AI 50.” ''Forbes'', 9 May 2022. ~~<nowiki>~~https://www.forbes.com/lists/ai50/?sh=73ac1959290f~~</nowiki>~~.		Popkin, Helen A.S., Ohsman, Alan and Kenrik Cai. “The AI 50.” ''Forbes'', 9 May 2022. https://www.forbes.com/lists/ai50/?sh=73ac1959290f.

	Raymond, Eric S. ''The Cathedral and the Bazaar''. ''Musings on Linux and Open Source by an Accidental Revolutionary.'' O’Reilly, 2001.		Raymond, Eric S. ''The Cathedral and the Bazaar''. ''Musings on Linux and Open Source by an Accidental Revolutionary.'' O’Reilly, 2001.

	Srnicek, Nick. “The Political Economy of Artificial Intelligence.” Recorded talk, ''Great Transformation'', Jena, 23.09.-27.09.2019. ~~<nowiki>~~https://www.youtube.com/watch?v=Fmi3fq3Q3Bo~~</nowiki>~~.		Srnicek, Nick. “The Political Economy of Artificial Intelligence.” Recorded talk, ''Great Transformation'', Jena, 23.09.-27.09.2019. https://www.youtube.com/watch?v=Fmi3fq3Q3Bo.

	Srnicek, Nick. “Data, Compute, Labor.” ''Digital Work in the Planetary Market,'' edited by Mark Graham and Fabian Ferrari. The MIT Press, 2022, pp. 241-262.		Srnicek, Nick. “Data, Compute, Labor.” ''Digital Work in the Planetary Market,'' edited by Mark Graham and Fabian Ferrari. The MIT Press, 2022, pp. 241-262.
Line 154:		Line 154:
	Turner, Fred. ''From Counterculture to Cyberculture''. The University of Chicago Press, 2006.		Turner, Fred. ''From Counterculture to Cyberculture''. The University of Chicago Press, 2006.

	Turner, Fred. “Machine Politics. The Rise of the Internet and a New Age of Authoritarianism.” ''Harper’s Magazine'', 2019. ~~<nowiki>~~https://harpers.org/archive/2019/01/machine-politics-facebook-political-polarization/~~</nowiki>~~.		Turner, Fred. “Machine Politics. The Rise of the Internet and a New Age of Authoritarianism.” ''Harper’s Magazine'', 2019. https://harpers.org/archive/2019/01/machine-politics-facebook-political-polarization/.

	van Dijck, José. ''The Culture of Connectivity. A Critical History of Social Media''. Oxford University Press, 2013.		van Dijck, José. ''The Culture of Connectivity. A Critical History of Social Media''. Oxford University Press, 2013.