Training and the problem of data - Revision history

Hd-onions at 15:45, 16 December 2015

2015-12-16T15:45:41Z

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	To conclude, I question how we might approach data from an alternative view that assumes any information mined in human environments already contain pre-existing cultural biases that can contaminate the machine learning process. As machines gain greater ability to automate and self-train, we cannot ignore the fact that machines, despite their levels of sophistication, are limited by the sensory perceptions of their environments. At present these environments are infused with data that articulate ecologies of biases that substantiate networks of power and inequality. To answer this question would involve a return to the human relationship with the machine. It is not meant to advocate for a divorce from technology, nor suggest that mathematics is to blame for human conditions. Instead, a new data humanism advocates for development that drives experimentation with the unknown. The provocation requires a shift in focus from experimentation as means of understanding and ordering of social processes to one which is more fugitive and leaves the artefact in place to drive new possibilities of the social. This humanism argues that only in this way can the deviances and deficits of datafied relations in terms of racism, classism, sexism, homophobia and general social disinterest be redefined as the authentic way of being. If not, we risk leaving these deviations behind, in the name of innovation and in the name of searches for the perfect human and the perfect solution, by means of the perfect machine.		To conclude, I question how we might approach data from an alternative view that assumes any information mined in human environments already contain pre-existing cultural biases that can contaminate the machine learning process. As machines gain greater ability to automate and self-train, we cannot ignore the fact that machines, despite their levels of sophistication, are limited by the sensory perceptions of their environments. At present these environments are infused with data that articulate ecologies of biases that substantiate networks of power and inequality. To answer this question would involve a return to the human relationship with the machine. It is not meant to advocate for a divorce from technology, nor suggest that mathematics is to blame for human conditions. Instead, a new data humanism advocates for development that drives experimentation with the unknown. The provocation requires a shift in focus from experimentation as means of understanding and ordering of social processes to one which is more fugitive and leaves the artefact in place to drive new possibilities of the social. This humanism argues that only in this way can the deviances and deficits of datafied relations in terms of racism, classism, sexism, homophobia and general social disinterest be redefined as the authentic way of being. If not, we risk leaving these deviations behind, in the name of innovation and in the name of searches for the perfect human and the perfect solution, by means of the perfect machine.

	'''Notes''' ~~<br>~~		'''Notes'''
	I See Oscar H. Gandy, Jr., Coming to Terms with Chance: Engaging Rational Discrimination and Cumulative Disadvantage. ~~<br>~~		*I See Oscar H. Gandy, Jr., Coming to Terms with Chance: Engaging Rational Discrimination and Cumulative Disadvantage.
	II Keith + Mendi Obadike, “The Black.Net.Art Actions: Blackness For Sale (2001), The Interaction of Coloreds (2002), And The Pink Of Stealth (2003)” in Re:Skin edited by Mary Flanagan, and Austin Booth. See pp. 245 - 250. ~~<br>~~		*II Keith + Mendi Obadike, “The Black.Net.Art Actions: Blackness For Sale (2001), The Interaction of Coloreds (2002), And The Pink Of Stealth (2003)” in Re:Skin edited by Mary Flanagan, and Austin Booth. See pp. 245 - 250.
	III Christopher L. McGahan, Racing Cyberculture: Minoritarian Art and Cultural Politics on the Internet. See pp. 85 - 122. ~~<br>~~		*III Christopher L. McGahan, Racing Cyberculture: Minoritarian Art and Cultural Politics on the Internet. See pp. 85 - 122.
	IV For an accessible account of race and technology, see Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. ~~<br>~~		*IV For an accessible account of race and technology, see Wendy Hui Kyong Chun, Programmed Visions: Software and Memory.
	V Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. See pp. 38. ~~<br>~~		*V Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. See pp. 38.
	VI Rob Kitchin and Tukufu Zuberi give clear analysis of the vulnerabilities of data practices to discrimination and stereotype. See, The Data Revolution Big Data, Open Data, Data Infrastructures and Their Consequences and Thicker Than Blood: How Racial Statistics Lie, respectively. ~~<br>~~		*VI Rob Kitchin and Tukufu Zuberi give clear analysis of the vulnerabilities of data practices to discrimination and stereotype. See, The Data Revolution Big Data, Open Data, Data Infrastructures and Their Consequences and Thicker Than Blood: How Racial Statistics Lie, respectively.
	VII Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316. ~~<br>~~		*VII Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316.
	VIII Allen Newell, “The knowledge level.” in AI Magazine, vol 2. See pp. 1-20. ~~<br>~~		*VIII Allen Newell, “The knowledge level.” in AI Magazine, vol 2. See pp. 1-20.
	IX Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316. ~~<br>~~		*IX Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316.
	X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/		*X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/

Hd-onions at 15:44, 16 December 2015

2015-12-16T15:44:17Z

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	===What would it take to adopt a fugitive statistics?===		===What would it take to adopt a fugitive statistics?===
	Lecture by [[Ramon Amaro]] during the Hackers & Designers Summer Talks 2015		Lecture by [[Ramon Amaro]] during the Hackers & Designers Summer Talks 2015



	I’d like to start with short descriptions of a few projects. The first is a conceptual work by artists Keith and Mendi Obadike called ‘Interaction of Coloreds'. Secondly, I’d like to discuss a research project conducted by Dr. Latanya Sweeney on the social implications of Google Adsense.		I’d like to start with short descriptions of a few projects. The first is a conceptual work by artists Keith and Mendi Obadike called ‘Interaction of Coloreds'. Secondly, I’d like to discuss a research project conducted by Dr. Latanya Sweeney on the social implications of Google Adsense.

	My hope is that this discussion will take us beyond our current understandings about data practices, user experience, user interaction or the coding environment, and instead intervene somewhere in between these spaces, where what I will later discuss are often unseen, yet explicitly clear violences that reveal themselves in data-dependent web and design practices. I use the word ‘violence’ here intentionally. It’s a strong word that few would associate with a matrix of data or with design and coding applications. After all it’s just code. It’s just data.… But, I believe violence is a necessary description of the possible residual effects of practices that do not carefully consider the cultural circumstances that inform the data we use as input for training purposes; or how interactions with our digital applications might be infused with cultural biases, even when these technologies (or the designers themselves) are not aware of the cultural impacts of the products they provide.		My hope is that this discussion will take us beyond our current understandings about data practices, user experience, user interaction or the coding environment, and instead intervene somewhere in between these spaces, where what I will later discuss are often unseen, yet explicitly clear violences that reveal themselves in data-dependent web and design practices. I use the word ‘violence’ here intentionally. It’s a strong word that few would associate with a matrix of data or with design and coding applications. After all it’s just code. It’s just data.… But, I believe violence is a necessary description of the possible residual effects of practices that do not carefully consider the cultural circumstances that inform the data we use as input for training purposes; or how interactions with our digital applications might be infused with cultural biases, even when these technologies (or the designers themselves) are not aware of the cultural impacts of the products they provide.


	My concern here is then two-fold: how can theories behind the maths — and the digital practices that they power — inform certain rationales concerning the development of an ideal agency? What I mean is, how did the maths that inform our present coding languages, algorithm designs and other design practices come about, and through which logics? And, is there a conflict between how the maths were derived and how we apply them to today’s digital environments?		My concern here is then two-fold: how can theories behind the maths — and the digital practices that they power — inform certain rationales concerning the development of an ideal agency? What I mean is, how did the maths that inform our present coding languages, algorithm designs and other design practices come about, and through which logics? And, is there a conflict between how the maths were derived and how we apply them to today’s digital environments?
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	[[File:figure1.0.jpg\|500px\|Figure 1.0 - The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html]]		[[File:figure1.0.jpg\|500px\|Figure 1.0 - The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html]]


	Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?		Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?
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	To conclude, I question how we might approach data from an alternative view that assumes any information mined in human environments already contain pre-existing cultural biases that can contaminate the machine learning process. As machines gain greater ability to automate and self-train, we cannot ignore the fact that machines, despite their levels of sophistication, are limited by the sensory perceptions of their environments. At present these environments are infused with data that articulate ecologies of biases that substantiate networks of power and inequality. To answer this question would involve a return to the human relationship with the machine. It is not meant to advocate for a divorce from technology, nor suggest that mathematics is to blame for human conditions. Instead, a new data humanism advocates for development that drives experimentation with the unknown. The provocation requires a shift in focus from experimentation as means of understanding and ordering of social processes to one which is more fugitive and leaves the artefact in place to drive new possibilities of the social. This humanism argues that only in this way can the deviances and deficits of datafied relations in terms of racism, classism, sexism, homophobia and general social disinterest be redefined as the authentic way of being. If not, we risk leaving these deviations behind, in the name of innovation and in the name of searches for the perfect human and the perfect solution, by means of the perfect machine.		To conclude, I question how we might approach data from an alternative view that assumes any information mined in human environments already contain pre-existing cultural biases that can contaminate the machine learning process. As machines gain greater ability to automate and self-train, we cannot ignore the fact that machines, despite their levels of sophistication, are limited by the sensory perceptions of their environments. At present these environments are infused with data that articulate ecologies of biases that substantiate networks of power and inequality. To answer this question would involve a return to the human relationship with the machine. It is not meant to advocate for a divorce from technology, nor suggest that mathematics is to blame for human conditions. Instead, a new data humanism advocates for development that drives experimentation with the unknown. The provocation requires a shift in focus from experimentation as means of understanding and ordering of social processes to one which is more fugitive and leaves the artefact in place to drive new possibilities of the social. This humanism argues that only in this way can the deviances and deficits of datafied relations in terms of racism, classism, sexism, homophobia and general social disinterest be redefined as the authentic way of being. If not, we risk leaving these deviations behind, in the name of innovation and in the name of searches for the perfect human and the perfect solution, by means of the perfect machine.

			'''Notes''' <br>
	'''Notes'''		I See Oscar H. Gandy, Jr., Coming to Terms with Chance: Engaging Rational Discrimination and Cumulative Disadvantage. <br>
			II Keith + Mendi Obadike, “The Black.Net.Art Actions: Blackness For Sale (2001), The Interaction of Coloreds (2002), And The Pink Of Stealth (2003)” in Re:Skin edited by Mary Flanagan, and Austin Booth. See pp. 245 - 250. <br>
	I See Oscar H. Gandy, Jr., Coming to Terms with Chance: Engaging Rational Discrimination and Cumulative Disadvantage.		III Christopher L. McGahan, Racing Cyberculture: Minoritarian Art and Cultural Politics on the Internet. See pp. 85 - 122. <br>
			IV For an accessible account of race and technology, see Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. <br>
	II Keith + Mendi Obadike, “The Black.Net.Art Actions: Blackness For Sale (2001), The Interaction of Coloreds (2002), And The Pink Of Stealth (2003)” in Re:Skin edited by Mary Flanagan, and Austin Booth. See pp. 245 - 250.		V Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. See pp. 38. <br>
			VI Rob Kitchin and Tukufu Zuberi give clear analysis of the vulnerabilities of data practices to discrimination and stereotype. See, The Data Revolution Big Data, Open Data, Data Infrastructures and Their Consequences and Thicker Than Blood: How Racial Statistics Lie, respectively. <br>
	III Christopher L. McGahan, Racing Cyberculture: Minoritarian Art and Cultural Politics on the Internet. See pp. 85 - 122.		VII Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316. <br>
			VIII Allen Newell, “The knowledge level.” in AI Magazine, vol 2. See pp. 1-20. <br>
	IV For an accessible account of race and technology, see Wendy Hui Kyong Chun, Programmed Visions: Software and Memory.		IX Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316. <br>

	V Wendy Hui Kyong Chun, Programmed Visions: Software and Memory. See pp. 38.

	VI Rob Kitchin and Tukufu Zuberi give clear analysis of the vulnerabilities of data practices to discrimination and stereotype. See, The Data Revolution Big Data, Open Data, Data Infrastructures and Their Consequences and Thicker Than Blood: How Racial Statistics Lie, respectively.

	VII Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316.

	VIII Allen Newell, “The knowledge level.” in AI Magazine, vol 2. See pp. 1-20.

	IX Thomas G. Dietterich, “Learning at the Knowledge Level,” in Machine Learning, vol . See pp 287-316.

	X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/		X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/

Hd-onions at 15:03, 15 December 2015

2015-12-15T15:03:13Z

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	X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/		X The Future of Life Institute describes itself as a ‘volunteer-run research and outreach organization working to mitigate existential risks facing humanity.’ The institute deals with issues of artificial intelligence and human interaction. See http://futureoflife.org/ai-news/



	~~[[Category:HDSA2015]]~~
	~~[[Category:Article]]~~
	~~[[Category:Ready to be published]]~~
	~~[[Category:Print]]~~

Selby at 10:38, 14 December 2015

2015-12-14T10:38:33Z

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	Ramon Amaro is a PhD researcher at the Centre for Cultural Studies, Goldsmiths, University of London and Assistant Editor of Big Data & Society, a SAGE journal. Ramon is a Mechanical Engineer and Social Scientist with a background in Technology and Engineering Policy, Engineering Quality Design, and Sociological Research. His doctoral research looks at the ethics of machine learning, artificial intelligence, and big data mining.

	Ramon holds a Masters degree in Sociological Research from the University of Essex, and a BSe in Mechanical Engineering from the University of Michigan, Ann Arbor. Prior to my doctoral research, he was a policy manager for the American Society of Mechanical Engineers (ASME), Alternative Fuels Division and a quality design engineer for General Motors Corporation

	~~This lecture was part of the Hackers & Designers Summer Talks 2015~~
	~~7 August 2015 in De PUNT Amsterdam~~
	~~http://summer.hackersanddesigners.nl~~

	[[Category:HDSA2015]]		[[Category:HDSA2015]]

Selby at 10:37, 14 December 2015

2015-12-14T10:37:16Z

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	===What would it take to adopt a fugitive statistics?===		===What would it take to adopt a fugitive statistics?===
	Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015		Lecture by [[Ramon Amaro]] during the Hackers & Designers Summer Talks 2015

Selby at 10:36, 14 December 2015

2015-12-14T10:36:38Z

Show changes

Selby: /* Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015 */

2015-11-24T15:00:19Z

Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015

Show changes

Selby: /* Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015 */

2015-11-24T14:48:02Z

Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015

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	This experiment in culture, or art (what is art if not an experiment in possibilities?), illuminate the role of digital design practices in the manoeuvrability of certain people. However, these experiments are limited to the speculative space. They have applicable consequences that have already foreclosed on the life chances of many individuals. Studies have shown that commonly used self-learning machines can produce results that reveal racist and other discriminatory practices, even if the algorithms are not designed with these intentions.vi In other words, machines may not yet comprehend the dynamics of racism, but they can be racist. For instance, in 2013 Dr. Latanya Sweeney, Professor of Government and Technology in Residence and Director of the Data Privacy Lab at Harvard University. In her study, Sweeney found that online ads suggesting arrest records appeared more often in searches for ‘black-sounding’ names than in searches for ‘white-sounding’ names. The obscurity of the measureables, ‘black-sounding’ and ‘white-sounding’, only illuminate the importance of Sweeney’s research question: Can racial groups be adversely affected by a digital application assumed to be egalitarian? Sweeney empirically questions suspected patterns of ad delivery in real-world searches. The study began with the assumption that personalized ads in Google Search suggestive of arrest records did not differ by race. This was accomplished by constructing the scientifically best instance of the pattern, or one with names shown to be racially identifying and pseudo-randomly selected. This was correlated with a proxy for race associated with racialised names, including hand picked names, and filtered through Google Images. What Sweeney found was ads displayed by Google Adsense suggesting arrest tended to appear with names associated with blacks, and neutral ads or no ads appeared with names associated with whites, regardless of whether that person had been previously arrested. The findings question the place of bias in realtime learning systems and how knowledge exchange operates despite the potential causes of discrimination.		This experiment in culture, or art (what is art if not an experiment in possibilities?), illuminate the role of digital design practices in the manoeuvrability of certain people. However, these experiments are limited to the speculative space. They have applicable consequences that have already foreclosed on the life chances of many individuals. Studies have shown that commonly used self-learning machines can produce results that reveal racist and other discriminatory practices, even if the algorithms are not designed with these intentions.vi In other words, machines may not yet comprehend the dynamics of racism, but they can be racist. For instance, in 2013 Dr. Latanya Sweeney, Professor of Government and Technology in Residence and Director of the Data Privacy Lab at Harvard University. In her study, Sweeney found that online ads suggesting arrest records appeared more often in searches for ‘black-sounding’ names than in searches for ‘white-sounding’ names. The obscurity of the measureables, ‘black-sounding’ and ‘white-sounding’, only illuminate the importance of Sweeney’s research question: Can racial groups be adversely affected by a digital application assumed to be egalitarian? Sweeney empirically questions suspected patterns of ad delivery in real-world searches. The study began with the assumption that personalized ads in Google Search suggestive of arrest records did not differ by race. This was accomplished by constructing the scientifically best instance of the pattern, or one with names shown to be racially identifying and pseudo-randomly selected. This was correlated with a proxy for race associated with racialised names, including hand picked names, and filtered through Google Images. What Sweeney found was ads displayed by Google Adsense suggesting arrest tended to appear with names associated with blacks, and neutral ads or no ads appeared with names associated with whites, regardless of whether that person had been previously arrested. The findings question the place of bias in realtime learning systems and how knowledge exchange operates despite the potential causes of discrimination.
	[[File:figure1.0.jpg\|500px\|Figure 1.0]]
	''Figure 1.0''		[[File:figure1.0.jpg\|500px\|Figure 1.0]]

			'''Figure 1.0''' \| ''The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html''

	Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?		Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?

Selby: /* Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015 */

2015-11-24T14:46:14Z

Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015

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	[[File:figure2.0.jpg\|500px\|Figure 2.0]]		[[File:figure2.0.jpg\|500px\|Figure 2.0]]

	'''Figure 2.0''' \| ''The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html''		'''Figure 2.0''' \| ''The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html''

Selby: /* Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015 */

2015-11-24T14:45:37Z

Lecture by Ramon Amaro during the Hackers & Designers Summer Talks 2015

← Older revision		Revision as of 16:45, 24 November 2015
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	Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?		Although Sweeney’s experiment does not point towards explicit intentionality by Google as the mediator of data exchange, much as in Foucault’s analysis of Borges animal taxonomy, it recognises the implicit nature of biases and suspicions of digital profiles that play themselves out at the point of user engagement. Therefore, as Sweeney asks, how can designer or coders think through societal consequences such as structural racism in technology design? And how can technologies be built to instead form new techno-political experiences for classified users?

	The difficulty here is that most generalisation patterns seek to improve performance without altering existing knowledge frameworks.i For knowledge-based learning researchers, knowledge is seen as ‘a succinct and efficient means of describing and predicting the behavior of a computer system’.ii From within this context, advocates argue		The difficulty here is that most generalisation patterns seek to improve performance without altering existing knowledge frameworks.i For knowledge-based learning researchers, knowledge is seen as ‘a succinct and efficient means of describing and predicting the behavior of a computer system’.ii From within this context, advocates argue

	[[File:figure2.0.jpg\|500px\|Figure 2.0]]		[[File:figure2.0.jpg\|500px\|Figure 2.0]]
	'''Figure 2.0''' \| ''The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html''		'''Figure 2.0''' \| ''The Interaction of Coloreds (2002). Source: http://www.blacknetart.com/IOCccs.html''