| 171. |
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| 172. |
- Persson, Anders, 1956-
(författare)
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Front- and backstage in "social media"
- 2010
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Ingår i: XVII ISA World Congress of Sociology, Sociology on the Move, Gothenburg, Sweden 11 - 17 July, 2010. - : International Sociological Association (ISA). ; , s. 367-367
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Excessive generosity with information about one's private life or the private lives of others, known as ”oversharing”, appears to be an oft-noted phenomenon in the context of ”new” me- dia. I have previously studied this phenomenon in connection with mobile phone conversa- tions in public places. After having eavesdropped on, or maybe rather overheard, such con- versations more systematically, I offered a number of different explanations for the intimacy that occasionally results from these conversations: Were such mobile phone users to be re- garded as inconsiderate individualists, or as communicating exhibitionists, or as absorbed communicators? It is against this background that I am studying, in this paper, oversharing in the context of social media such as Facebook, and attempting to answer the question: What is it in ”social media” as media and in the communicative situation of the person using them that constitutes front- and backstage and the border dividing them? The paper consists of a com- parative analysis of face-to-face interaction and interaction in social media in which I use Goffman's terms ”front- and backstage” and ”expressions given and expressions given off.” The two forms of interaction are also compared based on Goffman's system model of com- munication, which consists of eight different system requirements and system constraints. The results of the comparison between face-to-face interaction and interaction in social media has to do with differences in the natures of the two forms of interaction. When it comes to the communicative situation in which social media users find themselves, it appears to be para- doxical: on the one hand, the users can present themselves in a highly controlled manner while, on the other hand, the risk of oversharing appears to be great.
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| 173. |
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| 174. |
- Persson, Anders, 1972, et al.
(författare)
-
Generic Monadic Constructs for Embedded Languages
- 2012
-
Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. ; 7257
-
Konferensbidrag (refereegranskat)abstract
- We present a library of generic monadic constructs for embedded languages. It is an extension of Syntactic, a Haskell library for defining and processing generic abstract syntax. Until now, Syntactic has been mostly suited to implement languages based on pure, side effect free, expressions. The presented extension allows the pure expressions to also contain controlled side effects, enabling the representation of expressions that rely on destructive updates for efficiency. We demonstrate the use- fulness of the extension by giving examples from the embedded language Feldspar which is implemented using Syntactic.
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| 175. |
- Persson, Anders, 1972, et al.
(författare)
-
Generic monadic constructs for embedded languages
- 2011
-
Ingår i: IFL 2011, the 23rd Symposium on Implementation and Application of Functional Languages.
-
Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- We present a library for generic monadic constructs for em- bedded languages. It is an extension of Syntactic, a Haskell library for defining and processing generic abstract syntax. Syntactic works best to implement languages based on pure, side effect free, expressions. The presented extension allows the pure expressions to also contain controlled side effects, enabling efficient representation of expressions that rely on destructive updates for efficiency.
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| 176. |
- Persson, Anders, et al.
(författare)
-
How to Make Decisions with Algorithms : Ethical Decision-Making Using Algorithms within Predictive Analytics
- 2017
-
Ingår i: CEPE/Ethicomp 2017. - Torino : Università degli Studi di Torino. ; , s. 1-13
-
Konferensbidrag (refereegranskat)abstract
- The use of automated decision-making support, such as algorithms within predictive analytics,will inevitably be more and more relevant and will be affecting society. Sometimes it is good,and sometimes there seems to be negative effect, such as with discrimination. The solutionfocused on in this paper is how humans and algorithms, or ICT, could interact within ethicaldecision-making. What predictive analytics can produce is, arguably, mostly implicit knowledge,so what a human decision-maker could, possibly, help with is the explicit thought processes.This could be one way to conceptualize an interactive effect between humans and algorithms thatcould be fruitful. Presently there does not seem to be very much research regarding predictiveanalytics and ethical decisions, concerning this human-algorithm interaction. Rather it is often afocus on pure technological solutions, or with laws and regulation.
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| 177. |
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| 178. |
- Persson, Anders
(författare)
-
Implicit bias in predictive data profiling within recruitments
- 2016
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Ingår i: Privacy And Identity Management. - Berlin : Springer Berlin/Heidelberg. - 9783319557823 ; , s. 212-230
-
Konferensbidrag (refereegranskat)abstract
- Recruiters today are often using some kind of tool with data mining and profiling, as an initial screening for successful candidates. Their objective is often to become more objective and get away from human limitation, such as implicit biases versus underprivileged groups of people. In this explorative analysis there have been three potential problems identified, regarding the practice of using these predictive computer tools for hiring. First, that they might miss the best candidates, as the employed algorithms are tuned with limited and outdated data. Second, is the risk of directly or indirectly discriminate candidates, or, third, failure to give equal opportunities for all individuals. The problems are not new to us, and from this theoretical analysis and from other similar work; it seems that algorithms and predictive data mining tools have similar kinds of implicit biases as humans. Our human limitations, then, does not seem to be limited to us humans.
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| 179. |
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| 180. |
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