Take away message

  • la frontiera tra esseri umani e macchine è porosa
  • i valori etici emergono nella stabilizzazione di questa frontiera

Schema dell’intervento

  • due aneddoti per entrare in materia
  • modellizzazione e visioni del mondo
  • due esempi: PageRank (pertinenza) e Tinder (amore/incontro/match)
  • .. e la filosofia?

Con una riga di codice…

Le computers

Ci si ricorda solo di…

John William Mauchly e J. Presper Eckert,

Gli a priori dell’opposizione umano/macchina

Uomo/macchina

Senso/sintassi

Qualità/quantità

Importante/triviale

Valori/neutro

Immateriale/materiale

Uomo/animale

Uomo/donna

Grande intelletto/piccole mani

Solo narcisismo?

Le frontiere instabili

My posthumanist account calls into question the givenness of the differential categories of human and nonhuman, examining the practices through which these differential boundaries are stabilized and destabilized. Barad, Karen. 2007. Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Second Printing edition. Durham: Duke University Press Books.

La modellizzazione del mondo

Tre fasi

  • modello discorsivo (rappresentazione)
  • modello funzionale
  • modello fisico

Le tre fasi non sono completamente separate!

L’implementazione di visioni del mondo

  • modelli, algoritmi, formati, protocolli
  • modello basato sui valori
  • riproduzione di questi valori
  • profezia che si autoavvera (self-fulfilling profecy)

Le macchine morali

L’algorithme du moteur de recherche de Google, le PageRank, est une machine morale. D. Cardon, Dans l’esprit de PageRank

PageRank: cos’è la “pertinenza”?

  • articolo di Brin e Page
  • Science citation index di Eugène Garfield (1962)
    • un punto di vista “esterno” (contrario del peer review)
    • procedurale e senza contenuto
    • onestà della citazione

Tinder: cos’è l’amore?

Il brevetto di Tinder

In this example, Harry is 10 years older than Sally, makes $10,000 more per year, and has a Master’s degree while Sally has a bachelor’s degree. Even with these disparities, matching server 20 will give Sally’s profile a high score which makes it more likely that Sally’s profile will appear in Harry’s result list. However, if it was Sally who submitted the search, and matching server 20 was evaluating Harry’s profile, a different score is possible. So, if it were Sally who was 10 years older, made $10,000 more per year, and had a Master’s degree while Harry had a Bachelor’s degree, matching server 20 would give a low score to Harry’s profile, making it less likely that his profile would appear in Sally’s result list. Matching server 20 may be configured this way because empirical data has shown that these demographic differences do not have an equivalent effect on the choices men and women make regarding matches.

Il brevetto di Tinder

In some embodiments, matching server 20 may analyze factors such as, but not limited to; average number of words per sentence, total number of words with greater than three syllables, and total number of words in the profile. Matching server 20 may also concatenate all of the collected responses with a single space between them. It may further break the text into sentences, words, and syllables. From these statistics, matching server 20 may also be configured to generate a readability score by, in one embodiment, taking the average of the Flesch Kincaid Reading Ease test, the Flesch Kincaid Grade Level test, and the Gunning Fox score. Other embodiments may utilize any other combination of these or other tests to determine a readability score. In some embodiments, analyses may be used to determine the IQ of an entity, the grade level of the writing, or how nervous the entity generally is. An advantage of this embodiment may be that the system provides user 14 with a metric for determining approximate intelligence of other users.

Il brevetto di Tinder

In some embodiments, matching server 20 may be configured to impute a level of physical attractiveness to an entity in pool 30. Matching server 20 may be configured to monitor how frequent an entity in pool 30 has been viewed as well as how many times that entity has been part of a result list in order to impute the level of physical attractiveness. Matching server 20 may further be configured to generate a score based on this data. Further, in some embodiments, matching server 20 may impute physical attractiveness to an entity based on the imputed physical attractiveness scores of other entities. Source: https://patents.google.com/patent/US9733811B2/en

Altri modelli dell’amore?

Interrogarsi su come emergono le idee, interrogarsi su come emerge il senso

Socrate e la filosofia come bug