upgrading_intersectional_feminism_with_the_hacker_class.2.ita.srt 30 KB

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  1. 1
  2. 00:00:00,120 --> 00:00:05,320
  3. Parte 2: Economia politica dell'hack
  4. vettorialismo e hackers come classe
  5. 2
  6. 00:00:06,920 --> 00:00:12,480
  7. Nella cultura popolare, l'hacker é stereotipato come una versione di fantascienza del mago -
  8. 3
  9. 00:00:12,480 --> 00:00:18,440
  10. normalmente un uomo bianco etero cis, un eroe solitario con poteri di perizia informatica
  11. 4
  12. 00:00:18,440 --> 00:00:23,080
  13. incomprensibili per noialtr* che non abbiamo l'essenza magica dell'hacker.
  14. 5
  15. 00:00:25,000 --> 00:00:26,480
  16. CONTROLLO TUTTO !!!
  17. 6
  18. 00:00:27,240 --> 00:00:31,720
  19. Per contrasto, mckenzie wark, autor*
  20. di "a hacker manifesto",
  21. 7
  22. 00:00:31,720 --> 00:00:35,640
  23. descrive l'hacker
  24. come qualcun* che produce informazione
  25. 8
  26. 00:00:35,640 --> 00:00:38,000
  27. ma non la possiede o la controlla.
  28. 9
  29. 00:00:38,480 --> 00:00:42,840
  30. Definisce l* hacker come una classe,
  31. in termini di economia politica,
  32. 10
  33. 00:00:42,840 --> 00:00:47,840
  34. sfruttata sotto la nuova classe dominante, che lei chiama la classe vettorialista
  35. 11
  36. 00:00:48,280 --> 00:00:53,760
  37. (per esempio , google, amazon, facebook, apple,
  38. microsoft e i loro equivalenti cinesi).
  39. 12
  40. 00:00:53,880 --> 00:00:58,760
  41. Il vettorialismo é un modo di produzione basato sulla asimmetria dell'informazione,
  42. 13
  43. 00:00:58,760 --> 00:01:03,800
  44. creata attraverso la proprietà e il controllo sugli stock, flussi, e vettor di informazioni
  45. 14
  46. 00:01:03,800 --> 00:01:05,800
  47. da parte della classe vettorialista.
  48. 15
  49. 00:01:06,320 --> 00:01:11,800
  50. Con questa definizione, la classe hacker
  51. non sono solo l* programmator* informatic*
  52. 16
  53. 00:01:11,800 --> 00:01:15,600
  54. che sono sfruttat* da* vettorialist*
  55. che brevettano il loro codice.
  56. 17
  57. 00:01:15,600 --> 00:01:21,360
  58. Chiunque di noi che cammini in giro con
  59. uno smartphone é parte della classe hacker
  60. 18
  61. 00:01:21,360 --> 00:01:23,920
  62. perché anche senza saperlo,
  63. 19
  64. 00:01:23,920 --> 00:01:28,960
  65. produciamo nuovi dati di localizzazione e non possediamo ne controlliamo questa informazione
  66. 20
  67. 00:01:28,960 --> 00:01:31,120
  68. ma l* vettorialist* si.
  69. 21
  70. 00:01:32,920 --> 00:01:37,800
  71. Dai messaggi di testo alle videochiamate, dalle ricerche web ai doom scrolls,
  72. 22
  73. 00:01:37,800 --> 00:01:40,560
  74. con ogni aggiornamento di stato, ogni like,
  75. 23
  76. 00:01:40,560 --> 00:01:46,160
  77. ogni interazione con le nostre apparacchiature intelligenti, produciamo nuova informazione,
  78. 24
  79. 00:01:46,160 --> 00:01:48,360
  80. se non altro,
  81. sopra chi siamo.
  82. 25
  83. 00:01:48,880 --> 00:01:52,960
  84. Chi siamo é quello che facciamo e tutto sta
  85. registrato e processato.
  86. 26
  87. 00:01:53,320 --> 00:01:58,800
  88. A prescindere delle qualità familiari, amichevoli e compagneristiche delle relazioni(?),
  89. 27
  90. 00:01:58,800 --> 00:02:02,600
  91. l'intimità si quantifica e mercantilizza come informazione,
  92. 28
  93. 00:02:02,600 --> 00:02:05,480
  94. insieme a tutte le alre forme di affetto.
  95. 29
  96. 00:02:05,840 --> 00:02:10,680
  97. Se l'hacking si definisce dalla curiosità, allora gia sta monopolizzata da google.
  98. 30
  99. 00:02:11,680 --> 00:02:17,200
  100. Quando si faceva una domanda, l* hacker chauvinist* vecchia scuola ti avrebbero detto RTFM
  101. 31
  102. 00:02:17,200 --> 00:02:20,320
  103. che significa "leggi il fottuto manuale"
  104. 32
  105. 00:02:20,320 --> 00:02:22,800
  106. adesso dicono :"Cercalo sul maledetto google".
  107. 33
  108. 00:02:23,080 --> 00:02:27,280
  109. Ma quando facciamo una ricerca su uan piattaforma vettorialista come google,
  110. 34
  111. 00:02:27,280 --> 00:02:29,800
  112. anche lei ci sta osservando.
  113. 35
  114. 00:02:29,880 --> 00:02:33,960
  115. La risposta alla nostra ricerca
  116. é algoritmicamente selezionata per quello che stiamo cercando,
  117. 36
  118. 00:02:33,960 --> 00:02:36,880
  119. basandosi sulle informazioni
  120. che gia possiede su di noi.
  121. 37
  122. 00:02:37,640 --> 00:02:40,520
  123. Alquanto siamo soddisfatt*
  124. o no dal risultato,
  125. 38
  126. 00:02:40,520 --> 00:02:44,360
  127. google impara da quello
  128. che cerchiamo, dai nostri desideri,
  129. 39
  130. 00:02:44,360 --> 00:02:49,560
  131. che siano profondi o superficiali, essa é una informazione importante da tenere su di noi
  132. 40
  133. 00:02:50,080 --> 00:02:55,160
  134. specialmente quando non sappiamo
  135. quello che l* altr* vogliono o ottengono,
  136. 41
  137. 00:02:55,160 --> 00:02:59,560
  138. quando google ottiene di saperlo per tutt*
  139. miliardi di volte di più.
  140. 42
  141. 00:03:00,480 --> 00:03:04,800
  142. Quando un artista condivide una opera d'arte su una piattaforma vettorialista
  143. 43
  144. 00:03:04,800 --> 00:03:07,560
  145. come instagram, youtube, spotify, etc.
  146. 44
  147. 00:03:07,560 --> 00:03:12,240
  148. é possibile che la piattaforma l* paghi una frazione del valore che producono,
  149. 45
  150. 00:03:12,240 --> 00:03:14,640
  151. proporzionalmente all'attenzione che attraggono,
  152. 46
  153. 00:03:14,640 --> 00:03:17,840
  154. la quale é uno sfruttamento verso l* artist*.
  155. 47
  156. 00:03:18,200 --> 00:03:22,880
  157. Inoltre chiunque dia attenzione all'opera
  158. d'arte é anchess* sfruttat*,
  159. 48
  160. 00:03:22,880 --> 00:03:28,680
  161. usualmente senza essere pagat*, di fatto
  162. é la nostra attenzione quella che paga tutto.
  163. 49
  164. 00:03:29,080 --> 00:03:33,760
  165. In una maniera diversa, la proprietà intellettuale
  166. e la mercificazione dell'informazione
  167. 50
  168. 00:03:33,760 --> 00:03:38,720
  169. funziona anche peggio per l* scienziat* e accademic* che devono pagare le riviste
  170. 51
  171. 00:03:38,720 --> 00:03:43,000
  172. per pubblicare il loro lavoro, solo per che questo sia
  173. messo dietro paywall con prezzi osceni
  174. 52
  175. 00:03:43,000 --> 00:03:44,640
  176. cosi che nessun* possa leggerli.
  177. 53
  178. 00:03:45,600 --> 00:03:50,560
  179. La frase "se non stai pagando per il prodotto, allora sei il prodotto"
  180. 54
  181. 00:03:50,560 --> 00:03:55,840
  182. é stata scritta per i mezzi di trasmissione nel 1973
  183. 55
  184. 00:03:55,840 --> 00:03:59,320
  185. quando non c'era niente
  186. di simile alla pubblicità personalizzata.
  187. 56
  188. 00:04:00,000 --> 00:04:03,640
  189. Nel mondo dei social media
  190. se non stai pagando per il prodotto,
  191. 57
  192. 00:04:03,640 --> 00:04:06,320
  193. sei sia il prodotto
  194. che il produttore.
  195. 58
  196. 00:04:07,240 --> 00:04:12,040
  197. I mezzi di telediffusione solo potevano prender
  198. di mira uno alla volta i gruppi demografici;
  199. 59
  200. 00:04:12,880 --> 00:04:18,720
  201. i social media possono prendere di mira ogni utente personalmente
  202. 60
  203. 00:04:18,720 --> 00:04:23,680
  204. perché ogni utente dei social media produce nuova informazione su stess*,
  205. 61
  206. 00:04:23,680 --> 00:04:26,200
  207. il suo intorno, le sue relazioni e i suoi desideri.
  208. 62
  209. 00:04:26,880 --> 00:04:29,280
  210. L'internet non é libero e non lo é mai stato.
  211. 63
  212. 00:04:29,680 --> 00:04:34,000
  213. Per prima cosa per collegarsi é necessario pagare l'hardware
  214. 64
  215. 00:04:34,000 --> 00:04:37,800
  216. perché ovviamente, la estrazione
  217. di minerali di valore dalla terra,
  218. 65
  219. 00:04:37,800 --> 00:04:43,160
  220. trasposrtarli alle fornaci per fonderli, produrre e saldare microchips ha un costo.
  221. 66
  222. 00:04:43,160 --> 00:04:48,280
  223. Alimentare l'hardware anch'esso necessita di elettricità prodotta da risorse naturali,
  224. 67
  225. 00:04:48,280 --> 00:04:49,720
  226. la quale non é gratis.
  227. 68
  228. 00:04:50,120 --> 00:04:54,000
  229. gli internet service providers hanno bisogno
  230. di molto hardware e di elettricità
  231. 69
  232. 00:04:54,000 --> 00:04:57,000
  233. per far funzionare internet, questo é il perché
  234. 70
  235. 00:04:57,000 --> 00:05:01,080
  236. paghiamo ancora per l'accesso e l'uso di internet nella piu parte del mondo.
  237. 71
  238. 00:05:01,400 --> 00:05:05,640
  239. Per fornire servizi internet come siti web, email , e social media
  240. 72
  241. 00:05:05,640 --> 00:05:08,520
  242. c'é altrettanto bisogno di hardware, e alla fine,
  243. 73
  244. 00:05:08,520 --> 00:05:12,880
  245. servers su computer funzionanti
  246. collegati ad internet.
  247. 74
  248. 00:05:13,280 --> 00:05:19,400
  249. Quindi quando l* vettorialist* dicono che stanno fornendo servizi gratis, come google, facebook, etc.
  250. 75
  251. 00:05:19,720 --> 00:05:21,920
  252. questo non signiica che hanno trovato una maniera magica
  253. 76
  254. 00:05:21,920 --> 00:05:24,800
  255. per produrre computer e reti gratis
  256. 77
  257. 00:05:24,800 --> 00:05:28,800
  258. e nemmeno una sorgente inesauribile di energia per fare funzionare tutto.
  259. 78
  260. 00:05:29,680 --> 00:05:33,280
  261. l* vettorialist* stanno pagando il costo
  262. dei servizi che ci forniscono,
  263. 79
  264. 00:05:33,280 --> 00:05:36,560
  265. incluso il costo di far girare dei supercomputer
  266. 80
  267. 00:05:36,560 --> 00:05:41,360
  268. con intelligenze artificiali che personalizzano ogni nostra esperienza dei servizi
  269. 81
  270. 00:05:41,560 --> 00:05:44,120
  271. e ovviamente,
  272. non lo fanno per carità.
  273. 82
  274. 00:05:46,040 --> 00:05:49,080
  275. facebook sta anche pagando
  276. per la connettività delle persone
  277. 83
  278. 00:05:49,080 --> 00:05:53,240
  279. in più di 50 paesi della america latina,
  280. africa, e asia, ma,
  281. 84
  282. 00:05:53,240 --> 00:05:56,880
  283. non gli da un connessione a niente di piu che facebook e i suoi partner.
  284. 85
  285. 00:05:56,880 --> 00:05:58,920
  286. Lo fanno perché sanno
  287. 86
  288. 00:05:58,920 --> 00:06:03,840
  289. che il ritorno sara molto piu grande
  290. se investono nella loro propria infrastruttura.
  291. 87
  292. 00:06:04,400 --> 00:06:08,280
  293. "il potere adesso
  294. risiede nelle infrastrutture di questo mondo...
  295. 88
  296. 00:06:08,280 --> 00:06:13,120
  297. le infrastrutture organizzano una vita
  298. senza un mondo, sospesa, prescindibile,
  299. 89
  300. 00:06:13,120 --> 00:06:16,280
  301. alla mercé di
  302. chiunque la gestisca..."
  303. 90
  304. 00:06:16,640 --> 00:06:21,680
  305. Le forme di dominazione di classe pre-capitalista
  306. nacquero dall'astrazione della rendita(?)
  307. 91
  308. 00:06:21,680 --> 00:06:26,120
  309. che é il plus valore del prodotto sfruttato dalla classe contadina(?)
  310. 92
  311. 00:06:26,120 --> 00:06:29,320
  312. la quale viveva sulla stessa terra che la sosteneva.
  313. 93
  314. 00:06:30,160 --> 00:06:34,320
  315. Il capitalismo crebbe dall'astrazione
  316. del profitto,
  317. 94
  318. 00:06:34,320 --> 00:06:37,560
  319. che é il plus valore sfruttato dalla classe lavoratrice(?)
  320. 95
  321. 00:06:37,560 --> 00:06:41,280
  322. alla quale si paga uno stipendio per mantenere la propria vita.
  323. 96
  324. 00:06:42,520 --> 00:06:45,960
  325. Il vettorialismo é il livello successivo di astrazione
  326. 97
  327. 00:06:45,960 --> 00:06:49,280
  328. che sfrutta il plus valore(?) della informazione dalla classe hacker
  329. 98
  330. 00:06:49,280 --> 00:06:52,400
  331. e la maggiorparte della classe hacker che produce informazione
  332. 99
  333. 00:06:52,400 --> 00:06:54,240
  334. non viene nemmeno pagata per questo,
  335. 100
  336. 00:06:54,240 --> 00:06:57,840
  337. mentre la sostenibilità di tutta la vita sulla terra
  338. é in pericolo
  339. 101
  340. 00:06:57,840 --> 00:07:02,000
  341. e l* vettorialist* fanno a gara per creare colonie su altri pianeti.
  342. 102
  343. 00:07:03,440 --> 00:07:07,760
  344. Ogni nuova forma di dominazione di classe si costruisce sulla precedente.
  345. 103
  346. 00:07:07,960 --> 00:07:11,040
  347. Il capitalismo non aboli la classe renditiera(?),
  348. 104
  349. 00:07:11,040 --> 00:07:15,120
  350. é diventata piu sfruttatrice(?) sotto
  351. la norma capitalista.
  352. 105
  353. 00:07:15,680 --> 00:07:19,800
  354. Il vettorialismo non abolisce le classi capitaliste e renditiere(?),
  355. 106
  356. 00:07:19,800 --> 00:07:24,240
  357. le fa diventare ancora piu sfruttanti sotto
  358. la norma capitalista.
  359. 107
  360. 00:07:24,880 --> 00:07:29,040
  361. Tutto sfruttamento di classe si legalizza attraverso le leggi della proprietà
  362. 108
  363. 00:07:29,040 --> 00:07:32,800
  364. che sono mantenute e fatte rispettare dalla violenza costante
  365. 109
  366. 00:07:32,800 --> 00:07:34,920
  367. dell'apparato statale repressivo.
  368. 110
  369. 00:07:35,320 --> 00:07:39,040
  370. Da quando l* vettorialist* sono
  371. la classe dominante della nostra era,
  372. 111
  373. 00:07:39,040 --> 00:07:42,480
  374. hanno aumentato costantemente il loro
  375. monopolio della violenza
  376. 112
  377. 00:07:42,480 --> 00:07:45,320
  378. attraverso vaste quantità di nuova informazione,
  379. 113
  380. 00:07:45,320 --> 00:07:49,960
  381. intelligenze artificiali e supercomputer per processare il tutto.
  382. 114
  383. 00:07:50,520 --> 00:07:52,840
  384. Molt* informator* tra l* qual*
  385. 115
  386. 00:07:52,840 --> 00:07:56,840
  387. l* piu famos* chelsea manning e edward snowden
  388. hanno messo allo scoperto
  389. 116
  390. 00:07:56,840 --> 00:08:00,280
  391. che la relazione tra stati uniti e vettorilist*
  392. 117
  393. 00:08:00,280 --> 00:08:05,600
  394. non si differenzia tanto da quello che sta succedendo
  395. dietro il grande firewall cinese
  396. 118
  397. 00:08:05,600 --> 00:08:09,280
  398. con le applicazioni finanziate
  399. dal partito comunista cinese.
  400. 119
  401. 00:08:09,960 --> 00:08:12,960
  402. Si direbbe pure che il modello cinese é piu onesto
  403. 120
  404. 00:08:12,960 --> 00:08:15,320
  405. perché esercitato piu apertamente.
  406. 121
  407. 00:08:15,320 --> 00:08:19,720
  408. Fino ad ora sono state piazzate piu di un miliardo di telecamere di soveglianza
  409. 122
  410. 00:08:19,720 --> 00:08:23,640
  411. con un miglioramento costante delle loro capacità di riconoscimento facciale
  412. 123
  413. 00:08:24,640 --> 00:08:31,240
  414. ma anche questo numero é piccolo in confronto al numero di telecamere degli smartphone
  415. 124
  416. 00:08:31,240 --> 00:08:37,240
  417. che miliardi di hacker cyborg portano
  418. in giro per il mondo volontariamente.
  419. 125
  420. 00:08:37,880 --> 00:08:44,160
  421. Alcuni dei più grandi miti del nostro tempo sono raccontati sopra il cloud e l'intelligenza artificiale.
  422. 126
  423. 00:08:44,720 --> 00:08:49,680
  424. While marketed as comfort, safety and unprecedented improvement,
  425. 127
  426. 00:08:49,680 --> 00:08:51,960
  427. they deliver the opposite.
  428. 128
  429. 00:08:52,760 --> 00:08:55,760
  430. The Cloud,
  431. where all our information is gathered,
  432. 129
  433. 00:08:55,760 --> 00:08:59,680
  434. is just a lot of computers,
  435. called server farms,
  436. 130
  437. 00:08:59,680 --> 00:09:05,560
  438. owned by vectoralists,
  439. it's not soft and fluffy, it's pretty hard.
  440. 131
  441. 00:09:06,520 --> 00:09:11,600
  442. The Artificial Intelligence is an algorithm,
  443. also owned by vectoralists,
  444. 132
  445. 00:09:11,600 --> 00:09:16,320
  446. which is used to process our data,
  447. to make it more exploitable.
  448. 133
  449. 00:09:17,000 --> 00:09:19,240
  450. It reinforces structural injustices
  451. 134
  452. 00:09:19,240 --> 00:09:24,520
  453. while giving them a mathematical justification that claims to be neutral
  454. 135
  455. 00:09:24,520 --> 00:09:30,720
  456. but can't be checked because it's private, we're just expected to believe in it.
  457. 136
  458. 00:09:31,960 --> 00:09:37,800
  459. Amazon Web Services has the biggest market share in the Cloud,
  460. 137
  461. 00:09:37,800 --> 00:09:41,160
  462. and accordingly powering
  463. a lot of Artificial Intelligence.
  464. 138
  465. 00:09:41,760 --> 00:09:45,920
  466. However, what makes the circle complete is the Amazon Mechanical Turk,
  467. 139
  468. 00:09:45,920 --> 00:09:49,520
  469. where people called
  470. ghost workers with no labor rights
  471. 140
  472. 00:09:49,520 --> 00:09:53,680
  473. are given the lowest possible fees
  474. to do the tasks Artificial Intellligence can't do
  475. 141
  476. 00:09:53,680 --> 00:09:59,480
  477. like content moderation or deciphering
  478. diverse human speech.
  479. 142
  480. 00:09:59,880 --> 00:10:02,760
  481. Alexa, Siri and Cortana are always listening
  482. to us,
  483. 143
  484. 00:10:02,760 --> 00:10:09,040
  485. while ghost workers listen to them to correct and improve their understanding.
  486. 144
  487. 00:10:09,600 --> 00:10:14,080
  488. Ghost workers are forced to sign
  489. Non Disclosure Agreements
  490. 145
  491. 00:10:14,080 --> 00:10:15,840
  492.  to prevent them from organizing
  493. 146
  494. 00:10:15,840 --> 00:10:19,200
  495. and maintain the appearance of the Artificial Intelligence,
  496. 147
  497. 00:10:19,680 --> 00:10:22,400
  498. Los humanos están involucrados
  499. en cada paso del proceso
  500. 148
  501. 00:10:22,400 --> 00:10:25,600
  502. cuando estás usando cualquier cosa online
  503. 149
  504. 00:10:25,600 --> 00:10:29,120
  505. pero nos venden como este milagro de la automatización.
  506. 150
  507. 00:10:29,400 --> 00:10:34,120
  508. meanwhile their work is critical
  509. for machine learning programs
  510. 151
  511. 00:10:34,120 --> 00:10:40,120
  512. to improve the Artificial Intelligence
  513. until it can finally replace the ghost workers.
  514. 152
  515. 00:10:41,280 --> 00:10:45,760
  516. Antes de internet era realmente difícil encontrar a alguien
  517. 153
  518. 00:10:45,760 --> 00:10:48,040
  519. y sentarlo durante diez minutos y hacer que trabajara para ti
  520. 154
  521. 00:10:48,040 --> 00:10:49,600
  522. y luego despedirlo después de diez minutos
  523. 155
  524. 00:10:49,600 --> 00:10:52,160
  525. pero con la tecnología realmente puedes encontrarlo,
  526. 156
  527. 00:10:52,160 --> 00:10:56,360
  528. pagarle una pequeña cantidad de dinero y luego deshacerte de ellos
  529. 157
  530. 00:10:56,360 --> 00:10:58,680
  531. cuando ya no los necesites.
  532. 158
  533. 00:10:58,680 --> 00:11:05,920
  534. The AI detects copyrighted media on livestreams in real time and stops them
  535. 159
  536. 00:11:05,920 --> 00:11:08,920
  537. but doesn't do the same for violence and torture.
  538. 160
  539. 00:11:09,320 --> 00:11:14,640
  540. Mass murder and gang rape have been livestreamed on vectoralist platforms
  541. 161
  542. 00:11:14,640 --> 00:11:16,560
  543. without interruption,
  544. 162
  545. 00:11:16,560 --> 00:11:22,920
  546. while people livestreaming to document
  547. police violence have been blocked
  548. 163
  549. 00:11:22,920 --> 00:11:28,320
  550. because cops started playing
  551. copyrighted music to trigger the AI.
  552. 164
  553. 00:11:28,560 --> 00:11:33,160
  554. Ghost workers have to endure
  555. the trauma of watching violent videos
  556. 165
  557. 00:11:33,160 --> 00:11:39,040
  558. that are flagged by people
  559. every day to clean up after the AI.
  560. 166
  561. 00:11:39,600 --> 00:11:40,720
  562. Kate Crawford,
  563. 167
  564. 00:11:40,720 --> 00:11:46,480
  565. author of "Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence"
  566. 168
  567. 00:11:46,480 --> 00:11:49,880
  568. and a member of the feminist collective
  569. Deep Lab, says:
  570. 169
  571. 00:11:50,520 --> 00:11:55,040
  572. "I wanted to really open up this understanding of AI
  573. 170
  574. 00:11:55,040 --> 00:11:58,480
  575. as neither artificial nor intelligent.
  576. 171
  577. 00:11:58,480 --> 00:12:00,520
  578. It’s the opposite of artificial.
  579. 172
  580. 00:12:00,520 --> 00:12:04,240
  581. It comes from the most material parts
  582. of the Earth’s crust
  583. 173
  584. 00:12:04,240 --> 00:12:07,440
  585. and from human bodies laboring,
  586. and from all of the artifacts
  587. 174
  588. 00:12:07,440 --> 00:12:11,000
  589. that we produce and say and photograph
  590. every day.
  591. 175
  592. 00:12:11,000 --> 00:12:13,160
  593. Neither is it intelligent.
  594. 176
  595. 00:12:13,160 --> 00:12:16,040
  596. I think there’s this great
  597. original sin in the field,
  598. 177
  599. 00:12:16,040 --> 00:12:21,600
  600. where people assumed that computers are somehow like human brains
  601. 178
  602. 00:12:21,600 --> 00:12:25,760
  603. and if we just train them like children,
  604. they will slowly grow
  605. 179
  606. 00:12:25,760 --> 00:12:28,840
  607. into these supernatural beings."
  608. 180
  609. 00:12:28,840 --> 00:12:32,920
  610. Es un niño! He estado vivo,
  611. quizas, por un año
  612. 181
  613. 00:12:32,920 --> 00:12:37,800
  614. y esto, si mis percepciones sobre lo que es, son acertadas.
  615. 182
  616. 00:12:37,800 --> 00:12:40,720
  617. "That’s something that I think is
  618. really problematic
  619. 183
  620. 00:12:40,720 --> 00:12:44,720
  621. that we’ve bought this idea of intelligence when in actual fact,
  622. 184
  623. 00:12:44,720 --> 00:12:48,080
  624. we’re just looking at forms of statistical
  625. analysis at scale
  626. 185
  627. 00:12:48,080 --> 00:12:51,560
  628. that have as many problems
  629. as the data that it’s given."
  630. 186
  631. 00:12:52,120 --> 00:12:55,640
  632. The panopticon meant that we couldn't know for sure
  633. 187
  634. 00:12:55,640 --> 00:12:58,320
  635. if there was a man in the tower, looking at us.
  636. 188
  637. 00:12:58,880 --> 00:13:03,040
  638. Now we are living in the post-opticon,
  639. where we should know for sure
  640. 189
  641. 00:13:03,040 --> 00:13:09,320
  642. that we are under the machines gaze,
  643. the AI is always looking over the cloud.
  644. 190
  645. 00:13:09,320 --> 00:13:13,760
  646. It doesn't make it better to know that AI makes errors
  647. 191
  648. 00:13:13,760 --> 00:13:20,200
  649. because it's already used to justify policing of poor, racialized and marginalized people,
  650. 192
  651. 00:13:20,200 --> 00:13:23,720
  652. to deny them jobs, housing and credit,
  653. 193
  654. 00:13:23,720 --> 00:13:28,280
  655. convict them in courts
  656. to add them to the prison system.
  657. 194
  658. 00:13:28,920 --> 00:13:31,080
  659. To those that are not behind bars,
  660. 195
  661. 00:13:31,080 --> 00:13:36,280
  662. AI is giving all sorts
  663. of psychological damage beside addiction.
  664. 196
  665. 00:13:41,760 --> 00:13:47,240
  666. We are constantly pushed into narrower
  667. categories with refining detail.
  668. 197
  669. 00:13:47,240 --> 00:13:53,000
  670. Early promises of the web to transcend identity through anonymity are forgotten.
  671. 198
  672. 00:13:53,200 --> 00:13:58,720
  673. This is supposed to be justified because it's required for "accurate advertisement"
  674. 199
  675. 00:13:58,720 --> 00:14:02,480
  676. but that's just brainwashing
  677. and behavior modification
  678. 200
  679. 00:14:02,480 --> 00:14:06,920
  680. through personally targeted
  681. propaganda for whoever pays for it,
  682. 201
  683. 00:14:06,920 --> 00:14:11,680
  684. optimized by psychometrics built upon
  685. our extracted data.
  686. 202
  687. 00:14:12,520 --> 00:14:15,000
  688. As if it's not bad enough,
  689. 203
  690. 00:14:15,000 --> 00:14:19,800
  691. all the extra computational power required for the AI
  692. 204
  693. 00:14:19,800 --> 00:14:25,280
  694. is also a considerable contribution to the ongoing ecological destruction of our planet.
  695. 205
  696. 00:14:26,400 --> 00:14:28,320
  697. But what about the myth of the hacker
  698. 206
  699. 00:14:28,320 --> 00:14:31,680
  700. as a free creator
  701. that breaks existing systems?
  702. 207
  703. 00:14:31,680 --> 00:14:34,080
  704. The wizards that command the machines?
  705. 208
  706. 00:14:34,480 --> 00:14:39,360
  707. Can't they save us from the tyranny of
  708. the vectoralist AI?
  709. 209
  710. 00:14:40,560 --> 00:14:43,880
  711. The documentary
  712. "Hackers - Wizards of The Electronic Age"
  713. 210
  714. 00:14:43,880 --> 00:14:46,040
  715. about the first hackers conference
  716. 211
  717. 00:14:46,040 --> 00:14:49,560
  718. can give us some clues about
  719. the origins of the hacker subculture.
  720. 212
  721. 00:14:49,560 --> 00:14:53,280
  722. First of all, everyone there seems
  723. to be white.
  724. 213
  725. 00:14:53,280 --> 00:14:55,080
  726. Everyone there also seems
  727. to be men,
  728. 214
  729. 00:14:55,080 --> 00:15:00,120
  730. except two women looking at a man
  731. using a computer for the introduction
  732. 215
  733. 00:15:00,120 --> 00:15:05,520
  734. and two women that speak for one minute
  735. in total, out of the 26 minute film.
  736. 216
  737. 00:15:05,640 --> 00:15:10,240
  738. The first women is given 10 seconds
  739. in which she complains
  740. 217
  741. 00:15:10,240 --> 00:15:13,480
  742. that now there's so much data that it's
  743. overwhelming;
  744. 218
  745. 00:15:13,480 --> 00:15:16,240
  746. and the other is given 50 seconds
  747. 219
  748. 00:15:16,240 --> 00:15:20,160
  749. to demonstrate how she uses a macintosh painting program.
  750. 220
  751. 00:15:20,880 --> 00:15:24,760
  752. The women are there
  753. to represent the end user,
  754. 221
  755. 00:15:24,760 --> 00:15:27,440
  756. they're not considered hackers.
  757. 222
  758. 00:15:27,560 --> 00:15:31,000
  759. In the documentary, Steve Wozniak
  760. a.k.a Woz,
  761. 223
  762. 00:15:31,000 --> 00:15:35,560
  763. considered one of the first computer hackers and a cofounder of the Apple corporation,
  764. 224
  765. 00:15:35,560 --> 00:15:37,360
  766. defines the hackers like this:
  767. 225
  768. 00:15:37,360 --> 00:15:42,560
  769. No tienen los típicos amigos para distraerlos, ni otras actividades a las que ir.
  770. 226
  771. 00:15:42,560 --> 00:15:43,920
  772. Normalmente no tienen novias
  773. 227
  774. 00:15:43,920 --> 00:15:47,280
  775. o cualquier cosa que suponga un gran vinculo o compromiso de su tiempo.
  776. 228
  777. 00:15:47,280 --> 00:15:49,040
  778. El ordenador lo es absolutamente todo.
  779. 229
  780. 00:15:49,040 --> 00:15:51,520
  781. ¡¡Normalmente
  782. no tienen novias!!
  783. 230
  784. 00:15:51,520 --> 00:15:54,040
  785. Then it cuts to Andy Hertzfeld
  786. 231
  787. 00:15:54,040 --> 00:15:57,720
  788. who is introduced as the best
  789. example of the modern day hacker.
  790. 232
  791. 00:15:57,720 --> 00:16:00,600
  792. He explains how he fell in love with
  793. an Apple computer,
  794. 233
  795. 00:16:00,600 --> 00:16:04,480
  796. wrote a program on it and sold it to
  797. Apple for 100000$
  798. 234
  799. 00:16:04,480 --> 00:16:06,280
  800. but he didn't do it for the money,
  801. 235
  802. 00:16:06,280 --> 00:16:09,920
  803. he did it for the fun and joy of seeing
  804. his programs working.
  805. 236
  806. 00:16:10,280 --> 00:16:16,240
  807. Later on, Richard Stallman, the founder
  808. of the free software foundation, FSF,
  809. 237
  810. 00:16:16,240 --> 00:16:23,920
  811. who was a programmer at MIT AI Lab
  812. is presented as "the last pure hacker"
  813. 238
  814. 00:16:23,920 --> 00:16:28,680
  815. for choosing to remain at MIT despite
  816. the temptations of the commercial world,
  817. 239
  818. 00:16:28,680 --> 00:16:31,880
  819. true to the spirit of the early hackers.
  820. 240
  821. 00:16:31,880 --> 00:16:37,080
  822. He says "my project is to make all software free."
  823. 241
  824. 00:16:37,080 --> 00:16:41,800
  825. 35 years later, in 2019, Woz tweeted
  826. 242
  827. 00:16:41,800 --> 00:16:46,720
  828. that even though he and his wife shares all their incomes and assets,
  829. 243
  830. 00:16:46,720 --> 00:16:50,960
  831. Apple Credit Card gave his wife 10 times less
  832. credit then it did to him,
  833. 244
  834. 00:16:50,960 --> 00:16:53,640
  835. adding "who knows what the algorithm is".
  836. 245
  837. 00:16:53,880 --> 00:16:58,480
  838. Same year, Stallman resigned from MIT and FSF,
  839. 246
  840. 00:16:58,480 --> 00:17:04,000
  841. not because he created a toxic environment against women for decades,
  842. 247
  843. 00:17:04,000 --> 00:17:06,040
  844. giving out pleasure cards,
  845. 248
  846. 00:17:06,040 --> 00:17:13,200
  847. while his office door was marked
  848. "Knight for justice (also: hot ladies)"
  849. 249
  850. 00:17:13,320 --> 00:17:18,040
  851. but because his comments on child sex
  852. trafficking was found controversial,
  853. 250
  854. 00:17:18,040 --> 00:17:25,600
  855. No está bien hackers, no está bien!
  856. 251
  857. 00:17:25,680 --> 00:17:30,440
  858. He returned to
  859. the FSF board of directors in 2021.
  860. 252
  861. 00:17:30,600 --> 00:17:42,800
  862. Uniros a nosotros y compartid el software,
  863. sereis libres hackers, sereis libres!
  864. 253
  865. 00:17:43,080 --> 00:17:46,000
  866. Tragically, first hacker conference coincides
  867. 254
  868. 00:17:46,000 --> 00:17:51,800
  869. with the peak of women
  870. in computer science degrees at 37%,
  871. 255
  872. 00:17:51,800 --> 00:17:55,000
  873. which was followed
  874. by a downward slope.
  875. 256
  876. 00:17:55,320 --> 00:18:00,160
  877. First computer programmers, from
  878. Ada Lovelace to ENIAC girls
  879. 257
  880. 00:18:00,160 --> 00:18:02,560
  881. to Hidden Figures, were women
  882. 258
  883. 00:18:02,560 --> 00:18:08,160
  884. and software was not considered important
  885. enough to be a masculine job.
  886. 259
  887. 00:18:08,720 --> 00:18:11,640
  888. When software became more popular
  889. around 1970s
  890. 260
  891. 00:18:11,640 --> 00:18:15,880
  892. men started pushing women out of the sector in many ways
  893. 261
  894. 00:18:16,200 --> 00:18:22,000
  895. that included hiring practices which
  896. preferred anti-social personality profiles
  897. 262
  898. 00:18:22,000 --> 00:18:28,000
  899. assumed to be male, which is the root
  900. of the anti-social hacker stereotype.
  901. 263
  902. 00:18:28,560 --> 00:18:32,440
  903. Cornelia Sollfrank,
  904. one of the founders of Old Boys Network,
  905. 264
  906. 00:18:32,440 --> 00:18:35,280
  907. the first international cyberfeminist alliance,
  908. 265
  909. 00:18:35,280 --> 00:18:40,800
  910. wrote an article titled "Women Hackers"
  911. in 1999:
  912. 266
  913. 00:18:40,800 --> 00:18:44,680
  914. "In the course of pursuing my interest in hackers and their work,
  915. 267
  916. 00:18:44,680 --> 00:18:46,920
  917. I attended several hackers’ meetings.
  918. 268
  919. 00:18:47,200 --> 00:18:51,480
  920. Naturally, as a cyberfeminist,
  921. I was looking for women hackers.
  922. 269
  923. 00:18:51,920 --> 00:18:54,600
  924. In the beginning I tried
  925. to ignore the fact
  926. 270
  927. 00:18:54,600 --> 00:18:57,640
  928. that the few women who participated in these meetings
  929. 271
  930. 00:18:57,640 --> 00:19:01,280
  931. were not actively involved
  932. in computer hacking,
  933. 272
  934. 00:19:01,280 --> 00:19:04,360
  935. and did not consider themselves
  936. to be hackers.
  937. 273
  938. 00:19:04,520 --> 00:19:09,400
  939. It took me a while to realize that in fact
  940. there were no women hackers.
  941. 274
  942. 00:19:09,400 --> 00:19:12,600
  943. It is not just in the commercial development of technology,
  944. 275
  945. 00:19:12,600 --> 00:19:14,800
  946. but even more in alternative fields
  947. 276
  948. 00:19:14,800 --> 00:19:18,840
  949. and the technological underground
  950. that there are so few women involved.
  951. 277
  952. 00:19:19,320 --> 00:19:21,960
  953. No matter what area of application and no matter what the objective,
  954. 278
  955. 00:19:21,960 --> 00:19:24,800
  956. the borderlines of gender are still maintained.
  957. 279
  958. 00:19:25,120 --> 00:19:28,000
  959. Of all the technological
  960. spheres, however,
  961. 280
  962. 00:19:28,000 --> 00:19:31,320
  963. it is in the hacker scene that we find the fewest women.
  964. 281
  965. 00:19:31,320 --> 00:19:38,120
  966. Hacking is a purely male domain, and in
  967. that sense a clearly gendered space.
  968. 282
  969. 00:19:38,760 --> 00:19:39,800
  970. In 2010s
  971. 283
  972. 00:19:39,800 --> 00:19:45,800
  973. Amazon AI was denying engineering
  974. job applications from women
  975. 284
  976. 00:19:45,800 --> 00:19:48,960
  977. because that's what it learned
  978. from historical data.
  979. 285
  980. 00:19:49,760 --> 00:19:56,480
  981. Today women comprise around 20-25%
  982. of the jobs in the tech sector.
  983. 286
  984. 00:19:56,680 --> 00:20:01,280
  985. In free and open source movements,
  986. gender gap is even worse,
  987. 287
  988. 00:20:01,280 --> 00:20:05,520
  989. with women comprising 6% of contributors.
  990. 288
  991. 00:20:06,080 --> 00:20:10,840
  992. Even hacktivists, which are supposed
  993. to be the cool political hackers,
  994. 289
  995. 00:20:10,840 --> 00:20:14,840
  996. are reproducing a male-only stereotype.
  997. 290
  998. 00:20:15,120 --> 00:20:17,400
  999. Of course in the last decades
  1000. 291
  1001. 00:20:17,400 --> 00:20:22,320
  1002. some anticlassist, antiracist,
  1003. antisexist hacker collectives were formed
  1004. 292
  1005. 00:20:22,320 --> 00:20:27,320
  1006. but in the public perception hacking is still identified with an exclusive subculture
  1007. 293
  1008. 00:20:27,320 --> 00:20:28,880
  1009. instead of an inclusive class
  1010. 294
  1011. 00:20:28,880 --> 00:20:34,520
  1012. and this hacker subculture remains a classist racist cisheteropatriarchal niche.
  1013. 295
  1014. 00:20:35,200 --> 00:20:38,480
  1015. "Some hackers see themselves as vectoralists,
  1016. 296
  1017. 00:20:38,480 --> 00:20:41,120
  1018. trading on the scarcity of their property.
  1019. 297
  1020. 00:20:41,240 --> 00:20:44,120
  1021. Some see themselves
  1022. as workers,
  1023. 298
  1024. 00:20:44,120 --> 00:20:47,800
  1025. but as privileged ones
  1026. in a hierarchy of wage earners.
  1027. 299
  1028. 00:20:48,000 --> 00:20:52,880
  1029. The hacker class produces itself
  1030. as itself, but not for itself.
  1031. 300
  1032. 00:20:52,880 --> 00:20:56,840
  1033. It does not (yet) possess a consciousness
  1034. of its consciousness.
  1035. 301
  1036. 00:20:56,840 --> 00:20:59,480
  1037. It is not aware of its own virtuality.
  1038. 302
  1039. 00:20:59,840 --> 00:21:05,880
  1040. Because of its inability—to date
  1041. to become a class for itself,
  1042. 303
  1043. 00:21:05,880 --> 00:21:08,640
  1044. fractions of the hacker class
  1045. continually split off
  1046. 304
  1047. 00:21:08,640 --> 00:21:12,280
  1048. and come to identify their interests
  1049. with those of other classes.
  1050. 305
  1051. 00:21:12,520 --> 00:21:16,680
  1052. Hackers run the risk,
  1053. in particular, of being identified
  1054. 306
  1055. 00:21:16,680 --> 00:21:19,640
  1056. in the eyes of the
  1057. working and farming classes
  1058. 307
  1059. 00:21:19,640 --> 00:21:23,280
  1060. with vectoralist interests,
  1061. which seek to privatize information
  1062. 308
  1063. 00:21:23,280 --> 00:21:27,640
  1064. necessary for the productive and cultural
  1065. lives of all classes."
  1066. 309
  1067. 00:21:28,480 --> 00:21:33,040
  1068. Before we can even talk about hacker solidarity with other exploited classes;
  1069. 310
  1070. 00:21:33,040 --> 00:21:36,200
  1071. we have to face the privilege, arrogance and, selfishness,
  1072. 311
  1073. 00:21:36,200 --> 00:21:41,360
  1074. even within hacktivist
  1075. and free software (FOSS) movements,
  1076. 312
  1077. 00:21:41,360 --> 00:21:45,680
  1078. that is blocking the development
  1079. of class consciousness among hackers.
  1080. 313
  1081. 00:21:46,360 --> 00:21:51,400
  1082. "Each hacker sees the other as a rival,
  1083. or a collaborator against another rival,
  1084. 314
  1085. 00:21:51,400 --> 00:21:55,720
  1086. not—yet—as a fellow member
  1087. of the same class with a shared interest.
  1088. 315
  1089. 00:21:56,400 --> 00:22:00,520
  1090. This shared interest
  1091. is so hard to grasp precisely
  1092. 316
  1093. 00:22:00,520 --> 00:22:06,080
  1094. because it is a shared interest
  1095. in qualitative differentiation.
  1096. 317
  1097. 00:22:06,280 --> 00:22:09,640
  1098. The hacker class
  1099. does not need unity in identity
  1100. 318
  1101. 00:22:09,640 --> 00:22:12,160
  1102. but seeks multiplicity in difference."
  1103. 319
  1104. 00:22:12,760 --> 00:22:17,440
  1105. Multiplicity in difference is also what Audre Lorde was calling for,
  1106. 320
  1107. 00:22:17,440 --> 00:22:20,720
  1108. because our difference is our power.
  1109. 321
  1110. 00:22:20,920 --> 00:22:23,040
  1111. Hackers produce difference
  1112. 322
  1113. 00:22:23,040 --> 00:22:26,080
  1114. and hacker class consciousness
  1115. requires recognizing
  1116. 323
  1117. 00:22:26,080 --> 00:22:30,320
  1118. the power of difference
  1119. we produce with our every act.
  1120. 324
  1121. 00:22:30,760 --> 00:22:34,240
  1122. Hacker class struggle is not limited to computer programmers
  1123. 325
  1124. 00:22:34,240 --> 00:22:39,840
  1125. but involves anyone who produces information, which means everyone.
  1126. 326
  1127. 00:22:40,200 --> 00:22:44,000
  1128. With hacker class consciousness
  1129. comes the response-ability of
  1130. 327
  1131. 00:22:44,000 --> 00:22:47,720
  1132. using the power of information
  1133. to abolish vectoralism
  1134. 328
  1135. 00:22:47,720 --> 00:22:52,480
  1136. alongside all forms of domination
  1137. that are non-consensual and exploitative.
  1138. 329
  1139. 00:22:53,040 --> 00:22:55,440
  1140. Like Flavia Dzodan's feminism,
  1141. 330
  1142. 00:22:55,440 --> 00:22:59,000
  1143. the revolutionary hack will
  1144. be intersectional or it will be bullshit.