The Billion Dollar Code

Summary: In 1990s Berlin, an artist and a hacker invented a new way to see the world. Years later, they reunite to sue Google for patent infringement on it.

Release Date: October 07, 2021

Genres: Drama, Crime, Biography

Runtime: 1 h 07 min

MPAA: TV-MA

Production: Kundschafter Filmproduktion, Sunny Side Up Films, Sunny Side Up

Country: Germany

Language: German

Adaptation/remake:

based on true story

N/A

The average Rotten Tomatoes audience score is 97%.

97%

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28%24%

28% of the Stars & Main Cast are Female.

24% of the Stars & Main Cast are "diverse."

White76.47%Latino11.76%Asian5.88%Arab5.88%

N/A

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CONTENT BREAKDOWN

Methodology

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97%

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Race	White	Latino	Arab	Asian	Black	Total	Visuals	Info
Kairos	10	2		1		13		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.kairos.com/demos
Forebears	2		2		1	5
Familysearch	1					1
Surname	1					1		A third party free program that uses US Census data to create an algorithm that analyzes surnames and guesses what ethnicity the person is. Obviously there are issues with this data set such as African Americans who have the last names of their ancestors' slave owner. However, when no other data set is available, this can give a rough estimate and expand the data pool for the less important cast members with no IMDb or TMDb image. Please consider downloading their source code via GitHub to verify the accuracy of our data set, and to peer review our conclusions. Source: https://pypi.org/project/ethnicolr/
Bettaface	1					1		A third party facial recognition software company we used to maintain objectivity. Upload images to the facial recognition demo page to check the veracity of our data set. Or use their API and do a bulk peer review if you'd like. Source: https://www.betafaceapi.com/demo_old.html
Total	15	2	2	1	1	21
Cast Percentages	71.43%	9.52%	9.52%	4.76%	4.76%	100%	{"name":"Cast Percentages","series":[{"name":"White","y":71.43,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":9.52,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Arab","y":9.52,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":4.76,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":4.76,"color":"#f45b5b","sliced":true,"selected":true}],"prefix":""}
U.S. Population (2020) Percentage	57.84%	18.73%	0.54%	6%	12.4%		{"name":"U.S. Population (2020)","series":[{"name":"White","y":57.84,"color":"#2b908f"},{"name":"Arab","y":0.54,"color":"#DF5353"},{"name":"Asian","y":6,"color":"#90ee7e"},{"name":"Black","y":12.4,"color":"#f45b5b"},{"name":"Indian","y":1.35,"color":"#7798BF"},{"name":"Indigenous","y":1.12,"color":"#aaeeee"},{"name":"Latino","y":18.73,"color":"#eeaaee"},{"name":"Mixed \/ Other","y":9.32,"color":"#55BF3B"},{"name":"Jewish","y":2.02,"color":"#ff0066"}],"prefix":""}	Source: US Census (Note: Racial and ethnic categories overlap so the sum will not equal 100%.)
U.S. Population (2020) Representation	+13.59	-9.21	+8.98	-1.24	-7.64		{"name":"U.S. Population (2020)","series":[["White",57.84],["Latino",18.73],["Arab",0.54],["Asian",6],["Black",12.4]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Latino","y":9.52,"color":"#eeaaee"},{"name":"Arab","y":9.52,"color":"#DF5353"},{"name":"Asian","y":4.76,"color":"#90ee7e"},{"name":"Black","y":4.76,"color":"#f45b5b"}]}
U.S. Buying Power (2023) Percentage	72.69%	10.06%		6.97%	8.02%		{"name":"U.S. Buying Power (2023)","series":[{"name":"White","y":13897000000000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1333100000000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1533100000000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":136400000000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1924000000000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":295300000000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: Selig Center, University of Georgia
U.S. Buying Power (2023) Representation	-1.26	-0.54		-2.21	-3.26		{"name":"U.S. Buying Power (2023)","series":[["White",72.69],["Latino",10.06],["Arab",0],["Asian",6.97],["Black",8.02]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Latino","y":9.52,"color":"#eeaaee"},{"name":"Arab","y":9.52,"color":"#DF5353"},{"name":"Asian","y":4.76,"color":"#90ee7e"},{"name":"Black","y":4.76,"color":"#f45b5b"}]}
U.S. Average Individual Income (2018) Percentage	65.34%	9.51%		7.52%	9.43%		{"name":"U.S. Average Individual Income (2018)","series":[{"name":"White","y":11825174998483,"color":"#2b908f","sliced":true,"selected":true},{"name":"Arab","y":0,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":1360705388565,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Black","y":1706765993728,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indian","y":0,"color":"#7798BF","sliced":true,"selected":true},{"name":"Indigenous","y":164296821776,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Latino","y":1721419354421,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":1318328353188,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Jewish","y":0,"color":"#ff0066","sliced":true,"selected":true}],"prefix":"$"}	Source: IPUMS-CPS, University of Minnesota
U.S. Average Individual Income (2018) Representation	+6.09	+0.01		-2.76	-4.67		{"name":"U.S. Average Individual Income (2018)","series":[["White",65.34],["Latino",9.51],["Arab",0],["Asian",7.52],["Black",9.43]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Latino","y":9.52,"color":"#eeaaee"},{"name":"Arab","y":9.52,"color":"#DF5353"},{"name":"Asian","y":4.76,"color":"#90ee7e"},{"name":"Black","y":4.76,"color":"#f45b5b"}]}
World population (2019) Percentage	14.24%	5.62%	8.82%	31.25%	14.47%		{"name":"World population (2019) ","series":[{"name":"Mixed \/ Other","y":14688882870,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Latino","y":42991259469,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Indigenous","y":10971786583,"color":"#aaeeee","sliced":true,"selected":true},{"name":"White","y":108889096410,"color":"#2b908f","sliced":true,"selected":true},{"name":"Black","y":110601821551,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Asian","y":238860161621,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Arab","y":67406299429,"color":"#DF5353","sliced":true,"selected":true},{"name":"Indian","y":167470202093,"color":"#7798BF","sliced":true,"selected":true},{"name":"Jewish","y":2568698093,"color":"#ff0066","sliced":true,"selected":true}],"prefix":""}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World population (2019) Representation	+57.19	+3.9	+0.7	-26.49	-9.71		{"name":"World population (2019) ","series":[["White",14.24],["Latino",5.62],["Arab",8.82],["Asian",31.25],["Black",14.47]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Latino","y":9.52,"color":"#eeaaee"},{"name":"Arab","y":9.52,"color":"#DF5353"},{"name":"Asian","y":4.76,"color":"#90ee7e"},{"name":"Black","y":4.76,"color":"#f45b5b"}]}
World Buying Power Percentage	36.48%	9.35%	6.38%	29.46%	6.38%		{"name":"World Buying Power","series":[{"name":"White","y":3010602822170000,"color":"#2b908f","sliced":true,"selected":true},{"name":"Latino","y":771525888400000,"color":"#eeaaee","sliced":true,"selected":true},{"name":"Jewish","y":91700962050000,"color":"#ff0066","sliced":true,"selected":true},{"name":"Black","y":526264226360000,"color":"#f45b5b","sliced":true,"selected":true},{"name":"Indigenous","y":174285727610000,"color":"#aaeeee","sliced":true,"selected":true},{"name":"Mixed \/ Other","y":148984454560000,"color":"#55BF3B","sliced":true,"selected":true},{"name":"Arab","y":526400066100000,"color":"#DF5353","sliced":true,"selected":true},{"name":"Asian","y":2431392083750000,"color":"#90ee7e","sliced":true,"selected":true},{"name":"Indian","y":572687873900000,"color":"#7798BF","sliced":true,"selected":true}],"prefix":"$"}	NOTE: ZR is the first organization to ever create a global buying power divided by race. Essentially we just took data from CIA.gov FactBook, UN Population Estimates, and The World Bank's Population Estimates / Purchasing Power Parity Per Capita.
World Buying Power Representation	+34.95	+0.17	+3.14	-24.7	-1.62		{"name":"World Buying Power","series":[["White",36.48],["Latino",9.35],["Arab",6.38],["Asian",29.46],["Black",6.38]],"cast":[{"name":"White","y":71.43,"color":"#2b908f"},{"name":"Latino","y":9.52,"color":"#eeaaee"},{"name":"Arab","y":9.52,"color":"#DF5353"},{"name":"Asian","y":4.76,"color":"#90ee7e"},{"name":"Black","y":4.76,"color":"#f45b5b"}]}
Notes		Many Latinos check "White" or "Other" on the Census. So this overlaps with "Mixed" race and "White." Also, when there was a discrepancy with the percentage listed on the Wikipedia table and our calculated numbers, the Wiki table's numbers were used. Likewise, when the population total was missing from the Census pdf, the estimate was calculated using the percentage from the Wiki table. NOTE: It turns out that the reason America doesn't have a separate "Latino" race is because the League of Latin American Citizens protested against discrimination towards Mexicans and got themselves to legally be considered "White." Check the research links in our Google Doc for more info.	Arab Americans are less than 1% of the US population and often check "White" or "Other" on the US Census. And the only data available for them started when the US Census added ancestry in 1980. So assume a less than 1% margin of error and overlap with the "White" and "Other" categories.	The US Census considers Pakistani, Sri Lankan, Indian, and Bangladeshi as Asians. At ZR we group Pakistanis with the Arab world. And we group Sri Lankan and Bangladeshi with India because 1.) they are all genetically similar as "South Asians and 2.) there are only a handful of actors from those regions in our database. However, you are viewing the "Asian Alone" category from the US Census when comparing it with Domestic representation. Also note that the 2000 and 2010 estimates include MultiRacial Asians not just "Asian Alone" estimates, and in some categories they include Native Hawaiians as "Asian." So there is a margin of error less than 1% and some inconsistencies with the grouping.

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