├── README.md ├── accumulator.py ├── bingscraper.py ├── doctor ├── doctor-tags.txt ├── doctorviz.png ├── flare.json └── index.html └── engineer ├── engineer-tags.txt ├── engineerviz.png ├── flare.json └── index.html /README.md: -------------------------------------------------------------------------------- 1 | #Clarifaing Gender Roles 2 | 3 | ###The Idea 4 | I wanted to play around with the Clarifai API, but had no long-term project in mind. As an advocate for women in tech, I wanted to see if the gender disparity in STEM fields could be quantified from Bing, [the image search engine used by one in five people](http://money.cnn.com/2015/04/16/technology/bing-usage/). 5 | 6 | ###The Implementation 7 | I coded a Python script that uses the Bing Cognitive Search API to find the first 150 hits from a given search term, and then save the content URL for each hit in a text file. I created another Python script that tags every uncorrupted image using the Clarifai Python API, and builds a dictionary of tags and frequencies. I use d3.js to [visualize the results as bubbles](http://bl.ocks.org/mbostock/4063269) such that tag frequency is equated to bubble area. 8 | 9 | This code has been abstracted for use with any search term, so feel free to clone/star this repository to try it out! 10 | 11 | ###Quick Caveat 12 | Machine learning and artificial intelligence technologies *cannot* define someone's gender identity. The results that follow give the Clarifai API's best guesses of who is seen each image, which is inherently biased by training data. For more on AI bias, check out [this article from "Model View Culture"](https://modelviewculture.com/pieces/the-hidden-dangers-of-ai-for-queer-and-trans-people). 13 | 14 | ###First Results: "Engineer" 15 | In the first 150 Bing Image Search results for the term "engineer," the Clarifai API counts 116 images tagged with "man" versus 33 tagged with "woman." "Man" was also the most frequent tag. Yikes. 16 | 17 | ![Who is an engineer?](https://raw.githubusercontent.com/alainakafkes/clarifaing-gender-roles/master/engineer/engineerviz.png) 18 | 19 | Keep in mind that this is what any Internet user sees when they search Bing for "engineer" -- mostly men. There is an evident lack of diversity in many engineering fields, so I believe that search results should strive to be gender-balanced to show girls & young women that they can and should exist in these spaces. 20 | 21 | ###"Doctor" 22 | "Man" (114 tags) still outnumber "woman" (73 tags), but to a much lesser extent. Unfortunately, even the tag "stethoscope" appears more than "woman." Other notable, frequent Clarifai tags include "isolated" and "fine-looking." 😉 23 | 24 | ![Who is a doctor?](https://raw.githubusercontent.com/alainakafkes/clarifaing-gender-roles/master/doctor/doctorviz.png) 25 | 26 | ###How To Contribute 27 | Create a pull request! ```bingscraper.py``` can be run in any Python 2 interpreter, whereas ```accumulator.py``` needs to be run in a Python 3 interpreter after [creating your first Clarifai app](https://developer.clarifai.com/) and by following the guidelines found [here](https://github.com/Clarifai/clarifai-python). 28 | 29 | **Comments? Questions? Suggestions for terms to explore? Feel free to share them with me.** 30 | -------------------------------------------------------------------------------- /accumulator.py: -------------------------------------------------------------------------------- 1 | # Clarifai Image Tag Accumulator 2 | # to run, follow instructions on: https://github.com/Clarifai/clarifai-python 3 | 4 | import json 5 | from collections import defaultdict 6 | from clarifai.client import ClarifaiApi 7 | clarifai_api = ClarifaiApi() 8 | 9 | # initialize tag dictionary for tags and their frequencies 10 | d = defaultdict(int) 11 | taglist = [] 12 | iter = 0 13 | 14 | # read and process each url 15 | with open("imglist.txt") as f: 16 | imglist = f.readlines() 17 | 18 | # get tags using clarifai, add tags to taglist 19 | for line in imglist: 20 | result = clarifai_api.tag_image_urls(line.rstrip('\n')) 21 | tags = result['results'][0]['result']['tag']['classes'] 22 | for i in range(0,len(tags)): 23 | if type(tags[i]) is str: 24 | taglist.append(tags[i]) 25 | print("next iter:" + str(iter)) 26 | iter += 1 27 | 28 | # convert taglist to dictionary 29 | for x in range(0,len(taglist)): 30 | d[taglist[x]] += 1 31 | 32 | # print dictionary 33 | for w in sorted(d, key=d.get, reverse=True): 34 | print(w, d[w]) 35 | -------------------------------------------------------------------------------- /bingscraper.py: -------------------------------------------------------------------------------- 1 | # Bing Image Scraper 2 | 3 | with open("bingsecrets.txt") as f: 4 | secrets = f.readlines() 5 | account_key = secrets[0].rstrip('\n') 6 | 7 | import urllib 8 | import requests 9 | from requests.auth import HTTPBasicAuth 10 | import json 11 | import random 12 | 13 | def get_pics(keyword="engineer"): 14 | """ amass pictures by keyword """ 15 | 16 | # search url obtained from bing api 17 | url = "https://api.cognitive.microsoft.com/bing/v5.0/images/search?q=" + keyword + "&count=150&offset=0&mkt=en-us&safeSearch=Moderate" 18 | 19 | # get ~authorized~ 20 | headers = {'Ocp-Apim-Subscription-Key': account_key} 21 | 22 | # get response from search url 23 | response_data = requests.get(url, headers=headers) 24 | 25 | # decode json-formatted response & save as pomeranians.json 26 | json_result = response_data.json() 27 | json_pretty = json.dumps(json_result, indent=4, sort_keys=True) 28 | with open("results.json","w") as outfile: 29 | outfile.write(json_pretty) 30 | outfile.close() 31 | 32 | # create list of content urls 33 | with open("imglist.txt","w") as outfile: 34 | for i in range(0,150): 35 | outfile.write(str(json_result['value'][i]['contentUrl'])+"\n") 36 | outfile.close() 37 | 38 | return "complete!" 39 | -------------------------------------------------------------------------------- /doctor/doctor-tags.txt: -------------------------------------------------------------------------------- 1 | doctor 121 2 | man 114 3 | healthcare 108 4 | medicine 102 5 | hospital 96 6 | health 84 7 | stethoscope 84 8 | woman 73 9 | uniform 73 10 | adult 71 11 | people 69 12 | isolated 65 13 | medical 65 14 | friendly 63 15 | diagnosis 63 16 | professional 59 17 | clinic 58 18 | indoors 58 19 | aid 52 20 | practitioner 46 21 | nurse 42 22 | business 40 23 | emergency 39 24 | looking 36 25 | one 32 26 | medical practitioner 32 27 | portrait 32 28 | success 31 29 | outerwear 26 30 | care 25 31 | surgeon 25 32 | fine-looking 24 33 | patient 24 34 | science 23 35 | wear 23 36 | young 22 37 | trust 21 38 | facial expression 20 39 | medical instrument 18 40 | treatment 18 41 | contemporary 18 42 | sit 17 43 | student 15 44 | cure 14 45 | two 14 46 | profession 13 47 | office 12 48 | technology 12 49 | teamwork 12 50 | cute 11 51 | desktop 11 52 | togetherness 11 53 | music 11 54 | room 11 55 | child 11 56 | dark 9 57 | illness 9 58 | fashion 9 59 | sketch 9 60 | person 8 61 | intelligence 8 62 | serious 8 63 | surgery 8 64 | group 8 65 | light 7 66 | work 7 67 | illustration 7 68 | art 7 69 | ailment 7 70 | character 7 71 | computer 7 72 | funny 7 73 | elderly 7 74 | casual 6 75 | scrutiny 6 76 | internet 6 77 | tie 6 78 | worker 6 79 | sitting 6 80 | suit 5 81 | outdoors 5 82 | connection 5 83 | security 5 84 | happiness 5 85 | musician 5 86 | service 5 87 | performance 5 88 | smoke 5 89 | eyeglasses 5 90 | retro 5 91 | research 5 92 | lid 5 93 | vector 5 94 | hand 4 95 | coat 4 96 | leader 4 97 | clipboard 4 98 | family 4 99 | offense 4 100 | outfit 4 101 | actor 4 102 | confidence 4 103 | squad 4 104 | cooperation 4 105 | education 4 106 | fun 4 107 | little 3 108 | pensive 3 109 | pretty 3 110 | love 3 111 | human 3 112 | attitude 3 113 | support 3 114 | industry 3 115 | practice 3 116 | singer 3 117 | pharmacist 3 118 | party 3 119 | concert 3 120 | graphic 3 121 | horizontal 3 122 | modern 3 123 | maternity 3 124 | safety 3 125 | length 2 126 | side view 2 127 | full 2 128 | crowd 2 129 | fantasy 2 130 | writing 2 131 | three 2 132 | elegant 2 133 | elder 2 134 | symbol 2 135 | festival 2 136 | formal 2 137 | 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secrecy 1 259 | band 1 260 | card 1 261 | screen 1 262 | 263 | -------------------------------------------------------------------------------- /doctor/doctorviz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alainakafkes/clarifaing-gender-roles/10f555da43d2773ec179c07880adaf0cd9732642/doctor/doctorviz.png -------------------------------------------------------------------------------- /doctor/flare.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "flare", 3 | "children": [ 4 | { 5 | "name": "data", 6 | "children": [ 7 | {"name": "doctor", "size": 121}, 8 | {"name": "man", "size": 114}, 9 | {"name": "healthcare", "size": 108}, 10 | {"name": "medicine", "size": 102}, 11 | {"name": "hospital", "size": 96}, 12 | {"name": "health", "size": 84}, 13 | {"name": "stethoscope", "size": 84}, 14 | {"name": "woman", "size": 73}, 15 | {"name": "uniform", "size": 73}, 16 | {"name": "adult", "size": 69}, 17 | {"name": "people", "size": 69}, 18 | {"name": "isolated", "size": 65}, 19 | {"name": "medical", "size": 65}, 20 | {"name": "friendly", "size": 63}, 21 | {"name": "diagnosis", "size": 63}, 22 | {"name": "professional", "size": 59}, 23 | {"name": "clinic", "size": 58}, 24 | {"name": "indoors", "size": 58}, 25 | {"name": "aid", "size": 52}, 26 | {"name": "practitioner", "size": 46}, 27 | {"name": "nurse", "size": 42}, 28 | {"name": "business", "size": 40}, 29 | {"name": "emergency", "size": 39}, 30 | {"name": "looking", "size": 36}, 31 | {"name": "one", "size": 32}, 32 | {"name": "medical practitioner", "size": 32}, 33 | {"name": "portrait", "size": 32}, 34 | {"name": "success", "size": 31} 35 | ] 36 | } 37 | ] 38 | } 39 | -------------------------------------------------------------------------------- /doctor/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 10 | 11 | 12 | 67 | -------------------------------------------------------------------------------- /engineer/engineer-tags.txt: -------------------------------------------------------------------------------- 1 | man 116 2 | people 106 3 | business 92 4 | adult 89 5 | industry 85 6 | technology 64 7 | helmet 55 8 | one 53 9 | safety 50 10 | expression 49 11 | indoors 42 12 | wear 40 13 | portrait 36 14 | outdoors 36 15 | woman 33 16 | equipment 30 17 | architect 27 18 | work 26 19 | illustration 26 20 | computer 26 21 | grinder 24 22 | hardhat 24 23 | lid 23 24 | vehicle 22 25 | sketch 20 26 | production 20 27 | security 20 28 | blueprint 20 29 | no person 20 30 | science 19 31 | employee 19 32 | isolated 19 33 | builder 19 34 | skill 18 35 | building 18 36 | connection 18 37 | military 16 38 | vector 16 39 | data 16 40 | machine 16 41 | professional 15 42 | education 15 43 | internet 15 44 | construction worker 14 45 | veil 14 46 | office 14 47 | competition 14 48 | power 13 49 | art 13 50 | two 13 51 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484 | Christmas 1 485 | money 1 486 | knob 1 487 | -------------------------------------------------------------------------------- /engineer/engineerviz.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/alainakafkes/clarifaing-gender-roles/10f555da43d2773ec179c07880adaf0cd9732642/engineer/engineerviz.png -------------------------------------------------------------------------------- /engineer/flare.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "flare", 3 | "children": [ 4 | { 5 | "name": "data", 6 | "children": [ 7 | {"name": "man", "size": 116}, 8 | {"name": "people", "size": 106}, 9 | {"name": "business", "size": 92}, 10 | {"name": "adult", "size": 89}, 11 | {"name": "industry", "size": 85}, 12 | {"name": "technology", "size": 64}, 13 | {"name": "helmet", "size": 55}, 14 | {"name": "one", "size": 53}, 15 | {"name": "safety", "size": 50}, 16 | {"name": "expression", "size": 49}, 17 | {"name": "indoors", "size": 42}, 18 | {"name": "wear", "size": 40}, 19 | {"name": "portrait", "size": 36}, 20 | {"name": "outdoors", "size": 36}, 21 | {"name": "woman", "size": 33}, 22 | {"name": "equipment", "size": 30}, 23 | {"name": "architect", "size": 27}, 24 | {"name": "work", "size": 26}, 25 | {"name": "illustration", "size": 26}, 26 | {"name": "computer", "size": 26}, 27 | {"name": "grinder", "size": 24}, 28 | {"name": "hardhat", "size": 24}, 29 | {"name": "lid", "size": 23}, 30 | {"name": "vehicle", "size": 22}, 31 | {"name": "sketch", "size": 20}, 32 | {"name": "production", "size": 20}, 33 | {"name": "security", "size": 20}, 34 | {"name": "blueprint", "size": 20}, 35 | {"name": "no person", "size": 20} 36 | ] 37 | } 38 | ] 39 | } 40 | -------------------------------------------------------------------------------- /engineer/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 10 | 11 | 12 | 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