ISSUE<\/p>\n
The entertainment industry fails to mirror our world both in front of and behind the camera. For example, the UCLA Hollywood Diversity Report illustrates that white people are overrepresented in lead roles in theatrical releases by 2x.<\/p>\n
https:\/\/socialsciences.ucla.edu\/wp-content\/uploads\/2019\/02\/UCLA-Hollywood-Diversity-Report-2019-2-21-2019.pdf<\/a><\/p>\n Behind the camera, it is a similar story. In fact, the USC Annenberg Inclusion Initiative found that while metacritic scores of films by male and female directors show no statistical difference, male directors outnumber female directors by 20 to 1 (http:\/\/assets.uscannenberg.org\/docs\/aii-inclusion-directors-chair-20200102.pdf<\/a>).<\/p>\n http:\/\/assets.uscannenberg.org\/docs\/aii-inclusion-directors-chair-20200102.pdf<\/a><\/p>\n AI SOLUTION<\/p>\n The Geena Davis Institute employs a multi-pronged approach towards remedying this deficiency, including GD-IQ: Spellcheck for Bias, developed in partnership with USC\u2019s Viterbi School of Engineering to help companies identify their own under- and misrepresentation of different cohorts. The tool identifies and quantifies the number of representations in video content, as well as the quality of characters across identities. Shri Narayanan, \u201cUSC engineering professor and director of Signal Analysis and Interpretation Laboratory (SAIL), said that the tool will use artificial intelligence technology to understand various aspects of the story that could reveal patterns of biases\u201d (http:\/\/www.uscannenbergmedia.com\/2020\/02\/25\/ai-technology-from-the-usc-viterbi-school-work-to-increase-representation-in-film-and-media\/<\/a>). The tool \u201crapidly tall[ies] the genders and ethnicities of characters\u2026 the number of speaking lines the various groups have, the level of sophistication of the vocabulary they use and the relative social status or positions of power assigned to the characters by group\u201d (https:\/\/www.hollywoodreporter.com\/news\/geena-davis-unveils-partnership-disney-spellcheck-scripts-gender-bias-1245347<\/a>). More specifically, the \u201cpatented machine learning tool\u201d identifies cohorts including \u201ccharacters who are people of color, LGBTQI, possess disabilities or belong to other groups typically underrepresented and failed by Hollywood storytelling\u201d (https:\/\/www.hollywoodreporter.com\/news\/geena-davis-unveils-partnership-disney-spellcheck-scripts-gender-bias-1245347<\/a>).<\/p>\n In the tool\u2019s first pilot, the Geena Davis Institute on Gender in Media partnered with Walt Disney Studios in late 2019 to assess early stage development (manuscript) development. As Davis says, \u201cI\u2019m very proud to announce we have a brand new partnership with Walt Disney Studios using Spell Check for Bias. They are our pilot partners and we\u2019re going to collaborate with Disney over the next year using this tool to help their decision-making, identify opportunities to increase diversity and inclusion to the manuscripts that they receive. We\u2019re very excited about the possibilities with this new technology and we encourage everybody to get in touch with us and give it a try\u201d (https:\/\/screenrant.com\/disney-movie-scripts-gender-bias-geena-davis\/<\/a>). By focusing on early stage development, Walt Disney Studios can flag issues in their content before spending more development money. And, by flagging early in the creative process, the course-correction can be fully integrated within the character and story development, instead of trying to shoehorn solutions in later, which may also seem inauthentic.<\/p>\n A few months later, the Geena Davis Institute on Gender in Media rolled out a second pilot, this time with Universal Entertainment Group to assess their Latinx representation. Universal Pictures president Peter Cramer lauded this partnership in the organization\u2019s \u201c\u2018strides throughout the production process towards more inclusive storytelling for all underrepresented groups, identifying additional resources to help improve and increase Latinx portrayals onscreen,\u2019\u201d (https:\/\/variety.com\/2020\/film\/news\/universal-geena-davis-usc-latinx-representation-1203507849\/<\/a>). They again focus on early stage development, aiming to promote \u201cmore authentic Latinx onscreen representation and casting opportunities\u201d (https:\/\/deadline.com\/2020\/02\/universal-geena-davis-institute-and-usc-spellcheck-for-bias-resource-latinx-representation-inclusion-diversity-1202862892\/<\/a>).<\/p>\n While USC and the Geena Davis Institute on Gender in Media are not releasing specific information on how the tool works, one can imagine that since it is a machine learning tool, that it will improve over time. Since machine learning tools can \u201cimprove[sic]\u2026over time based on additional data or new experiences,\u201d (HBS Case 9-619-014: Zebra Medical Vision) the more scripts it \u2018reads,\u2019 the better the tool will be at identifying and quantifying representations. And, the more studios and creative partners that engage with GD-IQ: Spellcheck for Bias, the better the tool will be.<\/p>\n RISKS AND CRITICISMS<\/p>\n As is the case with any coded tool, the machine learning tool itself is only as good as the artist\/programmer creating the code. And, specifically, for a tool that is built to surface unconscious and conscious biases towards representations, may not that tool itself be prone to the unconscious biases of its creators? In part due to this potential pitfall, USC\u2019s student newspaper, the Daily Trojan, released an op-ed on the topic: \u201cViterbi must end its partnership with diversity spell check\u201d (http:\/\/dailytrojan.com\/2019\/11\/03\/viterbi-must-end-its-partnership-with-diversity-spell-check\/<\/a>). There are mechanisms, however, to reduce this potential to build biases into the tool. Namely, extracting all normative or qualitative measures from the assessment can ensure a completely fact-based AI. And, one might argue that even though the tool might still include some bias, that this tool is making strides towards improving representation and has much less bias than a development process devoid of such analyses.<\/p>\n![\"\"](\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2020\/04\/AI-Blog_Lead-Actors-Chart.png\")
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