Artificial Intelligence in Film: Greenlighting

This article was written by Loice Kerubo Nyaribo, who is participating in qLegal as part of her Technology, Media and Telecommunications Law Masters studies at Queen Mary University of London. She is a Silver Award Winner of qLegal’s Blog Writing Competition 2021–2022. This paper does not constitute legal advice and should not be relied upon as a source of legal advice. **

Entertainment has always been creating trends and certainly a way to influence culture and social movements. A prime example is the obsession with celebrities and the subsequent popularity of what they wear, what they do and where they go. Films, especially those considered as captivating by the person watching, have the ability to influence behaviour. Personally, watching Mighty Joe Young (1998) put silverback gorillas on my animals-to-see bucket list and the Lord’s Prayer as performed in Sarafina (1992) ignited my love for musicals. Nonetheless, I have pointedly refused to watch The Lion King (1994) remake in an effort to guard my childhood memories around the film (TikToker ClassyKing0 has an interesting theory about Mufasa’s death).

It is, therefore, unsurprising that the film industry is a booming business; with studios clamouring to garner audiences for their material. Streaming platforms are a godsend for many film lovers, especially with the Covid-19 pandemic, as access to a variety of films is possible from the comfort of one’s home. Such platforms, like Netflix, use algorithms to recommend users what to watch; portraying an example of the application of artificial intelligence (AI) in the film industry.

AI does not have a customary definition, but its character has been agreed upon to include its ability to mimic human beings. Alan Turing, considered the ‘father of computer science’ and on whom the riveting film The Imitation Game (2014) is based, was the first to give a test for machine intelligence in 1950.(1) The ‘Turing Test’ proposes that if a person poses questions and receives answers from both a human and a computer but cannot tell which answer is the computer’s, then the computer is acting humanly. For this blog, I will define AI as systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. AI includes machine learning which can be defined as a set of computerised techniques for recognising patterns in data. This is extremely helpful in making mundane and data-oriented tasks easy and done in a fraction of the time that a human being would otherwise do it.

The popularity of streaming platforms has forced major studios, which are primarily dependant on box office sales, to seek alternative means and direct their efforts to production and distribution of their films in order to compete for an audience.(2) An essential part of the pre-production process is greenlighting of a screenplay. It is the process of giving approval for a film by analysing its potential commercial success. Imo Bábics gives a good breakdown of how greenlighting occurs. In summary, it involves considering the film’s budget, similar films that have been released and their gross in the box office, estimating how much the film will potentially make and the likely profits to a movie studio company.

Greenlighting has traditionally been done through a ‘gut-feeling’ process which is inconsistent and does not guarantee the success of a film.(3) Consequently, incorporation of AI into this process has been proposed, for instance, the use of natural language processing techniques, screenwriting domain knowledge and statistical learning methods to increase a studio’s return on investment.(4) Application of machine learning techniques, therefore, has helped to solve this issue as an algorithm is able to easily consider trends and determine the profitability of a screenplay. In our capitalistic society, where minimum spend for maximum returns is valued, using AI to determine whether to pursue and fund certain projects has been extremely useful and cost efficient. Additionally, it is quicker and able to spot trends that may not be immediately identified by humans, hence giving a better estimate of a potential film’s performance.

On the flip side, diversity has been an ongoing battle and the film industry has not been immune. In a historic action, numerous actors boycotted the Oscars in 2016 because none of the award nominees were people of colour for the second year running. It is notable that the Academy, which gives the Oscars, is still predominantly white. It is safe to say that the canon (a list of works generally accepted as being important) in film is linked to politics, resulting in the marginalisation of minority groups. Even when ‘value-free’ systems of judgment in film are used, they fail to serve all segments of society in favour of serving the dominant social class.(5) Due to this institutionalised marginalisation, as explained above, there is a threat of AI and machine learning techniques perpetuating injustice as AI has been criticised for adopting its creator’s biases.

Technology is only a tool or conduit for human actions and biased human actions influence its neutrality. During the greenlighting process, if a person only gives screenplays depicting a certain style of film to an AI algorithm for analysis, then that style will be registered as the standard due to machine learning. Undoubtedly, this will perpetuate stereotypes and discrimination, regardless of the intention of the person facilitating the greenlighting. Films that do not fit the mould will be filtered out in favour of those that do, leaving little room for creativity in scriptwriting and variety in theatres.

Furthermore, standardising screenplays threatens our multi-cultural society by creating a monoculture, i.e., where a single culture dominates. A lack of exposure to different realities of life as experienced by different people leads to intolerance and subsequent aggression or even violence by the majority group. A monoculture, especially in film, greatly prejudices minority groups in terms of class, gender, culture, ability, and sexual orientation. Everyone should have the right to create films and have those films experienced by others.

AI is here to stay and has endless potential to improve the film industry. Perhaps the first step to mitigating the challenges above is recognising that it indeed has the potential to discriminate against screenplays representing the minority in society. Studios should aim to diversify their production chains to avert potential marginalisation during greenlighting by the algorithms they may apply. They can also insist that these algorithms be developed by teams with diverse and inclusive representation! Studios should not wait for campaigns against their productions to effect inclusive policies. For example, the Academy only vowed to diversify its membership to include women and ethnic minority members after public outcry and the trending #OscarsSoWhite social media campaign. However, because institutional change has historically proven to be harder than legislative change, this is possibly a discussion that should be magnified at the national level for legislation regarding the use of AI in films to be drafted and implemented.

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1. A. M. Turing, Computing Machinery and Intelligence, (Mind 49, 1950) 433–460 accessed on 30 June 2022.

2. Pei-Sze Chow, Ghost in the (Hollywood) machine: Emergent applications of artificial intelligence in the film industry, NECSUS_European Journal of Media Studies. #intelligence, Jg. 9 (2020–07–06), Nr. 1, S. 193–214, 194–195 accessed 30 June 2022.

3. Chow (n 2), 196.

4. Jehoshua Eliashberg, Sam Hui and Z Zhang, From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts, Management Science, June 2007, 53 (6), 881–893 accessed on 30 June 2022.

5. Janet Staiger, The Politics of Film Canons, (Cinema Journal, 1985), 24(3), 10 accessed 30 June 2022.

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