Adhering to TV Censorship Standards and Practices using AI

There was a once a time when TV audiences across the world had to limit their viewership to within the geographical boundaries of local TV networks. In the age of internet however, video-on-demand platforms have taken over the media industry and the content we access is no longer bound by geography. In fact, our viewership experience is excitingly international given that we are able to watch content produced in say Brazil, Germany, Korea and the USA from the comforts of our home in any corner of the world. But what is often overlooked in this seemingly unhindered chain of distribution are the countless sets of laws and practices that govern what content can be distributed where and how.

From UA in India to TV-PG in the USA and MA15+ in Australia, the standards that define Television Content Rating systems across the world are painfully complex and diverse. For example, a 60 minute season finale (June, 2015) of the US TV show Game of Thrones had to be halved in length to allow it to conform to the censorship norms in India. A movie or TV show produced for an audience in one country may carry a certain level of certification but is likely to contain content (like nudity, profanity, violence, etc) that is not acceptable for an international audience governed by several censorship regimes and standards. What this means for broadcasters and movie studios is a considerable amount of manual effort spent in editing their content to comply with various censorship norms. The financial and logistical burden this poses is weighed down further given that the conditions imposed by these regimes are as dynamic as they are complex, thereby necessitating intensive training to gain expertise in the field.

A representative comparison of current television content rating systems between four countries with the horizontal axis indicating age.

With the help of AI however, most of this work can now be almost completely automated, thus eliminating the need for time-consuming manual labour. The presence of restricted content like nudity, profanity, violence, the act of smoking and alcohol consumption can be detected and tagged at scale by new-age AI models. These models, once trained, can go on to identify not only the presence of the restricted content, but also the levels of presence as pre-defined by various censorship standards. The use of machine learning will then enable this technology to identify the exact points in the given video/audio content where further editing is required. The model finally completes the task by allowing the production softwares to automatically use this tagged data to perform corrective actions such as;

  • Blurring the image or bleeping the word
  • Completely removing or cropping the image
  • Displaying a warning sign on the screen

Image Credits – Poster for Sin City: A Dame to Kill For (2014)

Needless to say, the cost and time efficiency of this process is nothing short of a boon that AI technology has granted the media industry. AthenasOwl has been making great progress in achieving these results and has successfully implemented our product for clients with similar use cases. The almost total automation of the censorship compliance process that AO offers can not only hasten the delivery of existing international content to our screens but will also serve as an incentive for movie studios to actively produce media for a wider audience.


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