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Learn About Our SXSW 2021 Panel Submissions

Mary Brendza

We submitted three panels to SXSW this year. Below is the synopsis and the links to the SXSW profile page.

 

1. Stop Stereotyping Me! bit.ly/3eRlQC1

Cherry Cake Twitter Post (1)

Unintentional stereotyped portrayals in stories are often the result of ignorance or unconscious bias on the part of storytellers. By integrating 100 years of social-science research and cutting-edge AI technology, we've built a process to measure the building blocks of negative stereotypes, including sexuality, violence, intelligence, and other character and story features that are key ingredients to well-studied stereotypes. With these data, filmmakers and other practitioners are made aware of stereotyping patterns in their stories before millions are invested. We'll share how we work with storytellers on many types of stereotypes from racial to gender to social class, so they can make informed decisions about what they want on-screen.

2. Violence in Storytelling: How Much is Too Much? bit.ly/2UjRX3x

Red and Yellow Woman Photography Safety Culture Twitter Post

Violence is hundreds of times more likely to occur in TV and movies than in real life. This thing we abhor IRL is a key ingredient of so many stories because it creates conflict and restores order. Violence is also difficult to define consistently. (Is it intentional? Does a consensual sparring match count differently from an antagonistic knife or gunfight?) Beyond defining and measuring violence, the film and tv industry wants to know how violence impacts audiences. AI is poised to answer these questions in ways that weren’t available even a year ago. With this technology, we can answer these questions and look at trends in violence. Empowered with this data, storytellers can be more intentional with their use or non-use of violence as a story element.

3. How to Ensure #AI Won’t Ruin Your Creative Biz: bit.ly/3lriI28

Welcome Interns (2)

The fear of using AI for creative pursuits in the entertainment industry is that biases will creep in, and formulas will lead to bland story sameness. Will blindly following past success lead to targeting audiences whose needs are already satisfied? Or will it create a vicious cycle of biased content leading to more biased content (e.g., stereotypes)? And even more worrisome, what if these models are based on junk science? How can we not get duped by fake formulas from AI companies? And more importantly, how can we break this pattern and use science without stifling creativity? Specifically, how can we use technology in the service of innovation rather than to innovation’s detriment?

Hope to see you at SXSW2021! 

 

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Mary Brendza
Mary Brendza