Cultural Immersion AI: The Key Problem to Consider

This semester, I am learning how to replicate the cognitive experience in AI. With a diverse team from an advertiser to an engineer to a banker, we have decided to select the project of exploring a cognitive experience in cultural immersion.Through research, I hope to solve the problem statement that most culturally immersive experiences are expensive. I hope to understand how low-income individuals generate ideas about culturally immersive experiences in relation to cost.

Image is from https://www.engadget.com/2016/08/16/the-next-wave-of-ai-is-rooted-in-human-culture-and-history/

My initial thought about conducting research was to conduct personal interviews. However, after considering that someone’s income level is very personal information, I believe that anonymous Typeform survey might be the best way to extract information needed to solve this problem. Furthermore, I believe I can decrease more of my personal bias in surveys rather than interviews. In interviews, I feel like I might perceive someone’s tone and emotion into my interpretation of their response.

This survey could ask several questions such as:

  • What cultural immersive experiences have you experienced in the past year?
  • Why did you select these specific cultural immersive experience?
  • Look past on your Google search history from your initial thought about taking a trip to finalizing a destination. What did you search?
  • What is your current salary this year?

When analyzing the survey results, I need to consider anchoring bias. Anchoring bias is the tendency for people to overvalue the first piece of information that they see. An example of anchoring bias in this situation could be:

The first 25% of respondents have only experienced cultural immersive experiences in their local city. I make the assumption that low-income individuals only have experienced cultural immersive experiences in their local city due to the cost. As more survey responses come, now majority of low-income individuals actually have had culturally immersive experience outside of their local city. However, I still hold my first assumption because it is the first piece of information I had considered.

Another bias could be a confirmation bias. Data is data, but it becomes information when processed. However, everyone processes different information differently. If I hold my assumption that low-income individuals only are interested in culturally immersive experiences in their local areas because of cost, I might interpret the data differently to confirm my assumption.

In order to account for these biases, I believe that I need to 1)get others to review my survey questions to make sure I do not anchor any information that could create biases for the user, 2) have an open mind and not focus on any assumptions I have made about the user’s experience, and 3) use technical tools such as R and SQL to figure out trends, rather than creating assumptions about trends. With these techniques, I hope to eradicate my personal bias from this data in order to create a solution best for the user.

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A 15-week course focused on techniques for designing products powered by AI that form new relationships with users. Written by students of the Advanced Design for AI course at the University of Texas in Austin.

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Martha Czernuszenko

Martha Czernuszenko

Data Science for Public Good @ UVA. Responsible AI @ AI Global. KodeWithKlossy. Avid Hiker. Bruncher.

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