Mitigating the Harm of Recommender Systems
Read one or more of the articles below and consider how to counter the radicalizing effects of recommender systems or ways to prevent algorithmic discrimination.
Renee Diresta, Wired.com (2018): Up Next: A Better Recommendation System
Zeynep Tufekci, The New York Times (2018): YouTube, the Great Radicalizer
Sanjay Krishnan, Jay Patel, Michael J. Franklin, Ken Goldberg (n/a): Social Influence Bias in Recommender Systems: A Methodology for Learning, Analyzing, and Mitigating Bias in Ratings
Recommender systems like any other predictions and machine learning techniques is a great way of improving customer’s experience but unfortunately it is being misused by the big companies such as facebook, youtube, google, etc as we saw in 2016’s election in US. Generally speaking, it helps us watch recommended movies, buy the products that might be useful for us, etc but the problem is it influences our decisions and most importantly it serves as radicalizer. Also, the ideas is to use algorithm that checks for biasness and avoids radicalism. Fewer companies started identifying radicalized contents and removing them from their platforms but it’s not an effective way as youtube daily receives huge data from all over the world and it is monumental task to remove them all. I don’t think it can be removed completely but regulations are needed to be implemented and user should be able to either turn it off or not based on preferences but at least it should be controllable. On the other side, let’s say we want to remove all the radicalized materials which I don’t see possible unless regulations come from government. In that case, classification techniques can be used to identify the potential radicalized contents based on few predictors which I think is possible over time and influx of more data on it.
I think radicalized contents should be clearly defined and only falsified information should be put in this category and not all contents and users should have control over the contents if they want to see them or not.