Instruction
Please complete the research discussion assignment in a Jupyter or R Markdown notebook. You should post the GitHub link to your research in a new discussion thread.
As more systems and sectors are driven by predictive analytics, there is increasing awareness of the possibility and pitfalls of algorithmic discrimination. In what ways do you think Recommender Systems reinforce human bias? Reflecting on the techniques we have covered, do you think recommender systems reinforce or help to prevent unethical targeting or customer segmentation? Please provide one or more examples to support your arguments.
Research Discussion Assignment 3
As more systems and sectors are driven by predictive analytics, there is increasing awareness of the possibility and pitfalls of algorithmic discrimination.
The goal of a recommendation system is to recommend the right content to the right user in order to get better content reviews. Models are a reflection of the developer and the purpose of use; the traditional recommender systems have been modeled with two paradigms, collaborative filtering and content-based systems.
In collaborative filtering-based systems, the recommendation is built over the “user-item interaction matrix”, which are records of users’ past interaction with the items. The underlying concept for collaborative filter-based methods is to detect similar users and their interest based on their proximity.
In content-based recommendation systems, the user information and preferences including purchase history are also taken into account. The system contains more descriptive information related to the content and tends to have high bias.
Recommender systems are used to help in decision making in things beyond buying new product or new movie but more critical things such as prioritizing manufacturing lines, routes for transportation. The system can lead to an increase human bias when poorly designed.
In what ways do you think Recommender Systems reinforce human bias?
Recommendation systems can lead to wrongful ideas or views about the society especially within the cyber world.
The recommendation systems can skew someone’s perception about a product and can lead to a world view that may be incorrect.
Recommender systems can reinforce our ignorance about certain views and perception creating social bias that may only be remedied through large scale social awareness programs.
Reflecting on the techniques we have covered; do you think recommender systems reinforce or help to prevent unethical targeting or customer segmentation?
According to Kroll (2018), some best practices can support human rights and governance norms in algorithmically driven decision-making systems and provide ethical standards on how these systems are used. The ethics of recommendation systems is becoming important topic in many articles.
A few years ago, a Target statistician Andrew Pole was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” score. More important, he could also estimate the due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy. An angry father went into a Target outside of Minneapolis, demanding to talk to a manager that Target is sending his daughter coupons for baby clothes and cribs believing that Target was encouraging his teenage daughter to become pregnant without realizing that she was already pregnant.
The action of Target reinforces how recommender systems may be used to unethically target certain individuals solely for marketing purpose. Although the prediction was accurate, neither parent had consented to this prediction, making it ethically problematic even though it was correct. In practice, however, some customers may object to a disclosure that say: data about your purchasing habits may be used for marketing purposes including medical and pregnancy to suggest products that may interest you.
Please provide one or more examples to support your arguments.
In e-commerce, Amazon, ebay and other retail group use recommendation system models for product recommendation and customer retention.
Companies like Netflix, Spotify, HBO use sophisticated recommendation systems for video and song recommendations as targeted marketing strategy.
Twitter uses recommendation filter for users to avoid content from low-quality accounts.
References
Kroll, J. A. (2018). Data Science Data Governance [AI Ethics], IEEE Security & Privacy, vol. 16 no 6. Available: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8636447