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Algorithm Bias & Gaming

workshop materials (fall 2020)

Algorithms

Welcome to this workshop and information on algorithm bias.  By the end of our time together, you will be able to

  • define algorithm bias.
  • recognize how algorithms may perpetuate bias or misrepresent certain people or groups.
  • understand how algorithms may be altered or gamed for certain purposes.
  • brainstorm strategies to minimize algorithm bias.

Overaching Question: Are search engines a fair and unbiased source of information?

search engine image

Image by mohamed_hassan at Pixabay

Algorithm: a set of rules to achieve a goal (Merriam-Webster)

Algorithms: "...a set of step-by-step instructions that computers follow to perform a task..." (The Brookings Institution)

Search Algorithm: "... a procedure that determines what kind of information is retrieved from a large mass of data." (Merriam-Webster)

Machine Learning: "...the study of computer algorithms that improve automatically through experience.[1][2] It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.[3] (Wikipedia)

text for algorithm and machine learning

image created with Canva

  • recommender services (e.g. Netflix, Facebook, Pinterest, Pandora)
  • retailers (Amazon)
  • search engines (Google, Bing, Library databases)
  • evaluation systems (credit evaluation, insurance risk)

Video: Your New Favorite Song Has Been Chosen By An Algorithm (2:22) NBC News

Question:

  1. How do Pandora and Spotify algorithms differ from each other?

 

Algorithmic Bias: "...  describes systematic and repeatable errors in a computer system that create unfair outcomes, such as privileging one arbitrary group of users over others.” (Wikipedia)

Video: The Coded Gaze: Bias in Artificial Intelligence (13:19) Joy Buolamwini, Algorithmic Justice League

Questions:

  1. Where has Buolamwini experienced algorithmic bias?
  2. What is often one key problem with systems using algorithms?
  3. In what other spheres might artificial intelligence (AI) be used inaccurately?