Faculty: Andrea Baer, History and Political Science Librarian

Learning objectives:

• Identify scenarios in which algorithmic bias influences search results in a given online environment.
• Identify scenarios in which algorithmic bias influences everyday life choices or decisions. 
• Identify and apply simple strategies for recognizing and counteracting the negative effects of algorithmic bias.

People often think of technology and search engines like Google as neutral and unbiased. But search engine algorithms frequently reflect larger societal biases, as search engines “learn” from things like: the online content that they index, the topics for which people search, the search terms people use, and the links people click on. Google and other search engines also rank search results based partly on ad revenue, rather than prioritizing source relevance or credibility. Looking beyond Internet search engines, algorithms make predictions about us that can have real consequences on our everyday lives (for example, how much someone pays for insurance, whether someone gets a bank loan, or even if a person’s job application makes it to the eyes of those doing the hiring). Often this further exacerbates social inequities.  

There are a good number of people and groups who are working actively to minimize and counteract the negative effects of bias in search systems and to build more ethical and human technology. But this work has a long road ahead. One thing that everyday citizens can do immediately is to increase our algorithmic awareness and to develop search and evaluation strategies that work to counteract the negative effects of algorithmic biases. 

Challenging the notion that technology and algorithms are neutral, the workshop explores how algorithms influence both online search and everyday life. Participants are invited to consider how algorithms affect their online and offline experiences, to become familiar with where “algorithmic bias” might show up, and to strengthen their abilities to minimize its negative effects, starting with increased algorithmic awareness. 

Related Research Guide

Skill levels for attendees: Little to no experience, Some experience

Software requirements: None