Caroline Criado-Perez On Data Bias And 'Invisible Women’
'The Computer Got It Wrong': How Facial Recognition Led To False Arrest Of Black Man
Coded Bias Asks: Are Our Faces Being Used Against Us?
Additional resources to think about
Algorithmic Bias and Fairness | Crash Course AI
Learn from Crash Course AI about five common types of algorithmic bias.
How Do Biased Algorithms Damage Marginalized Communities?
On this segment from the TED Radio Hour, learn from Joy Buolamwini who researches bias in technology.
Algorithmic Justice League
Explore the Algorithmic Justice League, an organization fighting for more inclusive and ethical technology.
Data for Good
Learn about a Canadian nonprofit trying to improve the world by using data.
Coded Bias | Independent Lens
Watch the full film and explore resources from Independent Lens' presentation of Coded Bias.
Data science can help us fight human trafficking
In this article from professors at WPI, they dive into how data can help find people who may be at risk for human trafficking, help victims, and disrupt the networks of traffickers. [Content may not be suitable for all students.]
Human Rights Data Analysis Group
The Human Rights Data Analysis Group studies the human consequences of data, analytics, and statistics.
Who created this message?
- What kind of “text” is it?
- How similar or different is it to others of the same genre?
- What are the various elements (building blocks) that make up the whole?
What creative techniques are used to attract my attention?
- What do you notice (about the way the message is constructed)?
- What’s the emotional appeal?
- What makes it seem “real?”
- What's the emotional appeal? Persuasive devices used?
How might different people understand this message differently from me?
- How many other interpretations could there be?
- How could we hear about them?
- How can you explain the different responses?
What lifestyles, values, and points of view are represented in, or omitted from, this message?
- What type of person is the reader/watcher/listener invited to identify with?
- What ideas or perspectives are left out?
- How would you find what’s missing?
- What judgments or statements are made about how we treat other people?
Why is this message being sent?
- What's being sold in this message? What's being told?
- Who is served by or benefits from the message
– the public?
– private interests?
Can data be unbiased?
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