Theory of Knowledge
Theory of Knowledge
13
Chapters
165
Notes
Chapter 1 - Knowledge & The Knower(Core)
Chapter 1 - Knowledge & The Knower(Core)
Chapter 2 - Knowledge & Technology(Optional)
Chapter 2 - Knowledge & Technology(Optional)
Chapter 3 - Knowledge & Language(Optional)
Chapter 3 - Knowledge & Language(Optional)
Chapter 4 - Knowledge & Politics(Optional)
Chapter 4 - Knowledge & Politics(Optional)
Chapter 5 - Knowledge & Religion(Optional)
Chapter 5 - Knowledge & Religion(Optional)
Chapter 6 - Knowledge & Indigenous Societies(Optional)
Chapter 6 - Knowledge & Indigenous Societies(Optional)
Chapter 7 - History(AoK)
Chapter 7 - History(AoK)
Chapter 8 - The Human Sciences(AoK)
Chapter 8 - The Human Sciences(AoK)
Chapter 9 - The Natural Sciences(AoK)
Chapter 9 - The Natural Sciences(AoK)
Chapter 10 - The Arts(AoK)
Chapter 10 - The Arts(AoK)
Chapter 11 - Mathematics(AoK)
Chapter 11 - Mathematics(AoK)
Chapter 12 - ToK Exhibition
Chapter 12 - ToK Exhibition
Chapter 13 - ToK Essay
Chapter 13 - ToK Essay
IB Resources
Chapter 2 - Knowledge & Technology(Optional)
Theory of Knowledge
Theory of Knowledge

Chapter 2 - Knowledge & Technology(Optional)

Digital Data's Impact: Beyond Faces to Behavior Insights

Word Count Emoji
563 words
Reading Time Emoji
3 mins read
Updated at Emoji
Last edited onย 14th Jun 2024

Table of content

๐ŸŽฏ Key Idea

Our modern, digital culture feeds off visual data, captured in photos from various sources. This data, when coupled with powerful technologies like facial recognition, can pose a privacy concern. But, technologies today see beyond our physical appearances. They capture our behaviors, language, reactions, and relationships, leading to some serious implications.

๐ŸŒ Data-driven insights & its consequences

Digital tech can provide insights about our behaviors, preferences, interactions, etc.

 

Example: Use of algorithms in criminal justice. They calculate a potential offender's likelihood to reoffend (recidivism risk). This is then used to suggest prison sentences.

 

Irony: Longer sentences have been shown to increase recidivism rates.

 

Concern: These tech systems have shown bias towards ethnic groups and economic classes, contributing to a cycle of discrimination.

 

๐Ÿ”ฎ Predictive Knowledge Limitations

 

Both human and machine predictions have their limitations.

 

Questions arise

  • How do we know if machine predictions are more reliable than humans?
  • What knowledge is necessary to evaluate the validity and neutrality of these algorithms?
  • Who decides the assumptions these models should be based on?

๐Ÿ’ก Real World Example: Consider the US prison system. People of color often receive higher recidivism risk scores leading to longer sentences. This results in fewer opportunities upon release and higher recidivism risk scores for others in their community. A tragic feedback loop!

 

๐Ÿ”— Want to dive deeper? Check out Cathy O’Neil's talk at Google about “Weapons of Math Destruction”.

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IB Resources
Chapter 2 - Knowledge & Technology(Optional)
Theory of Knowledge
Theory of Knowledge

Chapter 2 - Knowledge & Technology(Optional)

Digital Data's Impact: Beyond Faces to Behavior Insights

Word Count Emoji
563 words
Reading Time Emoji
3 mins read
Updated at Emoji
Last edited onย 14th Jun 2024

Table of content

๐ŸŽฏ Key Idea

Our modern, digital culture feeds off visual data, captured in photos from various sources. This data, when coupled with powerful technologies like facial recognition, can pose a privacy concern. But, technologies today see beyond our physical appearances. They capture our behaviors, language, reactions, and relationships, leading to some serious implications.

๐ŸŒ Data-driven insights & its consequences

Digital tech can provide insights about our behaviors, preferences, interactions, etc.

 

Example: Use of algorithms in criminal justice. They calculate a potential offender's likelihood to reoffend (recidivism risk). This is then used to suggest prison sentences.

 

Irony: Longer sentences have been shown to increase recidivism rates.

 

Concern: These tech systems have shown bias towards ethnic groups and economic classes, contributing to a cycle of discrimination.

 

๐Ÿ”ฎ Predictive Knowledge Limitations

 

Both human and machine predictions have their limitations.

 

Questions arise

  • How do we know if machine predictions are more reliable than humans?
  • What knowledge is necessary to evaluate the validity and neutrality of these algorithms?
  • Who decides the assumptions these models should be based on?

๐Ÿ’ก Real World Example: Consider the US prison system. People of color often receive higher recidivism risk scores leading to longer sentences. This results in fewer opportunities upon release and higher recidivism risk scores for others in their community. A tragic feedback loop!

 

๐Ÿ”— Want to dive deeper? Check out Cathy O’Neil's talk at Google about “Weapons of Math Destruction”.

Unlock the Full Content! File Is Locked Emoji

Dive deeper and gain exclusive access to premium files of Theory of Knowledge. Subscribe now and get closer to that 45 ๐ŸŒŸ