CS61B Textbook
  • Contributors
  • DISCLAIMER
  • 1. Introduction
    • 1.1 Your First Java Program
    • 1.2 Java Workflow
    • 1.3 Basic Java Features
    • 1.4 Exercises
  • 2. Defining and Using Classes
  • 3. References, Recursion, and Lists
  • 4. SLLists
  • 5. DLLists
  • 6. Arrays
  • 7. Testing
  • 8. ArrayList
  • 9. Inheritance I: Interface and Implementation Inheritance
  • 10. Inheritance II: Extends, Casting, Higher Order Functions
    • 10.1 Implementation Inheritance: Extends
    • 10.2 Encapsulation
    • 10.3 Casting
    • 10.4 Higher Order Functions in Java
    • 10.5 Exercises
  • 11. Inheritance III: Subtype Polymorphism, Comparators, Comparable
    • 11.1 A Review of Dynamic Method Selection
    • 11.2 Subtype Polymorphism vs Explicit Higher Order Functions
    • 11.3 Comparables
    • 11.4 Comparators
    • 11.5 Chapter Summary
    • 11.6 Exercises
  • 12. Inheritance IV: Iterators, Object Methods
    • 12.1 Lists and Sets in Java
    • 12.2 Exceptions
    • 12.3 Iteration
    • 12.4 Object Methods
    • 12.5 Chapter Summary
    • 12.6 Exercises
  • 13. Asymptotics I
    • 13.1 An Introduction to Asymptotic Analysis
    • 13.2 Runtime Characterization
    • 13.3 Checkpoint: An Exercise
    • 13.4 Asymptotic Behavior
    • 13.6 Simplified Analysis Process
    • 13.7 Big-Theta
    • 13.8 Big-O
    • 13.9 Summary
    • 13.10 Exercises
  • 14. Disjoint Sets
    • 14.1 Introduction
    • 14.2 Quick Find
    • 14.3 Quick Union
    • 14.4 Weighted Quick Union (WQU)
    • 14.5 Weighted Quick Union with Path Compression
    • 14.6 Exercises
  • 15. Asymptotics II
    • 15.1 For Loops
    • 15.2 Recursion
    • 15.3 Binary Search
    • 15.4 Mergesort
    • 15.5 Summary
    • 15.6 Exercises
  • 16. ADTs and BSTs
    • 16.1 Abstract Data Types
    • 16.2 Binary Search Trees
    • 16.3 BST Definitions
    • 16.4 BST Operations
    • 16.5 BSTs as Sets and Maps
    • 16.6 Summary
    • 16.7 Exercises
  • 17. B-Trees
    • 17.1 BST Performance
    • 17.2 Big O vs. Worst Case
    • 17.3 B-Tree Operations
    • 17.4 B-Tree Invariants
    • 17.5 B-Tree Performance
    • 17.6 Summary
    • 17.7 Exercises
  • 18. Red Black Trees
    • 18.1 Rotating Trees
    • 18.2 Creating LLRB Trees
    • 18.3 Inserting LLRB Trees
    • 18.4 Runtime Analysis
    • 18.5 Summary
    • 18.6 Exercises
  • 19. Hashing I
    • 19.1 Introduction to Hashing: Data Indexed Arrays
      • 19.1.1 A first attempt: DataIndexedIntegerSet
      • 19.1.2 A second attempt: DataIndexedWordSet
      • 19.1.3 A third attempt: DataIndexedStringSet
    • 19.2 Hash Code
    • 19.3 "Valid" & "Good" Hashcodes
    • 19.4 Handling Collisions: Linear Probing and External Chaining
    • 19.5 Resizing & Hash Table Performance
    • 19.6 Summary
    • 19.7 Exercises
  • 20. Hashing II
    • 20.1 Hash Table Recap, Default Hash Function
    • 20.2 Distribution By Other Hash Functions
    • 20.3 Contains & Duplicate Items
    • 20.4 Mutable vs. Immutable Types
  • 21. Heaps and Priority Queues
    • 21.1 Priority Queues
    • 21.2 Heaps
    • 21.3 PQ Implementation
    • 21.4 Summary
    • 21.5 Exercises
  • 22. Tree Traversals and Graphs
    • 22.1 Tree Recap
    • 22.2 Tree Traversals
    • 22.3 Graphs
    • 22.4 Graph Problems
  • 23. Graph Traversals and Implementations
    • 23.1 BFS & DFS
    • 23.2 Representing Graphs
    • 23.3 Summary
    • 23.4 Exercises
  • 24. Shortest Paths
    • 24.1 Introduction
    • 24.2 Dijkstra's Algorithm
    • 24.3 A* Algorithm
    • 24.4 Summary
    • 24.5 Exercises
  • 25. Minimum Spanning Trees
    • 25.1 MSTs and Cut Property
    • 25.2 Prim's Algorithm
    • 25.3 Kruskal's Algorithm
    • 25.4 Chapter Summary
    • 25.5 MST Exercises
  • 26. Prefix Operations and Tries
    • 26.1 Introduction to Tries
    • 26.2 Trie Implementation
    • 26.3 Trie String Operations
    • 26.4 Summary
    • 26.5 Exercises
  • 27. Software Engineering I
    • 27.1 Introduction to Software Engineering
    • 27.2 Complexity
    • 27.3 Strategic vs Tactical Programming
    • 27.4 Real World Examples
    • 27.5 Summary, Exercises
  • 28. Reductions and Decomposition
    • 28.1 Topological Sorts and DAGs
    • 28.2 Shortest Paths on DAGs
    • 28.3 Longest Path
    • 28.4 Reductions and Decomposition
    • 28.5 Exercises
  • 29. Basic Sorts
    • 29.1 The Sorting Problem
    • 29.2 Selection Sort & Heapsort
    • 29.3 Mergesort
    • 29.4 Insertion Sort
    • 29.5 Summary
    • 29.6 Exercises
  • 30. Quicksort
    • 30.1 Partitioning
    • 30.2 Quicksort Algorithm
    • 30.3 Quicksort Performance Caveats
    • 30.4 Summary
    • 30.5 Exercises
  • 31. Software Engineering II
    • 31.1 Complexity II
    • 31.2 Sources of Complexity
    • 31.3 Modular Design
    • 31.4 Teamwork
    • 31.5 Exerises
  • 32. More Quick Sort, Sorting Summary
    • 32.1 Quicksort Flavors vs. MergeSort
    • 32.2 Quick Select
    • 32.3 Stability, Adaptiveness, and Optimization
    • 32.4 Summary
    • 32.5 Exercises
  • 33. Software Engineering III
    • 33.1 Candy Crush, SnapChat, and Friends
    • 33.2 The Ledger of Harms
    • 33.3 Your Life
    • 33.4 Summary
    • 33.5 Exercises
  • 34. Sorting and Algorithmic Bounds
    • 34.1 Sorting Summary
    • 34.2 Math Problems Out of Nowhere
    • 34.3 Theoretical Bounds on Sorting
    • 34.4 Summary
    • 34.5 Exercises
  • 35. Radix Sorts
    • 35.1 Counting Sort
    • 35.2 LSD Radix Sort
    • 35.3 MSD Radix Sort
    • 35.4 Summary
    • 35.5 Exercises
  • 36. Sorting and Data Structures Conclusion
    • 36.1 Radix vs. Comparison Sorting
    • 36.2 The Just-In-Time Compiler
    • 36.3 Radix Sorting Integers
    • 36.4 Summary
    • 36.5 Exercises
  • 37. Software Engineering IV
    • 37.1 The end is near
  • 38. Compression and Complexity
    • 38.1 Introduction to Compression
    • 38.2 Prefix-free Codes
    • 38.3 Shannon-Fano Codes
    • 38.4 Huffman Coding Conceptuals
    • 38.5 Compression Theory
    • 38.6 LZW Compression
    • 38.7 Summary
    • 38.8 Exercises
  • 39. Compression, Complexity, P = NP
    • 39.1 Models of Compression
    • 39.2 Optimal Compression, Kolmogorov Complexity
    • 39.3 Space/Time-Bounded Compression
    • 39.4 P = NP
    • 39.5 Exercises
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On this page
  • The Ledger of Harms
  • Making Sense of the World
  • Attention
  • Mental Health
  • Relationships
  • Politics and Democracy
  • Systemic Oppression
  • Children
  1. 33. Software Engineering III

33.2 The Ledger of Harms

Previous33.1 Candy Crush, SnapChat, and FriendsNext33.3 Your Life

Last updated 2 years ago

The concerns that we talked about from the previous section are not left unnoticed by leaders of the industry. In fact, the opposite is true: many of them has openly expressed their concerns regarding these not-so-novel technologies that permeate our lives now.

  • “I think we have created tools that are ripping apart the social fabric of how society works.” - (early executive at Facebook)

  • “God only knows what it[Facebook]’s doing to our children’s brains.” - (the Napster guy)

  • “The technologies we were most excited about 10 years ago are now implicated in just about every catastrophe of the day.” - (technology journalist)

  • “These are our lives. These are our precious, finite, mortal little lives. The idea that we are spending them distracted, not accomplishing the thing that we’re trying to do, is just painful. It’s crazy.” - , creator of the Like button and founder of Asana

  • “Facebook appeals to your lizard brain — primarily fear and anger,” he said. “And with smartphones, they’ve got you for every waking moment.”” - (early investor in many tech companies, interesting guy)

The Ledger of Harms

The problems have long been identified. What about the solution?

The was founded by current and former tech workers to raise awareness and try to combat harmful side effects of internet tech platforms----to develop solutions for these problems.

In 2018, they created a to “collect those negative impacts of social media and mobile tech that do not show up on the balance sheets of companies, but on the balance sheet of society.”

The Ledger encompasses the following categories:

  • The Next Generations: Developmental delays, suicide, physical/mental/social changes.

  • Making Sense of the World: Misinformation, conspiracies, fake news.

  • Attention and Cognition: Loss of ability to focus without distraction.

  • Physical and Mental Health: Stress, loneliness, addiction, risky behavior.

  • Social Relationships: Less empathy, more confusion and misinterpretation.

  • Politics and Elections: Propaganda, distorted dialogue, disrupted democratic processes.

  • Systemic Oppression: Amplification of discrimination.

  • Do Unto Others: Tech employees limit tech usage in their own homes.

For each category, they provide research and citations for potential harms.

The rest of this section will list quotes from some categories to read through.

Making Sense of the World

  • “64% of all extremist group joins are due to our recommendation tools...our recommendation systems grow the problem”, noted an internal Facebook presentation in 2016. Yet repeated attempts to counteract this have been repeatedly ignored, diluted, or deliberately shut down by senior Facebook officers, according to a 2020 Wall Street Journal investigation.”

  • “Fake news spreads six times faster than true news. According to researchers, this is because fake news grabs our attention more than authentic information: fake news items usually have a higher emotional content and contain unexpected information which inevitably means that they will be shared and reposted more often.”

  • “45% of tweets about coronavirus are from bots spreading fake information, according to research from Carnegie Mellon University.”

  • “2 minutes of exposure to a conspiracy theory video reduces people’s pro-social attitudes (such as their willingness to help others), as well as reducing their belief in established scientific facts.”

Attention

  • “The presence of a smartphone, even when off, can reduce cognitive capacity by taxing the attentional resources that reside at the core of both working memory capacity and fluid intelligence.******”

  • “72% of teens and 48% of parents feel the need to immediately respond to texts, social-networking messages, and other notifications.”

  • “1 hour per day is the amount of time most Americans spend dealing with distractions and then getting focused and back on track each day, which comes to a grand total of 5 full weeks in a year.”

Mental Health

  • “30% of 18-44 year olds feel anxious if they haven’t checked Facebook in the last 2 hours [in 2020].”

  • “1 month away from Facebook leads to a significant improvement in emotional well-being. In an experimental study of over 1,600 American adults (who normally used Facebook for up to an hour each day), deactivating Facebook accounts led to a significant increase in emotional well-being (including a reduction in loneliness and an increase in happiness), as well as a significant reduction in political polarization.”

  • “In just 3 years, there has been a quadrupling in the number of plastic surgeons with patients undergoing cosmetic surgery for the sake of looking good on social media (from 13% in 2016 to 55% in 2019).“

Relationships

  • “Even the mere presence of smartphone can disrupt the connection between two people, having negative effects on closeness, connection, and conversation quality.”

  • “People overestimate their ability to correctly interpret sarcasm, humor, or sincerity over text communication, and this means people tend to believe they can communicate over e-mail more effectively than they actually can.”

  • “50% of Americans report that their partner is often or sometimes distracted by their devices when they are trying to talk to them.

  • “89% of cellphone users admit to using their phones during their last social gathering.”

Politics and Democracy

  • “More fake political headlines were shared on Facebook than real ones during the last 3 months of the 2016 US elections.”

  • “Exposure to a fake political news story can rewire your memories: in a study, where over 3,000 voters were shown fake stories, many voters later not only “remembered” the fake stories as if they were real events but also "remembered" additional, rich details of how and when the events took place.”

  • “Analyzing over 2 million recommendations and 72 million comments on YouTube in 2019, researchers demonstrated that viewers consistently moved from watching moderate to extremist videos;” [Josh Note: This is much much better now. Good job youtube!]

Systemic Oppression

  • “Russia's IRA spread false information designed to create outrage about Black Lives Matter and deepen social division in the US. Research indicates that one of the IRA's major strategies was to use social media platforms to target conservative groups who supported the police or veterans and specifically feed them misinformation about BLM.”

  • “With over 800 million users, TikTok promotes itself as a place for self-expression and unrestricted creativity, yet its internal documents reveal a policy of downgrading content from users who do not fit normative ideals of gender, race, class, sexuality, or able-bodiedness, with moderators urged to censor users with "abnormal body shape", "too many wrinkles", or whose environment shows signs of poverty such as "cracks in the wall" or "old decorations".”

Children

  • “58 minutes per day is the average amount of time 2-4 years old spend on mobile devices.“

  • “In a longitudinal study tracking over 200 children from the age of 2 years to 5 years old, children with higher levels of screen time showed greater delays in development across a range of important measures, including language, problem-solving, and social interaction. Analyses indicated that the level of screen time was significantly linked to the specific level of developmental delay 12 -14 months later. “

Chamath Palihapitiya
Sean Parker
Farhad Manjoo
Justin Rosenstein
Roger McNamee
Center for Humane Technology
Ledger of Harms