AP Computer Science P Unit 5 Cheatsheet: Impact of Computing

Core idea: Computing innovations create both intended and unintended consequences. The same effect can be beneficial to one group and harmful to another.

• Unanticipated effects: Creators cannot predict all uses or impacts in advance
• Dual nature: A single innovation (e.g., social media, targeted ads, machine learning) may help some while harming others
• Examples:
  • Web: designed for scientists → global communication platform
  • Targeted ads: business tool → privacy invasion & discrimination
  • Machine learning: medical breakthroughs → discriminatory algorithms
• Programmer responsibility: Consider unintended uses and harmful potential, though impossible to foresee all outcomes
• Positive spillover: Innovations often advance unrelated fields (medicine, arts, engineering)
• Scale matters: Rapid sharing with many users amplifies unintended impacts beyond programmer control

Definition: Unequal access to computing devices and the Internet based on socioeconomic, geographic, or demographic factors.

• Variation factors:
  • Income level
  • Geographic location (rural vs. urban)
  • Age, race, ethnicity
  • Country/region
• Scope: Affects both groups and individuals globally and locally
• Consequences: Creates issues of equity, access, and influence
• Who influences it: Individuals, organizations, governments all play roles in widening or narrowing the divide
• Ethical concern: Unequal access raises fundamental fairness questions about who benefits from computing

Definition: Algorithms and systems can reflect existing human biases, which may be introduced intentionally or unintentionally through the algorithm's design or through the data it uses.

• Sources of bias:
  • Biased algorithms (logic written by humans with assumptions)
  • Biased training data (historical inequities baked in)
• Pervasiveness: Bias can enter at any stage—design, coding, data collection, testing
• Programmer duty: Actively work to identify and reduce bias as a way to combat existing human prejudices
• Real-world impact: Biased systems can discriminate in hiring, lending, criminal justice, healthcare