Algorithms and Bias

Understanding Bias

  • Definition of Bias
    • Unfair beliefs or behaviors directed towards an individual or group.
    • Typically tied to preconceived notions regarding gender, race, age, or sexual orientation.
    • Can be positive (e.g., taller athletes being favored) or negative (e.g., racism).
    • The historical context of bias in contexts like sports (e.g., Michael Lewis' "Moneyball").

Implicit Bias

  • Definition
    • Often unconscious bias; individuals may not be aware of it.
  • Stereotype Threat
    • Example experiment:
    • Math test presented as challenging → Men outperform women.
    • Math test presented neutrally → Performance equal across genders.
    • The subjectivity and controversy surrounding the phenomenon.

Our Shared Biases

  • Cultural Recognition
    • Everyone possesses some level of bias.

Search Engine Bias

  • Examples of bias in search engines:
    • Ads for executive positions are displayed less to women.
    • Searching "doctors" predominantly shows men, despite 34% being women.
    • Racially biased outputs:
    • Example searches show disparities in representation of groups.
    • Notable improvements since awareness was raised.

AI and the Justice System

  • Applications
    • Use of predictive algorithms in parole, sentencing recommendations, and policing.
    • Can perpetuate existing biases through historical data.

Bias in Robotics

  • Concerns
    • Life-or-death decisions made by military robots and system errors, particularly with vision.
    • Medical robots needing to make ethical value judgments (e.g., in triage situations).

Comparing ML and Symbolic AI

  • Definitions
    • Machine Learning: Utilizing datasets with statistical methods.
    • Symbolic AI: Processing symbols that hold real-world meanings.
  • Future Trends
    • Movement towards ML while also anticipating a need for synthesis with symbolic reasoning.

Understanding ML Bias

  • Black Box Issue
    • Algorithms typically provide answers without clear reasoning behind them.
    • Concept of "garbage in, garbage out" reflects on data quality being paramount to result quality.

Explainable AI

  • Current Research
    • Focus on creating ML models that can elucidate reasons behind decisions made.
    • Developing rule-based expert systems as a potential methodology for transparency.

Types of Bias in ML Datasets

  • Key Bias Types
    • Reporting Bias: Extreme opinions can skew understanding (e.g., book reviews).
    • Automation Bias: Users placing undue trust in algorithmic outcomes.
    • Selection Bias: Non-representative data selection.
    • Coverage Bias: Skewed data collection methods leading to poor representation.
    • Non-response Bias: Variation in response likelihood based on characteristics.
    • Sampling Bias: Non-random sampling methods (e.g., first 200 responses).

Group Attribution Bias

  • Definitions
    • In-group Bias: Favoring individuals similar to oneself (e.g., alumni hiring).
    • Out-group Homogeneity Bias: Stereotyping outsiders.

Types of Bias in ML Datasets (cont.)

  • Additional Bias Types
    • Implicit Bias: Assumptions based on personal beliefs that may not be widely applicable.
    • Confirmation Bias: Tendency to validate personal beliefs unconsciously.
    • Experimenter’s Bias: Iterative modifications to uphold expected results.

FATE Model in AI

  • Elements
    • Fairness: Ensuring equity in AI applications.
    • Accountability: Maintaining responsibility for decisions made by AI.
    • Transparency: Demanding clarity in algorithms and processes.
    • Ethics: Ethical considerations in AI deployment and functionalities.