Ethics and Privacy in Big Data

Security, Economics, Ethics, Privacy, and Big Data

• Security, economics, ethics, privacy, and big data are interconnected fields. Big data relies on secure systems and ethical considerations. Economic factors influence data usage and privacy policies.
• Ethical theories, like utilitarianism, deontology, and virtue ethics, provide frameworks for evaluating decision-making and policies related to these areas.

Five Vs of Big Data

• Volume: the massive scale of data.
• Velocity: the speed at which data is generated and processed.
• Variety: the diverse formats of data (structured, unstructured, semi-structured).
• Veracity: the trustworthiness and accuracy of the data.
• Value: the potential benefit gained from analyzing the data.

Fair Information Practices (FIPS)

• Organizations should adhere to guidelines for privacy policies and the development of privacy laws.

Ethical Theories

• Utilitarianism: Focuses on outcomes. Action judged solely by consequences.
• Deontology: Actions judged based on conformity to moral norms, regardless of consequences.
• Virtue Ethics: Goal is well-being (happiness) attained through virtuous character.

Biases

• Bias: Prejudice in favor or against someone or something, usually unfairly.
• Categories of Bias:
  • Preexisting Bias: Existing before system creation (e.g., historical data reflecting existing inequalities).
    • Explicit: Intentional bias (e.g., excluding certain groups from a survey).
    • Implicit: Unintentional bias (e.g., data containing biased information).
  • Individual Bias: Personal attitudes and biases influencing the system.
  • Societal Bias: Significant systemic issues in a culture or society.
  • Technical Bias: Technical constraints or considerations impacting fairness. (e.g., random number generation issues, design flaws)
  • Emergent Bias: Bias arising after system deployment (e.g., user feedback, new societal knowledge).

Goals of Algorithms

• Accuracy: Aim for correct results.
• Termination: Algorithms must conclude.
• Performance: Optimal speed and efficiency.

Cases of Algorithmic Bias

• Stanford Vaccine Algorithm Example: Prioritized remote workers over frontline doctors due to flawed data.
• Racial Bias in Medical Algorithm Example: Favored white patients over sicker black patients due to incomplete and biased data.
• Criminal Risk Scores Example: Data inevitably reflects existing societal biases.
• Amazon AI Recruiting Tool: Tools developed with biased data produced biased outcomes.
• Credit Scoring System Example: Problems extend beyond technical bias to systemic inequities in the data.

Fairness, Justice, and Discrimination

• Fairness: Ensuring algorithms treat all groups equally.
• Justice: Fairness and reasonableness in the application of rules.
• Discrimination: Unjust and prejudicial treatment of different groups.
• Disparate Treatment: Treating groups differently, requiring intent.
• Disparate Impact: Unintentional outcomes that treat groups differently.

Challenges of Algorithmic Fairness

• Removing sensitive attributes from data does not eliminate implicit bias.
• Algorithms can predict sensitive characteristics using other features.

Theories of Fairness in Algorithms

• John Rawls:
  • Principle of equal liberty: Basic rights for all.
  • Difference principle: Addressing inequalities to benefit the least advantaged.
  • Fair equality of opportunity: Equal opportunities for all.
• Robert Nozick: Just acquisition of wealth determines access.
• Michael Walzer: Needs-based allocation of resources.

Surveillance and Power

• Employee Monitoring: Tracking communications and activities.
• Workplace Communications Policies: Guidelines governing employee communications.
• Panopticon: Concept of surveillance where individuals are constantly observed, creating self-policing.