Ethics and Privacy in Big Data

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Questions and Answers

Which of the following correctly defines Deontology in ethical theories?

Utility is assessed based on the goodness of a policy.

The focus is on achieving happiness through virtuous character.

An action's rightness is judged by its consequences.

The morality of an action is determined by its adherence to rules. (correct)

Which of the following is NOT one of the 5 V's of Big Data?

Volume

Velocity

Variety

Validity (correct)

What is an example of preexisting bias?

A survey excludes people under 18 intentionally.

A software that promotes user privacy while collecting data.

An algorithm is trained only on historical data favoring one demographic. (correct)

A data set only includes individuals from a specific income bracket.

Which ethical theory focuses on the outcomes of actions to determine their morality?

Utilitarianism Signup and view all the answers

In which scenario is implicit bias most likely to occur?

A program that unintentionally learns from biased inputs. Signup and view all the answers

What defines 'Virtue Ethics' as a philosophical approach?

It aims for happiness through virtuous character development. Signup and view all the answers

Which statement reflects the view of technology determinists?

Technology fundamentally alters societal structures and functions. Signup and view all the answers

What does the FIPS principle emphasize regarding organizations?

They should adopt recommendations for privacy policies. Signup and view all the answers

What is a significant issue with algorithms related to fairness?

They might still predict sensitive attributes even when removed from the dataset. Signup and view all the answers

Which of the following best defines emergent bias?

Bias that arises from user interaction after a system is deployed. Signup and view all the answers

What is disparate treatment?

Deliberate unequal treatment based on identity factors. Signup and view all the answers

What contributed to the bias in the Stanford vaccine distribution algorithm?

It used data that reflected historical inequities. Signup and view all the answers

How did Amazon's AI recruiting tool exhibit bias?

It penalized resumes based on gender-specific affiliations. Signup and view all the answers

What is the primary concern regarding fairness in algorithms?

Data used can reflect existing social inequities. Signup and view all the answers

Which theory states that wealth acquisition should impact access to healthcare?

Robert Nozick’s perspective. Signup and view all the answers

What does the term 'panopticon' relate to in the context of surveillance?

A concept for constant observation and control. Signup and view all the answers

Why is it challenging to make algorithms fair?

Removing sensitive attributes sometimes still leads to biased predictions. Signup and view all the answers

What can result from a mismatch between users and system design?

Emergent bias in the system. Signup and view all the answers

Study Notes

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.

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Description

Explore the intricate relationship between security, economics, ethics, and privacy in the realm of big data. This quiz covers the Five Vs of big data, ethical theories, and fair information practices. Challenge your understanding of how these elements influence data policies and decision-making.