1. Advancements in measurement & data collection
1.1. Leads to debate about the right, legitimate, and proper ways to use data
1.2. Data creators, suppliers, and users should engage in ethical considers about data
1.2.1. Aim must be to help focus on issues and attempt to remove confusion and ambiguity.
1.2.1.1. Provide principles that can help people come to a conclusions about what is the right way to act and right thing to do.
1.3. Progress in data science & technology will always be ongoing and will never plateau.
1.3.1. New Data Technologies
1.3.1.1. Blockchain
1.3.1.2. Homomorphic Computation
1.3.1.3. Quantum computing
1.3.2. Statistics & Computer Science
1.3.2.1. Center of Data Revolution
1.3.2.1.1. Previously very technical focused
1.3.2.2. Current tools and the way they are being used is highly sensitive to subtle ethical issues
1.3.2.3. Two types of Analysis
1.3.2.3.1. Aimed at Research
1.3.2.3.2. Aimed at Practice
1.3.3. Hard to create precise regulations and guidelines because of the rapid changes
2. Consent & Purpose
2.1. Should be obtained before an intervention based on the understanding of implications and consequences.
2.1.1. (1) An understanding of what the data might be used for in the future.
2.1.1.1. Difficult concept because future is unknown.
2.1.1.2. Data may be merged with other data sets to reveal information.
2.1.1.2.1. Valuable for exploring aggregate properties of population.
2.1.1.2.2. Important for decisions relating to individuals.
2.1.1.3. Might not be possible to say what use given data will contribute to.
2.1.2. (2) An understanding of how the data are to be used.
2.1.2.1. Assumes that the person being asked to consent has the expertise and knowledge to understand the data
2.1.2.2. Obtain an explanation of the decision reached after such assessment.
2.1.2.2.1. Find a strategy for identifying which variables are important in reaching a decision.
2.1.2.3. Defines characteristic of modern data analytic tools.
2.1.2.3.1. Ex: neural networks, Support vector machines, and ensemble systems, that are intrinsically complex.
2.2. Contains 3 Pillars: Accountability, Transparency and Responsibility
3. Metcalf outward facing general ethics codes for professions
3.1. Protect vulnerable populations
3.2. Protect/enhance the good reputation of the profession
3.3. Establish the profession as a distinct moral community
3.4. Provide a basis for public expectations and evaluation
3.5. Serve as a basis for adjudicating disputes among members
3.6. Create resilient institutions
3.7. Respond to past harms
4. Principle based approach
4.1. Code of Practice of the UK Statistics Authority based on eight principles
4.2. Data Management and Use: Governance in the 21st Century based on five principles
4.3. The Accenture Universal Principles of Data Ethics that has twelve principles
4.4. The ACM Code of Ethics and Professional Conduct
4.5. Highest Level of Principles
4.5.1. Integrity
4.5.2. Honesty
4.5.3. Objectivity
4.5.4. Responsibility
4.5.5. Trustworthiness
4.5.6. Impartiality
4.5.7. Nondiscrimination
4.5.8. Transparency
4.5.9. Accountability
4.5.10. Fairness
4.5.11. Robustness
4.5.12. Resilience
4.5.13. Usability
4.5.14. Efficiency
4.5.15. Independence
4.5.16. Six Main Principles
4.5.16.1. Start with clear user need and public benefit
4.5.16.2. Use data and tools with min. intrusion
4.5.16.3. Create Robust Data Science Models
4.5.16.4. Be alerts to public perceptions
4.5.16.5. Be open and accountable
4.5.16.6. Keep data secure
5. Data Ownership
5.1. If the data are anonymized
5.1.1. No problems arise
5.1.2. data from research of 100 participants whos identifying aspects have been removed
5.1.3. research data
5.2. GDPR states natural persons should have personal ownership of their
5.3. Digital watermarking
5.3.1. Steganography
5.3.1.1. message or other information is concealed in a body of data
5.4. identifying signal is embedded in the data
5.5. Many times ill defined
5.6. In other contexts
5.6.1. Ownership is dictated by if that person has the right to control use
5.6.2. creator or legal entity for whom the creator is working is typically the initial owner
5.7. Who owns
5.7.1. Google recording search histories
5.7.2. personal data, data ‘‘about’’ someone?
5.7.3. A shop records the prices of the goods you have bought so it can calculate how much to charge you
5.8. Various authors say
5.8.1. ownership of personal data rests with the people that data are about
5.9. Alex Pentland
5.9.1. individuals might have control over their personal data, so that ‘‘a person’s data would be equivalent to their ‘money.’
5.9.1.1. reside in an account where it would be controlled, managed, exchanged, and accounted for just like personal banking services operate today.
5.9.1.1.1. States it would idealistic and not necessarily realistic
5.9.1.1.2. Not feasible
5.10. Potential of data commons
5.10.1. trusted organizations that store data from a variety of bodies
5.10.2. Similar to credit bureaus
5.10.3. Advantages include range of bodies feeding in data reduces selection bias risks
5.10.4. Looked at by ADRN
5.10.4.1. links data from different UK govt. dept. in a secure environment
5.11. Legal uncertainty exists
5.11.1. EU Inception Impact Assessment
5.11.1.1. Personal data cannot be owned
5.11.1.2. Strict rules on access are in place
5.11.1.3. gap exists with regard to non personal data
5.11.1.4. Lack of legal harmonization across jurisdictions
6. Trustworthiness
6.1. Key Factor is Provenance
6.1.1. Knowing where the data comes from and knowing the source proven reliable in the past.
6.1.1.1. More eyes mean fewer lies, and fewer mistakes.
6.1.1.2. If you do not understand the data, you should not trust it.
6.1.2. Applies to Mostly News articles and blogs
6.2. Occurs at the following levels.
6.2.1. Raw data
6.2.2. Analytic Methods
6.2.3. Analyst
6.2.4. Employer Organization
6.3. Comprehensive metadata
6.3.1. Understand the data and be able to track them back to origin.
6.3.1.1. Requires that an analysis should be reproducible
6.3.2. If you do not understand the data, you should not trust it.
6.3.2.1. A method cannot be trusted if it is being used to answer the wrong question.
6.3.2.1.1. Variants of this theory
7. Privacy & Confidentiality
7.1. The right to be left alone and not be bothered.
7.2. The right to be protected from government intrusion.
7.3. Power to selectively reveal oneself to the world.
7.4. Key aspect of one’s identity.
7.5. Privacy and Data
7.5.1. Only exist relative to the person from whom we wish to keep something hidden.
7.5.1.1. Dependent on the context and relationship between giver and receiver of data.
7.5.1.2. Dependent on where the data will be put.
7.5.2. Adverse effect on the value of data sets if they allow people to opt in or opt out of being included.
7.5.3. Sometimes it is unethical not to use available data.
7.5.3.1. Strike a balance between different kinds of risks and different kinds of gains.
7.5.3.1.1. Risks and gains were clear and measurable
7.6. Privacy Paradox
7.6.1. Point out that the privacy balance is rather more nuanced than is typically understood.
7.6.1.1. Offer privacy with one hand while creating privacy risks with the other.
7.7. Personal Data and Privacy
7.7.1. Names, addresses, usernames, passwords, account codes, e-mail addresses, and ID numbers.
7.7.1.1. Information is removed from a database, then the entries are anonymized, they cannot be matched to their owners.
8. Data Technology VS. Other Advanced Technologies
8.1. Data Technology requires careful consideration of ethical issues for the following reasons:
8.1.1. Aspect of societal infrastructure
8.1.2. Interconnectedness of data
8.1.3. Dynamic nature of data
8.1.4. Real-tome and online analysis and decision making
8.1.5. Synergistic analysis through merging and combination of data sets
8.1.6. Lack of space, time and social context limitation
8.1.7. Ability to use for unexpected purposes & reveal unexpected information
8.1.8. Risk of exceptional intrusiveness
8.1.9. Potential for misuse, privacy breach, blackmail and other crimes
8.1.10. Subtle ownership issues
9. Ethical codes for data collection, manipulation
9.1. Providing guidance on how to behave in difficult circumstances
9.2. Preserving privacy in a way that users and the public will find acceptable
9.3. Ensuring that data will benefit the public
9.4. Reassuring customers, the public and others about an organization's integrity
9.5. Reassuring employees that they work for a trust- worthy organization
10. DATA
10.1. Comparted to oil in that it is processed to extract value
10.1.1. However data are not consumed like oil and gas
10.1.2. Data can be reused
10.1.3. Cannot be depleted
10.2. Numbers are not data without metadata
10.2.1. Minimum meta data are unit of measurement
10.2.1.1. same as making comparison to two objects
10.2.2. Can data make sense outside of a theory or world view?
10.3. How data is used is important
10.3.1. bringing different sets of data together and comparing them in different ways can create different uses.
10.3.2. Unlimited ways it can be used
10.4. Captured automatically in most cases.
10.4.1. No additional time or resources needed to capture
10.4.2. This is considered observational data
10.4.2.1. not at risk for being distorted
10.4.3. Administrative data
10.4.3.1. can tell
10.4.3.1.1. What people are
10.4.3.1.2. What people do
10.4.3.2. differentiate from
10.4.3.2.1. What people say
10.4.3.2.2. What people claim
10.4.4. If it might be useful record and store it
10.4.4.1. Cheap to record
10.4.4.2. Contrary to minimization principle
10.5. Individuals cast long data shadows
10.5.1. used in tracking individuals
10.5.2. emails
10.5.3. Possibility driven attitude
10.5.4. Credit card activity
10.5.5. Web activity
10.5.6. phone calls
10.5.7. can linger for extended periods
11. Personal Data
11.1. When combined with other personal data can tell you much about other people whos data you may not have
11.2. The most ethical of all issues regarding data
11.3. GDPR
11.3.1. Replaces UK's Data protection Act (DPA)
11.3.2. EU wide regulation protecting individuals
11.3.3. Increases accountability of those processing personal data
11.3.4. Details obligation associated with personal data
11.3.5. Defines personal data as: any information relating to an identified or identifiable natural person
11.3.5.1. an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person’’
11.3.6. dictates how personal data can be used.