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DMBOK - Chp 9
Ethics
| Question | Answer |
|---|---|
| Ethics | Principles of behavior based on ideas of right and wrong. |
| Ethics of data handling focuses on | Impact on people Potential for misuse Economic value of data |
| Ethical data handling business drivers | Increase the trustworthiness of an organization, its data, and outcomes Reducing the risk that data will be misused |
| Ethical Principle: Respect for Person | People be treated in a way that respects their dignity and autonomy as human individuals |
| Ethical Principle: Beneficence | Do not harm; Maximize possible benefits and minimalize possible harms |
| Ethical Principle: Justice | Consider the fair and equitable treatment of people |
| GDP Principle: Fairness, Lawfulness, Transparency | Personal data shall be processed lawfully, fairly, and in a transparent manner in relation to the data subject |
| GDP Principle: Purpose Limitation | Personal data must be collected for specified, explicit, and legitimate purposes, and not processed in a manner that is incompatible with those purposes |
| GDP Principle: Data Minimization | Personal data must be adequate, relevant, and limited to what is necessary in relation to the purpose for which they are processed |
| GDP Principle: Accuracy | Personal data must be accurate, and where necessary, kept up-to-date. Every reasonable step must be taken to ensure that personal data that are inaccurate, having regard to the purpose for which they are processed, are erased or rectified without delay. |
| GDP Principle: Storage Limitation | Data must be kept in a form that permits identification of data subjects [individuals] for no longer than is necessary for the purpose for which the personal data are processed. |
| GDP Principle: Integrity and Confidentiality | Data must be processed in a manner that ensures appropriate security of the personal data, including protection against unauthorized or unlawful processing and against accidental loss, destruction or damage. |
| GDP Principle: Accountability | Data Controllers shall be responsible for, and be able to demonstrate compliance with these principles |
| Personal Information Protection and Electronic Documents Act (PIPEDA) | Canadian privacy law that applies to every organization that collects, uses, and disseminates personal information in the course of commercial activities. |
| FTC Fair Information Processing Principles (2012) | Notice / Awareness Choice / Consent Access / Participation Integrity / Security Enforcement / Redress |
| Risk of Unethical Data Handling practices: timing | It is possible to lie through omission or inclusion of certain data points in a report or activity based on timing. E.g., equity market manipulation through 'end of day' stock trades |
| Risk of Unethical Data Handling practices: misleading visualizations | Charts and graphs can be used to present data in a misleading manner. E.g., changing scale can make a trend line look better or worse |
| Risk of Unethical Data Handling practices: Unclear definitions or invalid comparisons | When required context is left out, the surface of the presentation may imply meaning that the data does not support. |
| Risk of Unethical Data Handling practices: Bias | Refers to an inclination of outlook. Can be introduced at different points in the lifecycle, when data is collected or created, when it is selected for inclusion, methods for analysis, and presentation. |
| Transforming and Integrating Data risk: Limited knowledge of data's origin and lineage | If an organization does not know where data came from and how it has changed as it has moved between systems, then the organization cannot prove that the data represents what they claim it represents. |
| Transforming and Integrating Data risk: Data of poor quality | Without confirmation of data quality, an organization cannot vouch for the data and data consumers may be at risk or put others at risk when they use the data |
| Transforming and Integrating Data risk: No documentation of data remediation history | Organizations should have auditable information related to the ways data has been changed, following a formal, auditable change control process. |
| Obfuscation/Redaction: Data aggregation | Aggregating across dimensions, and removing identifying data, a dataset can still serve an analytic purpose without concern for disclosing personal identifying information (PII) |
| Obfuscation/Redaction: Data marking | Used to classify data sensitivity and to control release to appropriate communities |
| Obfuscation/Redaction: Data masking | Practice where only appropriate submitted data will unlock processes. Operators cannot see what the appropriate data might be. |
| Ethical Data Handling Strategy components | Value statements Ethical data handling principles Compliance framework Risk assessments Training and communications Roadmap Auditing and monitoring |
| Use of personal data should have a disciplined approach to | How populations for studies are selected How data will be captured What activities analytics will focus on How the results will be made accessible |
| Ethical risks in a sampling project include | Identification (e.g., Demographics and selection) Behavior capture (e.g. capture method and activities) BI/Analytics/Data Science (e.g., profiling prospects) Results (e.g. benefit or sanction, biased treatment) |
Created by:
sshupert