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Big Data and Ethical Innovation


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Andrej Zwitter - The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. Responses will have to take into account these limitations and shift the responsibility for ethical conduct to the engineering and data scientist side of data driven innovation. A code of conduct is not enough - innovation in a datafied society needs to abide by principles guiding "ethics by design" and responsible use of data.

Published in: Law
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Big Data and Ethical Innovation

  2. 2. HOW DIFFERENT IS BIG DATA • Proclivities and Differences: • From Tidy to Messy • From Causality to Correlation • From Individuals to Groups (from PII to DII) • From Individual Agency to Networked Agency • From Regulation to Nudging • From Prediction to Prevention • A code of conduct for Big Data or Data Science in general
  3. 3. DATA HANDLING, ANALYSIS, REPRESENTATION • Biases in datasets – non-intentional biases; intentional biases; structural biases • Data cleaning – raw / technically correct / tidy / aggregate / meta – DATA • Analysis – input-output disconnect • Machine learning – supervised / unsupervised • Objectiveness of dataset, cleaning process & analysis • Representation • Values or Graphs: audience dependency, audience ability, interpretation and application • Output-action disconnect (leads further into an action-input disconnect)
  4. 4. HUMAN(E) BIG DATA • Technical and ethical standards as prerequisites of professionalism • Basic principles of the practice • Skillful execution • Loyalty to the trade • Representation of the trade • Basic principles of societal ethical standards • Harm principle • Trust principle • Charity principle
  5. 5. DATA SCIENCE CODE OF CONDUCT (DSA) • Rule 2 – Competence • Rule 3 – Scope of Data Science Professional Services Between Client and Data Scientist • Rule 4 – Communication with Clients • Rule 5 – Confidential Information • Rule 6 – Conflicts of Interest • Rule 7 – Duties to Prospective Client • Rule 8 – Data Science Evidence, Quality of Data and Quality of Evidence • Rule 9 – Misconduct
  6. 6. DATA FOR HUMANITY • Big data as a tool in need of rules (Zicari & Zwitter 2015) • Passive and active duties dependent on the profession • Do no harm • Ensure peaceful coexistence • Help people in need • Protect the environment • Eliminate discrimination
  7. 7. UNIVERSAL CODE OF PROFESSIONAL CONDUCT Do no Harm Harm contingent on: - Definition of Society - Membership in Society - Societal needs are more pressing Do your best Contractual Obligation to: Employer and Clients Natural obligation to: Family or Society and Class of Work The right reasons Intentio Recta & Virtues: •Justice •Prudence •Courage •Temperance Living with your deeds Ex ante perspective on decisions individually and as a group Living with society Reciprocal and relative responsibility in relation to role in society (Trust) (Leadership) (Decision- making-power)
  8. 8. ETHICS BY DESIGN Identify Assess Avoid Reduce Design Monitor Evaluate Improve