[Paper Reading] An Introduction to Data Ethics (2018)

by Shannon Vallor, William J. Rewak

It’s been a while (college years) since I formally looked at (the philosophy around) ethics. This was a great introduction to how we apply our understanding of ethics to the prevalence and impact of big data today. At conferences like FACCT, you find many of these ontologies/taxonomies (I tried to outline from this paper in this post). The spaces in and around ML have many similar frameworks peppered throughout the literature. Below is the outline of this overview:

Introduction - Why Ethics for Data?

• The purpose of ethics is to understand how to best live.

• IEEE has a group on technological ethics.

• Technologies are not ethically ‘neutral’, for they reflect the values that we ‘bake in’ to them with our design choices, as well as the values which guide our distribution and use of them. Technologies both reveal and shape what humans value, what we think is ‘good’ in life and worth seeking.

• We can define a harm or a benefit as ‘ethically significant’ when it has a substantial possibility of making a difference to certain individuals’ chances of having a good life.

Benefits, Harms, and Challenges

In the context of data practice, the potential harms and benefits are no less real or ethically significant, up to and including matters of life and death. But due to the more complex, abstract, and often widely distributed nature of data practices, as well as the interplay of technical, social, and individual forces in data contexts, the harms and benefits of data can be harder to see and anticipate. 

Benefits

• Human understanding

• Economic efficiency

• Predictive accuracy/personalization

Harms

• Privacy/security

• Fairness/justice

• Transparency/autonomy

Challenges

• Control + autonomy

• Storage + security

• Hygiene + relevance

• Validation + testing

• Human accountability

• User training

• Broader impacts

Practitioner Obligations

Ethical decision-making thus requires cultivating the habit of reflecting carefully upon the range of stakeholders who together make up the ‘public’ to whom I am obligated, and weighing what is at stake for each of us in my choice, or the choice facing my team or group. 

Virtue Ethics

Consequential/Utilitarian Ethics

Deontological Ethics

Best Practices

1. Data ethics: in spotlight out of compliance

2. Consider human lives/interests behind data

3. Focus on downstream risks and uses

4. Envision ecosystem

5. Mind the gap between expectation + reality

6. Treat data as conditional good

7. Avoid dangerous hype

8. Chains of responsibility/accoundability

9. Data disaster planning and crisis response

10. Disparate resources / impacts / interests

11. Diverse stakeholder input

12. Design for privacy / security

13. Standards should be pervasive, iterative, rewarding

14. Transparency/autonomy/trustworthiness

Paper: https://www.scu.edu/media/ethics-center/technology-ethics/IntroToDataEthics.pdf