Building digital trust: The role of data ethics in the digital age

4. Seek to match privacy and security safeguards with privacy and security expectations.

Data subjects hold a range of expectations about the privacy and security of their data. These expectations are often context-dependent. Designers and data professionals should give due consideration to those expectations and align safeguards and expectations with them, as much as possible.

5. Always follow the law, but understand that the law is often a minimum bar.

Digital transformations have become a standard evolutionary path for businesses and governments. However, because laws have largely failed to keep up with the pace of digital innovation and change, existing regulations are often miscalibrated to current risks. In this context, compliance means complacency. To excel in data ethics, leaders must define their own compliance frameworks to outperform legislated requirements.

6. Be wary of collecting data just for the sake of having more data.

The power and peril of data analytics is that data collected today will be useful for unpredictable purposes in the future. Give due consideration to the possibility that less data may result in both better analysis and less risk.

7. Data can be a tool of both inclusion and exclusion.

While everyone should have access to the social and economic benefits of data, not everyone is equally impacted by the processes of data collection, correlation, and prediction. Data professionals should strive to mitigate the disparate impacts of their products and listen to the concerns of affected communities.

8. As far as possible, explain methods for analysis and marketing to data disclosers.

Maximizing transparency at the point of data collection can minimize the more significant risks that arise as data travels through the data supply chain.

9. Data scientists and practitioners should accurately represent their qualifications (and limits to their expertise), adhere to professional standards, and strive for peer accountability.

The long-term success of this discipline depends on public and client trust. Data professionals should develop practices for holding themselves and their peers accountable to shared standards.

10. Aspire to design practices that incorporate transparency, configurability, accountability, and auditability.

Not all ethical dilemmas have design solutions. But paying close attention to design practices can break down many of the practical barriers that stand in the way of shared, robust ethical standards. Data ethics is an engineering challenge worthy of the best minds in the field.

11. Products and research practices should be subject to internal (and potentially external) ethical review.

Organizations should prioritize establishing consistent, efficient, and actionable ethics review practices for new products, services, and research programs. Internal peer-review practices help to mitigate risk, and an external review board can contribute significantly to public trust.

12. Governance practices should be robust, known to all team members and regularly reviewed.

Data ethics poses organizational challenges that cannot be resolved by compliance regimes alone. Because the regulatory, social, and engineering terrains are in flux, organizations engaged in data analytics need collaborative, routine and transparent practices for ethical governance.

Trust can be improved at the beginning of a data supply chain by making informed consent a priority. Where do you start? Explore guidelines for avoiding harm across data disclosure, manipulation and consumption:

Data Manipulation

DATA AT REST

Data is stored locally without widespread distribution channels; all transformations happen locally

Guideline: Set up a secure environment for handling static data so the risk of security breaches is minimized and data is not mistakenly shared with external networks. Data movement and transformation should be fully auditable

DATA IN MOTION

Data is actively being moved or aggregated; data transformations use multiple datasets or API calls which might be from multiple parties; the Internet may be used

Guideline: Ensure that data moving between networks and cloud service providers is encrypted; shared datasets should strive to minimize the amount of data shared and anonymize as much as possible. Be sure to destroy any temporary databases that contain aggregated data. Are research outcomes consistent with the discloser’s original intentions?

Trust can be improved at the beginning of a data supply chain by making informed consent a priority. Where do you start? Explore guidelines for avoiding harm across data disclosure, manipulation and consumption:

Data Consumption

DATA AT REST

Data analytics processes do not rely on live or real-time updates

Guideline: Consider how comfortable data disclosers would be with how the derived insights are being applied. Gain consent, preferably informed consent, from data disclosers for application-specific uses of data.

DATA IN MOTION

Data insights could be context-aware, informed by sensors, or might benefit from streamed data or API calls

Guideline: The data at rest guidelines for data consumption are equally important here. In addition, adhere to any license agreements associated with the APIs being used. Encrypt data. Be conscious of the lack of control over streamed data once it is broadcast. Streaming data also has a unique range of potential harms—the ability to track individuals, deciphering network vulnerabilities, etc.