Twelve Principles of Data Ethics
23 Jun 2016

Twelve Principles of Data Ethics

Ethical Resolve has helped author Accenture’s newly

23 Jun 2016

Ethical Resolve has helped author Accenture’s newly released Data Ethics report, and in particular took the lead role in writing the section Developing a Code of Data Ethics. Steven Tiell and I hashed these out with the assistance of multiple contributors. The full report is available here. These 12 universal principles of data ethics are intended to help enterprises and professional communities develop tailored codes of ethics to guide responsible data use. Let us know if your organization needs assistance instantiating these principles.

A set of universal principles of data ethics can help guide data science professionals and practitioners in creating a code of data ethics that is specific and contextual for their organization or community of stakeholders:

 1. The highest priority is to respect the persons behind the data.

Where insights derived from data could impact the human condition, the potential harm to individuals and communities should be the paramount consideration. Big data can produce compelling insights into populations, but those same insights can be used to unfairly limit an individual’s possibilities.

2. Account for the downstream uses of datasets.

Data professionals should strive to use data in ways that are consistent with the intentions and understanding of the disclosing party. Many regulations govern datasets on the basis of the status of the data: “public,” “private” or “proprietary”, for example. But what is done with datasets is ultimately more consequential to subjects/users than the type of data or the context in which it is collected. Correlative use of repurposed data in research and industry represents the greatest promise and the greatest risk of data analytics.

3. The consequences of utilizing data and analytical tools today are shaped by how they’ve been used in the past.

There’s no such thing as raw data. All datasets and accompanying analytic tools carry a history of human decision-making. As far as possible, that history should be auditable. This should include mechanisms for tracking the context of collection, methods of consent, chains of responsibility, and assessments of data quality and accuracy.

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. 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 can’t 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.

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