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Twelve Principles of Data Ethics

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.

Avanade’s TechSummit 2016 panel on digital ethics

I recently had the honor of participating on a panel at Avanade’s annual TechSummit conference. Organized by Steven Tiell of Accenture’s TechVision team, we were tasked with discussing the role of digital ethics and digital trust in enterprise. I joined Steven on stage with Bill Hoffman, Associate Director of the World Economic Forum and Scott David, Director of Policy at the University of Washington Center for Information Assurance and Cybersecurity. Below are my prepared remarks, which of course differ extensively from what I actually got around to saying on stage.

1. We’ve seen ethics requirements for medical and academic research, particularly when federal dollars are at play. Why should businesses care about ethics in their research?

Businesses should care about ethics most of all because it is, by definition, the right thing to do. But to go beyond a pat answer, I think it is useful to define the domain of “ethics.” I think of ethics as the methods and tools you use to make a consequential decision when there is relatively little settled guidance about the right thing to do. If you knew the right thing to do, then it would probably be a matter for compliance or legal departments. I like how digital sociologist Annette Markham recently put it when discussing a major data research scandal: “ethics is about making choices at critical juncture,” particularly when those choices affect other people. What I would add to Annette’s definition is that ethics is not just the decisions, but also all the work you have to do in advance to enable those critical decisions. You need the capacity to identify and evaluate those critical junctures, and to then make efficient, consistent and actionable decisions. Done well, ethics is a future-oriented stance. In my opinion, building the habits and infrastructures that make it possible for business to make good choices at critical junctions is simply something that will be good for the bottom line in the long run. It will certainly enable businesses to identify and mitigate risks more effectively.

When it comes to the matter of research ethics in particular, there are three aspects that bear more scrutiny when considering how and why enterprises should engage in ethics review practices.

First, because businesses now hold more data about human behavior than any other entity in human history, the value of those businesses is increasingly indexed to what they can do with that data now and in the future. Thus, the types of research being done looks like the types of research that have traditionally been located in university settings. It should indicate something important to us that academic researchers and institutions have invested so much in handling research ethics: research practices carry significant risk and require sustained attention.

Second, anyone can now be a researcher and everyone is a research subject. Yet all of our familiar ethics norms and infrastructures make certain outdated assumptions about institutional boundaries that create formal and informal professional limits on who can do consequential research. But those assumptions do not hold when human data research happens everywhere. Without the familiar institutional boundaries, businesses will need to make up the slack somehow.

Third, big data research methods simply do pose new kinds of risks for enterprise. Holding so much private data and using that data to intervene in people’s’ lives in a tailored, personalized fashion, poses risks beyond simply privacy. Research is often perceived as creepy or controlling, where even products that do the same thing might not. Thus it is important to align design practices, product development and ethics review in a manner that users of your services or providers of your data can be comfortable with.

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Digital Trust at the Core of Accenture’s 2016 Vision

The partners of Ethical Resolve recently joined Accenture in their San Jose office to learn about Accenture’s 2016 Tech Vision. We have been collaborating with their staff on a report on data ethics to be released later in 2016.

We were pleased to hear about Accenture’s commitment to focusing on ethical issues in order to help their clients build digital trust with customers.

In particular, we agree that it is vital for companies  to focus on their stewardship of user data to ensure that this information is used responsibly and with the interests and rights of customers in mind. As we move further into 2016, it has become clear that one of the simplest approaches to data ethics is to implement effective processes for ethical decision making. What this means for companies is that any employee who makes decisions with ethical ramifications needs to have a clear and effective process for determining what is right thing to do.

Practices as simple as the use of checklists and templates for ethical decision making can greatly improve a company’s ability to properly manage ethical risks and build trust with their customers. With the proper implementation of customized processes for ethical decision making, companies can greatly improve their relationships with customers without undue difficulty.

We look forward to working more with Accenture to help offer processes that are easily adopted by clients to achieve the aim of greater digital trust between tech companies and their customers.