While the private sector has struggled with unethical uses of data, government can learn from these mistakes and craft best practices by creating a data framework that is both principled and innovative.
In the last decade, we’ve seen amazing advances in technology that have made it easier for companies to create efficiencies, provide client services, and reach the ideal audience for their messages. With the rise of big tech, we’ve seen a surge in big data. This allows organizations unprecedented access to an individual’s personal information, with little control over how and why that data is used. Machine learning and predictive analytics are becoming more ingrained in our daily lives — the shows we watch, the social groups we join, and the transit routes we take are all based on unseen algorithms.
While I’m excited for the new opportunities these technological advances present, I’m concerned we are losing sight of an important aspect of how we, as leaders in the public tech space, leverage new technologies while putting the customer first. We need to think about the ethics of data and what an ethical data program looks like. When creating an ethical data framework, we should fulfill these six principles: Ownership, Transparency, Openness, Privacy, Consent, and Literacy.
While algorithms can have an arguably minimal impact on our daily lives, as the examples above suggest, they can also define someone’s future — including job application processes, higher education selection processes, and an increased use of facial recognition software in public spaces. While well-intentioned — and some not-so-well-intentioned — organizations believe that their data models are objective measures of reality, it’s impossible to escape the subjective. All of this comes down to the data we collect and how we use it.
When thinking about government use of AI and advanced analytics, we have the responsibility to dictate how data is being collected, used and stored in order to mitigate potential impacts on our most vulnerable populations. And as governments increase their use of big data and algorithmic systems, we have a mandate to understand the unintended consequences this has on equity and social and economic opportunity for our residents.
Technology is beginning to reach into even the dustiest corners of government back offices. We’re seeing a great variety of small, disparate systems and a handful of massive catch-all monoliths come online. These systems create a tangle of data. It’s a net positive that governments are modernizing and using tools to create efficiencies, but we need to think holistically around data management and ask ourselves, “we can do this, but should we?”
So how do we do this? What guidelines, policies, and best practices can we put in place to shape the acceptable use of (often sensitive) data as we continue to evolve in this fast-paced tech world?
When standing up a customer relationship management (CRM) system, data system or program, the first step is to ask questions of your data and of your users. Often, the people setting up your systems are experts in the system, not in a particular use case. To make certain users are solving what they intend to solve, start by asking basic “who, what, when, where, why, and how” questions. This allows people to document the decision-making process and iterate where necessary in the future, while providing transparency.
Some examples include:
By implementing a discovery mindset, we can identify areas of risk, places where bias can be introduced or exacerbated and data points that may not be necessary to collect while providing a road map for future innovation and iteration.
The next step, which ensures the application of Ownership, Transparency, Openness, Privacy, Consent, and Literacy, is governance. By creating an intentional governance structure at the policy level, and providing every employee who touches data with clear, concise guidelines, we can start building strong foundations of not only data literacy but data ethics.
An ethical data governance structure should incorporate the following principles:
We’ve seen the detrimental impact of unethical uses of data and AI in private companies. Governments have to learn from the mistakes of others while not stunting innovation. We can take best practices, and lessons learned from others, and apply them to the public space. We know bias exists, we know seemingly benign decisions in data collection cause unforeseen consequences, and we don’t have to repeat the same missteps we’ve seen play out over the last decade. Government has a duty to be mindful and intentional in its data collection and usage practices. By creating an ethical framework within public institutions, we are poised for a future where government is seen as a trusted innovator when providing services for all residents, including the most vulnerable.
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