A Concept for Data Practices.

What's the significance and value of a social license to operate in the data industry? The mining industry is often offered as an example of an industry required to earn a social license, and maintain it, by behaving in a trustworthy and responsible way; if it were otherwise, it would face costly delays and interference in its activities. Analogies have been drawn between a social license for the mining of minerals and for the process of data mining/processing. What the social license emphasizes is the possible need for those (whether they are public or private bodies or specific occupational groups) undertaking activities likely to provoke public disquiet to go ‘beyond compliance’ with legal requirements.

Challenges experienced in data practices

Data practices and relationships are complex and changing very fast. This means that existing institutions and frameworks are not up to the task. There is no clear, authoritative guidance for public or private sector entities in sharing and using data. Moreover, members of the public have little guidance as to what is an acceptable or unacceptable use of their data.

Public and private sector organizations have rich data but are unwilling or feel unable to share even when there could be a large benefit from doing so — because acceptable boundaries for sharing and reuse are unclear.

Public trust in the data use ecosystem is tenuous and, once lost, can be hard to restore. Maintaining public confidence is vital to ongoing data innovation. No independent trusted forum exists for an inclusive conversation on data use.

Development of trusted data practices frameworks — many restrictions are due to concerns about appropriate use and interpretation of data, concerns about unintended consequences, the accidental release of sensitive data, and adherence to legislation. Frameworks for trusted data practices would help address these challenges.

Actions that can be taken.

Finding out what features of data use matter the most by providing guidance. The guidelines are voluntary and aim to provide assistance to organizations wishing to increase trust in their data practices. Any organization (a local council, government agency, or company) is free to adopt the guidelines and make them mandatory within their organization. The data landscape is changing rapidly, so highly prescriptive guidelines would quickly become dated. In such an environment, we believe that transparency is central. We expect these questions will continue to be relevant to how comfortable people feel about their data being used, regardless of changes in technology.

Government agencies and nongovernmental organizations expect that, by building the guidelines into their data practices, government agencies and NGOs will increase trust and improve their relationships with clients. Information given freely to an organization that is trusted is likely to be of higher quality, which means better service delivery, better research, and better policy development.

Companies The examples provided with the guidelines show that companies, both here and overseas, vary considerably in their data practices. How companies collect and use data from their customers and the ways they communicate this range from quite poor to excellent. Companies have an opportunity to gain a competitive advantage by ‘doing data’ in a transparent and trusted way, improving customer loyalty and data quality. When all companies reach a high standard of data use, it will add to the country’s reputation as a place that is good for conducting business.

The guidelines are predicated on the basis that transparency about data use is beneficial. However, in cases such as security intelligence agencies and the activities of the Police, being transparent about data would undermine the agency’s effectiveness. Nevertheless, all agencies may find that they can be transparent about data uses and purposes in general terms and that the guidelines can usefully be applied to at least some of their data activities.

Data sharing.

Despite the potential benefits of increased data sharing and the establishment of state-level legislative reform around public data sharing, data sharing within government remains a challenge for several reasons.

Many data custodians remain hesitant to share data due to concerns around appropriate use and interpretation of data, about unintended consequences of sharing data, the accidental release of sensitive data, and adherence to privacy legislation.

Aggregation of individual data is a standard approach to reduce the risk that personal information is included in a shared dataset. Part of the data-sharing challenge is that there is no way to unambiguously determine if there is personal information in aggregated data.

Data and human rights.

Well, yes, data is enormously valuable, but the flip side is that all this personal information can lead to intrusions and interferences with people’s private lives. That can be distressing and damaging for individuals. It can give those who are in control of data power over people in a potentially dangerous way for society and democracy. Even more so, if there aren’t proper protections in place.

It’s vital that data protection laws(this is where the social license to operate comes in) are underpinned by a respect for fundamental human rights. That’s because the storage and use of personal information should be at the service of people. To ensure this happens, data protection laws should take into account people’s right to a private life

As much as human rights for data protection are in place it’s important to understand the significance social license would have in enhancing this.

Why do we need a framework such as a social license?

Literally, all agencies, that have access to data are facing the overarching challenge of creating privacy-preserving, ethical frameworks that support automated data sharing to facilitate the deployment and creation of smart services. The intention is to identify and develop working frameworks for data governance, privacy preservation, and practical data sharing.

Data Science and other shenanigans!

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