Big Data Ethics Initiative

Big data processing creates significant accountability challenges for data controllers and the enforcement agencies that oversee data protection and privacy. Big data uses large diverse data sets and seeks insights from the correlations that exist between the data. Big data both stretches the purposes for which data was originated and creates insights, which are new data elements. This analysis is conducted at a distance from the individual, so governance concepts such as consent are not fully effective.

To be trustworthy, big data analytics must be governed. This requires organisations to assess the processes to assure they are fair and consistent with the intent of data protection laws. The information policy governance process has lagged the development of big data.

The Foundation’s Big Data Ethical Framework Initiative commenced in the Spring of 2014 with two key objectives:

  • Create a mechanism for organisations when assessing an organisation’s big data projects so that they will have the knowledge to make decisions on proceeding with a big data project.
  • Create an oversight mechanism that regulators trust.

The Big Data Ethical Framework Initiative establishes legitimacy for big data projects. The legitimacy fills the governance gap in countries outside the United States where one needs legal permission to conduct big data analysis and globally where consent is insufficient. In the United States, legitimacy helps fill the trust gap that is often lacking.

The initiative tackled these problems by breaking them into four parts. Part A, the Unified Ethical Frame, was completed in October 2014 and creates a theoretical basis for legitimacy. Based on that theoretical analysis, Part B creates an assessment framework for establishing legitimacy, while Part D is a library of assessment questionnaires, which would be industry or business specific. Part C builds an enforcement process that would be trusted by regulators. The initiative creates practical tools that fill existing corporate needs and an approach that could very well preclude regulations that might stifle data driven innovation.

For more information, contact Martin Abrams at

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