Our client was a prominent Australian brand that was increasingly processing large volumes of data in order to derive business insights.
Background
As a leading Australian consumer goods corporation, our client relied on extensive datasets to shape its go-to-market strategies. Through the use of Machine Learning (ML) and Artificial Intelligence (AI) algorithms, data is processed to deliver insights that contribute to a range of business decisions. The client sought to ensure that its data processing activities would not result in unintended privacy breaches, and that biases in algorithmic outputs would not result in unethical decision-making.
Our client required a framework to assess whether its practices could result in unintended privacy breaches or ethical compromises.
Our role
elevenM worked collaboratively with the client to understand how it uses ML and AI algorithmic models within its business processes. This included investigating data sources, the processes around de-identifying data, and how consumer behaviour data was crunched with demographic statistics to deliver insights that shaped business decisions.
The client wanted to ensure that none of its data processing activities compromised the privacy of its customers, nor resulted in biased decision-making that did not align with its ethical commitments.
As a result of this collaboration, elevenM developed a repeatable framework that included extensive questionnaires, templates and facilitators’ guides. These were designed to be used when running internal workshops to assess potential privacy implications, and to identify unintended biases with ethical consequences.
This framework ensured the client’s data team could bring together all relevant stakeholders across the organisation, in order to build consistent approaches to privacy and data ethics challenges.
What we did
elevenM developed a program which:
- Assessed the client’s data processing practices, especially the use of ML and AI
- Developed a range of artefacts, including questionnaires, templates and facilitators’ guides, so the client could run repeatable internal workshops
- Developed a data ethics framework that enables the client to asses potential ethical consequences of its data processing practices