Challenge the status quo with AI-supported decision making, Generative AI, Smart Search and Cognitive Analytics, delivered with transparent ethics and minimum bias.
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Talk to an ExpertWe deliver Advanced Analytics that stand up to external scrutiny & consider moral outcomes for all.
We are versed in open source ML tools and major deep learning libraries, and are at home in both Python and R.
We minimise reporting and selection biases through delivery methods that favour breadth and consider all data.
We seek diverse representation on project teams and deliver with transparent stage-gates to reduce implicit bias.
Delivery of advanced analytic solutions requires a considered approach to ensure that the data assets provide tangible results and uphold societal expectations .
In complex enterprise environments, trusting the professionals, means minimising risk to reputational damage and loss of customer goodwill. Revenite staff are active participants in data ethics forums and attempt to minimise bias in built models through mechanisms such as data sanitisation, independent testing & review and delivering observable models.
Our delivery approach considers data sensitivity levels enabling data custodians to prioritise security controls and decide which data requires more stringent protection measures. This classification helps define the data handling, processing, storage, and disposal requirements. Our solutions consider attacks that could compromise the security of the system and fit within our customers regulatory requirements and mandates. In turn, these are often informed by standards such as the General Data Protection Regulation (GDPR), the Payment Card Industry Data Security Standard (PCI DSS), Victorian Protective Data Security Standards (VPDSS) and NIST.
Additionally, privacy is essential for maintaining trust with customers and stakeholders. When individuals share their personal data with an organisation, they expect that it will be used in a responsible and transparent manner. Failure to protect privacy can damage an organisation’s reputation and erode trust with customers and other stakeholders.
Clients trust us with their data but choose us because we deliver with fairness and transparency in mind. As a matter of principle, where possible, we avoid using sensitive data, such as race or gender, and employ independent testing with users to ensure the model does not exhibit bias.
