Ethical Principals For AI and ML

At accentedge we adhere to the following ethical principles regarding AI as recommended by The Institute for Ethical AI & Machine Learning as follows:

Human augmentation

We commit to assess the impact of incorrect predictions and, when reasonable, design systems with human-in-the-loop review processes.

Bias evaluation

We commit to continuously develop processes that allow us to understand, document and monitor bias in development and production.

Explainability by justification

We commit to develop tools and processes to continuously improve transparency and explainability of machine learning systems where reasonable.

Reproducible operations

We commit to develop the infrastructure required to enable for a reasonable level of reproducibility across the operations of ML systems.

Displacement strategy

We commit to identify and document relevant information so that business change processes can be developed to mitigate the impact towards workers being automated.

Practical accuracy

We commit to develop processes to ensure our accuracy and cost metric functions are aligned to the domain-specific applications.

Trust by privacy

We commit to build and communicate processes that protect and handle data with stakeholders that may interact with the system directly and/or indirectly.

Data risk awareness

We commit to develop and improve reasonable processes and infrastructure to ensure data and model security are being taken into consideration during the development of machine learning systems.