AI is rapidly becoming a pervasive force in our lives, and as such, has become increasingly important for digital ethics. AI can be used to make decisions and predictions faster and more accurately than humans, but it also introduces potential issues related to privacy, security, accuracy, and transparency.
Incorporating Digital Ethics into the production process is an essential approach to creating trustworthy and user-centric products. Doing so allows for the embedding of a human-centric focus throughout the product’s entire lifecycle, thus helping to systematically mitigate any potential harm that might arise from its development or usage. Companies must be cognizant of the potential risks associated with their product, including data privacy and security, algorithmic bias, surveillance, and censorship. Additionally, companies should prioritize digital ethics in order to ensure that products are designed responsibly, and with respect for human rights & dignity.
The most effective way to ensure digital ethics is upheld is by educating teams on its principles and having a clear set of guidelines in place. Companies should also establish processes for incorporating digital ethics into their product development cycles, as well as for monitoring, testing, and auditing their products for ethical compliance. Digital ethics must become part of an organisation’s culture from the top down to ensure that it is followed by all employees at every level.
MLOps provides companies with the ability to proactively measure and address digital ethics, both before and after the deployment of AI applications. It helps ensure that products are created in a way that respects human rights, data privacy and security, algorithmic bias, surveillance, and censorship. By incorporating Digital Ethics into the production process, MLOps enables teams to systematically identify and mitigate any potential harm that might arise from using AI.
Overall, MLOps greatly helps companies to ensure their AI applications are designed in an ethical and responsible manner. It provides the necessary tools, processes, feedback loops, and governance mechanisms for teams to monitor, test, and audit products for ethical compliance. In doing so, it can help organisations to create AI-powered applications and products that are more user-centric, trustworthy, transparent, and accountable.
MLOps is a powerful tool that enables organizations to uphold digital ethics. The following list shows how MLOps allows this:
Model Packaging and Deployment simplifies the deployment of models built using a variety of languages at scale on any cloud or on-premise. Successful user acceptance testing in preproduction triggers model deployment and integration with downstream applications. Governance tools are in place and appropriate communications to relevant stakeholders are cascaded.
Model Monitoring and Management enables continuous tracking of various performance measures against appropriate thresholds and benchmarks identifying retraining needs. Business ROI and other business metrics are also tracked to demonstrate benefits realisation.
Model Governance includes features such as model access control, an audit trail to provide transparency on the model’s functioning, and any other regulatory and compliance needs for model usage.
Model Security ensures the protection of models from being exposed to cyberattacks or inappropriately accessed by unauthorised users.
Model Discovery provides model registries or catalogs for models produced within the tool ecosystem as well as a searchable model marketplace that provides a way to locate consumable models, both internally developed as well as third-party models which are vetted against the Trustworthy AI framework.
Data Preparation ensures data governance practices are enacted to collect, label, cleanse and process appropriate data and that these measures are repeatable as the model is deployed.
ML Pipeline Development ensures use cases are selected and prioritised in line with the organisation’s strategies and values. It allows users to define repeatable and reusable steps for model development.
In conclusion, digital ethics and AI should be developed hand-in-hand. By integrating digital ethics into the MLOps process, companies can ensure their products are developed responsibly and ethically with respect for human rights and dignity. Doing so will help to mitigate any potential risks associated with AI, allowing organisations to develop trust in their products and services.
