The alpha Principles for Ethical Use set out six points to align the use of data-driven technologies within government processes, programs and services with ethical considerations and values. Our team has undertaken extensive jurisdictional scans of ethical principles across the world, in particular the US the European Union and major research consortiums. The Ontario “alpha” principles complement the Canadian federal principles by addressing a gap concerning specificity. Ontario’s principles support our diverse economic ecosystem by not clashing with existing best practices, principles and frameworks. This approach references and harmonizes with known standards, principles and tools to create clarity rather than barriers for innovation that is safe, responsible and beneficial.
We’re in the early days of bringing these principles to life. We encourage you to adopt as much of the principles as possible, and to share your feedback for us. You can send us an email [email protected], or see CONTRIBUTING.md for more details.
You can also check out the Transparency Guidelines
1. Transparent and Explainable
4. Accountable and Responsible
There must be transparent and responsible disclosure around data-driven technology like AI, automated decisions and ML systems to ensure that people understand outcomes and can discuss, challenge and improve them.
Where automated decision making has been used to make individualized and automated decisions about humans, meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for the data subject should be available.
There is no way to hold data-driven technologies accountable, particularly as they impact various historically disadvantaged groups if the public is unaware of the algorithms and automated decisions the government is making. Transparency of use must be accompanied with plain language explanations for the public to have access to and not just the technical or research community. For more on this, please consult the Transparency Guidelines.
Data-driven technologies should be designed in a way that respects the rule of law, human rights, democratic values and diversity, and they should include appropriate safeguards to ensure a fair and just society.
Designers, policy makers and developers should respect the rule of law, human rights and democratic values, throughout the AI system lifecycle. These include freedom, dignity and autonomy, privacy and data protection, non-discrimination and equality, diversity, fairness, social justice, and internationally recognized labor rights.
Algorithmic and machine learning systems evolve through their lifecycle and as such it is important for the systems in place and technologies to be good and fair at the onset, in their data inputs and throughout the lifecycle of use. The definitions of good and fair are intentionally vague to allow designers and developers to consider all of the users both directly and indirectly impacted by the deployment of an automated decision making system.
Data-driven technologies like AI and ML systems must function in a robust, secure and safe way throughout their life cycles and potential risks should be continually assessed and managed.
Designers and developers should implement mechanisms and safeguards, such as capacity for human determination and complete halt of the system operations, that are appropriate to the context and predetermined at initial deployment.
Creating safe data-driven technologies means embedding safeguards throughout the life cycle of the deployment of the algorithmic system. Automated algorithmic decisions can reflect and amplify undesirable patterns in the data they are trained on. Despite our best efforts there will be unexpected outcomes and impacts. Systems will require ongoing monitoring and mitigation planning to ensure that if the algorithmic system is making decisions that are no longer agreeable that a human can adapt, correct or improve the system.
Organizations and individuals developing, deploying or operating AI systems should be held accountable for their ongoing proper functioning in line with the above principles. Algorithmic systems should be periodically peer-reviewed or audited to ensure that unwanted biases have not inadvertently crept in over time.
Where AI is used to make decisions about individuals there needs to be a process for redress to better understand how a given decision was made.
In order for there to be accountability for decisions that are made by an AI or ML system a person, group of people or organization needs to be identified prior to deployment. This ensures that if redress is needed there is a preidentified entity that is responsible and can be held accountable for the outcomes of the algorithmic systems.
The processes and outcomes behind an algorithm should always be developed with human users as the main consideration. Human centered AI should reflect the information, goals, and constraints that a human decision-maker weighs when arriving at a decision.
Keeping human users at the center entails evaluating any outcomes (both direct and indirect) that might affect them due to the use of the algorithm. Contingencies for unintended outcomes need to be in place as well, including removing the algorithms entirely or ending their application.
Placing the focus on human user ensures that the outcomes do not cause adverse effects to users in the process of creating additional efficiencies.
In addition, Human-centered design is needed to ensure that you are able to keep a human in the loop when ensuring the safe operation of an algorithmic system. Developing algorithmic systems with the user in mind ensures better societal and economic outcomes from the data-driven technologies.
Data-driven technologies like AI or ML shall be developed with consideration of how it may apply to specific sectors or to individual cases and should align with the Canadian Charter of Human Rights and Freedoms and with Federal and Provincial AI Ethical Use.
Other biproducts of deploying data-driven technologies such as environmental, sustainability, societal impacts should be considered as they apply to specific sectors and use cases and applicable frameworks, best practices or laws.
Algorithmic systems and machine learning applications will differ by sector and user. As a result, while the above principles are a good starting point for developing ethical data-driven technologies it is important that additional considerations be given to the specific sectors and environments to which the algorithm is applied.
Experts in both technology and ethics should be consulted in development of data-driven technologies such as AI to guard against any adverse effects (including societal, environmental and other long-term effects).