Overview

These Principles for Ethical Use set out six points to align the use of data enhanced technologies within government processes, programs and services with ethical considerations and values.

The Trustworthy AI team within Ontario’s Digital Service has undertaken extensive jurisdictional scans of ethical principles across the world, in particular New Zealand, the United States, the European Union and major research consortiums.

The Ontario “beta” 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 with us. You can email dawn.edmonds@ontario.ca for more details.

You can also check out the Transparency Guidelines (GitHub).

1. Transparent and explainable

There must be transparent use and responsible disclosure around data enhanced technology like AI, automated decisions and machine learning systems to ensure that people understand outcomes and can discuss, challenge and improve them. This includes being open about how and why these technologies are being used.

When automation has been used to make or assist with decisions, a meaningful explanation should be made available. The explanation should be meaningful to the person requesting it. It should include relevant information about what the decision was, how the decision was made, and the consequences.

Why it matters

Transparent use is the key principle that helps enable other principles while building trust and confidence in government use of data enhanced technologies. It also encourages a dialogue between those using the technology and those who are affected by it.

Meaningful explanations are important because they help people understand and potentially challenge outcomes. This helps ensure decisions are rendered fairly. It also helps identify and reverse adverse impacts on historically disadvantaged groups.

For more on this, please consult the Transparency Guidelines.

2. Good and fair

Data enhanced technologies should be designed and operated in a way throughout their life cycle that respects the rule of law, human rights, civil liberties, and democratic values. These include dignity, autonomy, privacy, data protection, non-discrimination, equality, and fairness.

Why it matters

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 life cycle of use. The definitions of good and fair are intentionally broad 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.

3. Safe

Data enhanced technologies like AI and ML systems must function in a safe and secure way throughout their life cycles and potential risks should be continually assessed and managed.

Designers, policy makers and developers should embed appropriate safeguards throughout the life cycle of the system to ensure it is working as intended. This would include mechanisms related to system testing, piloting, scaling and human intervention as well as alternative processes in case a complete halt of system operations is required. The mechanisms must be appropriate to the context and determined before deployment but should be iterated upon throughout the system’s life cycle.

Why it matters

Automated algorithmic decisions can reflect and amplify undesirable patterns in the data they are trained on. As well, issues with the system can arise that only become apparent after the system is deployed.

Therefore, despite our best efforts unexpected outcomes and impacts need to be considered. Accordingly, systems will require ongoing monitoring and mitigation planning to ensure that if the algorithmic system is making decisions that are not intended, a human can adapt, correct or improve the system.

4. Accountable and responsible

Organizations and individuals developing, deploying or operating AI systems should be held accountable for their ongoing proper functioning in line with the other principles. Human accountability and decision making over AI systems within an organization needs to be clearly identified, appropriately distributed and actively maintained throughout the system’s life cycle. An organizational culture around shared ethical responsibilities over the system must also be promoted.

Where AI is used to make or assist with decisions, a public and accessible process for redress should be designed, developed, and implemented with input from a multidisciplinary team and affected stakeholders. Algorithmic systems should also be regularly peer-reviewed or audited to ensure that unwanted biases have not inadvertently crept in over time.

Why it matters

Identifying and appropriately distributing accountability within an organization helps ensure continuous human oversight over the system is properly maintained. In addition to clear roles related to accountability, it is also important to promote an organizational culture around shared ethical responsibilities. This helps prevent gaps and avoids the situation where ethical considerations are always viewed as someone else’s responsibility.

While our existing legal framework includes numerous traditional processes of redress related to governmental decision making, AI systems can present unique challenges to those traditional processes with their complexity. Input from a multidisciplinary team and affected stakeholders will help identify those issues in advance and design appropriate mechanisms to mitigate them.

Regular peer review of AI systems is also important. Issues around bias may not be evident when AI systems are initially designed or developed, so it's important to consider this requirement throughout the lifecycle of the system.

5. Human centric

AI systems should be designed with a clearly articulated public benefit that considers those who interact with the system and those who are affected by it. These groups should be meaningfully engaged throughout the system’s life cycle, to inform development and enhance operations. An approach to problem solving that embraces human centered design is strongly encouraged.

Why it matters

Clearly articulating a public benefit is an important step that enables meaningful dialogue early with affected groups and allows for measurement of success later.

Placing the focus on those who interact with the system and those who are affected by it ensures that the outcomes do not cause adverse effects in the process of creating additional efficiencies.

Developing algorithmic systems that incorporate human centred design will ensure better societal and economic outcomes from the data enhanced technologies.

6. Sensible and appropriate

Every data enhanced system exists not only within its use case, but also within a particular sector of society and a broader context that can feel its impact. Data enhanced technologies should be designed with consideration of how they may apply to a particular sector along with awareness of the broader context. This context could include relevant social or discriminatory impacts.

Why it matters

Algorithmic systems and machine learning applications will differ by sector. As a result, while the above principles are a good starting point for developing ethical data enhanced technologies it is important that additional considerations be given to the specific sectors to which the algorithm is applied.

Encouraging sector specific guidance also helps promote a culture of shared ethical responsibilities and a dialogue around the important issues raised by data enhanced systems.