Safe and Ethical AI (SEA) Platform Network · Linking Artificial Intelligence Principles (LAIP)

6. AI systems should be developed in a diverse team that includes individuals capable of assessing the ethical and socioeconomic implications of the system;

Principle: Seven principles on the use of AI systems in government, Jun 28, 2018 (unconfirmed)

Published by The Treasury Board Secretariat of Canada (TBS)

Related Principles

2. Fairness and Equity

Deployers should have safeguards in place to ensure that algorithmic decisions do not further exacerbate or amplify existing discriminatory or unjust impacts across different demographics and the design, development, and deployment of AI systems should not result in unfair biasness or discrimination. An example of such safeguards would include human interventions and checks on the algorithms and its outputs. Deployers of AI systems should conduct regular testing of such systems to confirm if there is bias and where bias is confirmed, make the necessary adjustments to rectify imbalances to ensure equity. With the rapid developments in the AI space, AI systems are increasingly used to aid decision making. For example, AI systems are currently used to screen resumes in job application processes, predict the credit worthiness of consumers and provide agronomic advice to farmers. If not properly managed, an AI system’s outputs used to make decisions with significant impact on individuals could perpetuate existing discriminatory or unjust impacts to specific demographics. To mitigate discrimination, it is important that the design, development, and deployment of AI systems align with fairness and equity principles. In addition, the datasets used to train the AI systems should be diverse and representative. Appropriate measures should be taken to mitigate potential biases during data collection and pre processing, training, and inference. For example, thetraining and test dataset for an AI system used in the education sector should be adequately representative of the student population by including students of different genders and ethnicities.

Published by ASEAN in ASEAN Guide on AI Governance and Ethics, 2024

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.

Published by Government of Ontario, Canada in Principles for Ethical Use of AI [Beta], Sept 14, 2023

Principle 1 – Fairness

The fairness principle requires taking necessary actions to eliminate bias, discriminationor stigmatization of individuals, communities, or groups in the design, data, development, deployment and use of AI systems. Bias may occur due to data, representation or algorithms and could lead to discrimination against the historically disadvantaged groups. When designing, selecting, and developing AI systems, it is essential to ensure just, fair, non biased, non discriminatory and objective standards that are inclusive, diverse, and representative of all or targeted segments of society. The functionality of an AI system should not be limited to a specific group based on gender, race, religion, disability, age, or sexual orientation. In addition, the potential risks, overall benefits, and purpose of utilizing sensitive personal data should be well motivated and defined or articulated by the AI System Owner. To ensure consistent AI systems that are based on fairness and inclusiveness, AI systems should be trained on data that are cleansed from bias and is representative of affected minority groups. Al algorithms should be built and developed in a manner that makes their composition free from bias and correlation fallacy.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· Plan and Design:

1 At the initial stages of setting out the purpose of the AI system, the design team shallcollaborate to pinpoint the objectives and how to reach them in an efficient and optimizedmanner. Planning the design of the AI system is an essential stage to translate the system’sintended goals and outcomes. During this phase, it is important to implement a fairness awaredesign that takes appropriate precautions across the AI system algorithm, processes, andmechanisms to prevent biases from having a discriminatory effect or lead to skewed andunwanted results or outcomes. 2 Fairness aware design should start at the beginning of the AI System Lifecycle with a collaborative effort from technical and non technical members to identify potential harm andbenefits, affected individuals and vulnerable groups and evaluate how they are impacted bythe results and whether the impact is justifiable given the general purpose of the AI system. 3 A fairness assessment of the AI system is crucial, and the metrics should be selected at this stage of the AI System Lifecycle. The metrics should be chosen based on the algorithm type (rule based, classification, regression, etc.), the effect of the decision (punitive, selective, etc.), and the harm and benefit on correctly and incorrectly predicted samples. 4 Sensitive personal data attributes relating to persons or groups which are systematically or historically disadvantaged should be identified and defined at this stage. The allowed threshold which makes the assessment fair or unfair should be defined. The fairness assessment metrics to be applied to sensitive features should be measured during future steps.

Published by SDAIA in AI Ethics Principles, Sept 14, 2022

· We will make AI systems accountable

1. Accountability for the outcomes of an AI system lies not with the system itself but is apportioned between those who design, develop and deploy it 2. Developers should make efforts to mitigate the risks inherent in the systems they design 3. AI systems should have built in appeals procedures whereby users can challenge significant decisions 4. AI systems should be developed by diverse teams which include experts in the area in which the system will be deployed

Published by Smart Dubai in Dubai's AI Principles, Jan 08, 2019