Global AI and Data Science

As artificial intelligence (AI) becomes increasingly ubiquitous in our daily lives, we must develop trustworthy AI systems designed to protect all individuals' rights and interests. Here are six best practices for creating trustworthy AI and mitigating risks:

Beyond Binary – Fairness and Non-discrimination

AI bias can lead to discrimination against individuals or certain groups, so it's important to ensure that our AI systems are designed to be fair and equitable for everyone. An organization can measure bias by evaluating performance across different groups based on characteristics such as gender, ethnicity, and age. To reduce bias, data debiasing, model amendment, and output amendment can be used depending on the source of bias, moving beyond binary thinking.

Human Agency and Oversight

Human oversight and agency are crucial for ensuring that AI systems are developed and used in ways that respect fundamental rights. Human oversight can help to ensure that AI systems are not used to undermine privacy or other fundamental rights.

Technical Robustness and Safety

It is essential to make AI systems resilient to attacks, secure, and have a contingency plan in case of failure. Technical robustness depends on accuracy, reliability, and reproducibility. Robustness can be measured by assessing performance on unseen data and testing the system's ability to deal with targeted or adversarial attacks. Strategies such as improving model generalization, retraining on new data, using adversarial training, and continuous monitoring can be implemented to mitigate risks.

Privacy and Data Governance

The importance of privacy and data governance throughout the AI lifecycle cannot be underestimated. Quality and integrity of data are also important, and individuals should have access to their own data. Privacy risk can be assessed by evaluating data type, the amount stored, and whether data minimization techniques were applied and can be resolved by reducing training data, anonymizing/pseudonymizing data, or implementing decentralized/federated models.

Accountability

To ensure trustworthy AI, accountability is crucial. Algorithmic auditing can help ensure accountability and minimize negative consequences. It is also important to consider trade-offs and redress in the development and deployment of these systems.

Transparency

Transparency is key to constructing trustworthy AI. AI systems should be traceable and explainable. Communication is also critical, and individuals should be informed about the use and limitations of AI systems. To ensure transparency, it is vital to examine the documentation and communication processes concerning models and data and how easy it is to interpret the model's decisions.

The importance of trustworthy AI cannot be overstated, as it safeguards the rights and interests of individuals. Adhering to these six best practices can help organizations ensure that their AI systems are developed and utilized in a manner that respects fundamental rights and promotes the greater good.

Reference: https://www.holisticai.com/papers/intersectional-fairness-a-fractal-approach