Ten Guidelines for Product Leaders to Implement Azure AI Responsibly 

Guidelines for Product Leaders to Implement Azure AI Responsibly 

Artificial Intelligence came up as a Sci-Fi dream, but it remained to stay as a reality. AI (Artificial Intelligence) is now helping to solve challenges not only in the field of technology, but also in medicine, academia, and the scientific community. Azure AI allows organizations to build and train machine learning models, deploy AI, and build AI solutions to any business process. Developing Azure AI responsibly according to the next ten guidelines must be a top priority of any organization aiming to implement these tools.

Knowing this, when designing AI, we must be aware of these recommendations to aim for harmonious results within performance, goals, and organizational values. According to the World Economic Forum, nearly 50% of organizations report having a formalized framework to encourage considerations of ethics, bias, and trust. Here we present to you the following ten guidelines to implement Azure AI Responsibly. They are created and categorized in three key phases

1. Assess and prepare for Azure AI 

Before developing, encourage your leaders to reach out and brainstorm potential approaches for your product. Asking yourselves, what are the primary uses and benefits of my product? Will there be a positive impact with it? How is this product enhancing our organizational values? 

While doing this, be sure to assemble a diverse (age, gender, ethnicity) and multidisciplinary team to make sure you’re including all points of view. Be aware of the outside world and the expertise you might be lacking, so you can include it. Your team should include a data scientist applying fair tools to Machine Learning (ML) models, a lawyer with expertise on the regulatory environment, two loan officers that will be in charge of the application process, a product leader that will be leading with PII (Personal Identifiable Information) and cybersecurity, a designer, and a user researcher. 

Ask yourself, could my product have any modes of failure in the social or environmental spheres? If so, how could we design strategies? How could we reduce the risk of negative impacts on individuals affected by my AI product? 

 

  • Assess the merit of developing the product, according to organization values and objectives
  • Assemble team reflecting diverse perspectives and with clearly defined roles and responsibilities 
  • Assess potential product impact by including input from domain experts and potentially impacted groups 

 

2. Design, build, and document

During the development of your AI product, please make sure to analyze fairness, shipping, metrics, and test criteria so you can predict the groups impacted, even in post deployment. 

If there is a negative impact (system failure, attack, unplanned use or even environmental) design an approach and feasible solution to reduce it. We recommend a legitimate and transparent data collection focused upon your solutions.

Another point to take into consideration is to enable features in your AI product that will empower humans by augmenting their decision-making skills and oversight by monitoring, interpreting, and customization. Be mindful towards people with disabilities. 

In the privacy area, it is important that you prioritize data privacy, encryption, anonymization, and methods that will protect locations and behavior of your users.

 

  • Evaluate data and system outcomes to minimize the risk of fairness harms
  • Design AI products to mitigate the potential negative impact on society and the environment 
  • Incorporate features to enable human control
  • Take measures to safeguard data and AI products
  • Document throughout the development lifecycle to enable transparency

 

3. Validate and support

Settle clearly the conditions where your product will have effective operations, sort out the evaluations that your users will have to perform and monitor continuously to ensure your AI product provides a safe and reliable use. Your model should propel engagement within suppliers, end users, and employees while enhancing customer relations.

 

  • Validate product performance and test for unplanned failures as well as foreseeable misuse unique to AI products
  • Communicate design choices, performance, limitations, and safety risks to end user

 

All of these recommendations are designed with the main objective of anticipating and mitigating any AI risk. We understand that the world we’re living in is ever changing. However, circumstances might be predicted, and positive outcomes can be built with values, responsibility, and integrity.

Want to integrate and develop Azure AI into your organization? Schedule a meeting with one of our experts today.