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The developer’s guide to best practices for AI development 

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The developer’s guide to best practices for AI development 

Developing a robust and future-ready AI system comes with many challenges and responsibilities. Understanding the best practices for AI development can help developers create effective and compliant AI systems for their business.

The artificial intelligence (AI) industry is predicted to grow by 33.2% between 2020 and 2027. The rate at which we apply AI technology in everyday business scenarios is growing exponentially. From a developer’s perspective, it means they need to stay updated on the latest AI technologies to create the most efficient IT ecosystem for businesses.

Among all the technological advancements, the basic principles of ethical AI development remain the same. Developers must adhere to these principles to make a robust AI system that checks all the boxes of transparency, fairness, and efficiency.

To help developers, here are some of the best practices to follow for each component of your AI solution, across the development life cycle:

1. Ensuring transparency in AI development 

Transparency in artificial intelligence refers to the user’s ability to monitor the internal workings of an AI system to find possible flaws and make the AI more reliable and trustworthy.

Here are a few tips for you as a developer for making your AI more transparent and manageable:

  • Following strict AI governance protocols is the first step in ensuring high standards in AI application development.  
  • Incorporating LIME (Local Interpretable Model-agnostic Explanation) helps in providing easy-to-understand explanations through advanced algorithms.  
  • Developers can design the AI system using the “transparency by design” philosophy that promotes the importance of transparency while overcoming AI-related challenges.  
  • Another great option to boost your AI transparency is using the decision tree model. Here, AI developers can observe a chain of decisions along with its branches in the AI system to make AI more governable at every step of application development. 

2. Improve AI accountability

The decisions made by an AI system are prone to errors due to multiple factors like improper data input, errors in programming, human interventions, and errors during scale up/down. Making AI accountable means tracing the AI decision to an individual or a step in AI decision-making.

Here are some best practices to make AI decisions more traceable: 

  • Create a comprehensive periodical AI auditing mechanism to observe and mitigate unwanted results from the system. These auditing practices should focus on avoiding unfair biases in the AI algorithms. 
  • If an error occurs that causes grievances to a person, companies should have their own redressal system to remedy and compensate for the wrongdoing. An efficient and humane redressal system is crucial to building strong trust among people associated with your business. 
  • Most importantly, businesses should identify activities with critical decision-making that cannot be delegated to an AI.

3. Overcoming bias in AI

An ideal AI system must provide fair decisions for every individual within and outside the organization. From an ethical perspective, it must be designed to mitigate bias towards gender, race, culture, and other social factors by monitoring historical data inputs and the lack of diverse data in the first place. 

With that said, here are some best practices to make AI fairer: 

  • Firstly, you need to identify people that are most vulnerable to biased AI results. This includes people using the AI system and those who are affected by the system, both directly and indirectly.  
  • A context-centric AI algorithm helps you mitigate the biases in AI results and helps developers identify possible discrimination due to errors in data entered previously. 
  • The best way to mitigate biases in general is to understand that we can classify an individual into multiple overlapping categories. Helping your AI system acknowledge these nuances in terms of age, gender, race, etc. helps in setting a clear context for decision making.
4. Security

Last but certainly not least, security is the biggest concern for designing a robust AI system. It is more important now than ever before as over 50% of organizations in India have faced some sort of cyber attacks in 2020 alone.

Here are some best practices for building trust among your customers with a secured AI system:

  • Inspect your IT vendor thoroughly. Make sure that your AI partner follows the latest security protocols at the time of signing the contract.  
  • Make sure that your vendor adheres to these security policies and keeps its promise of protecting sensitive business and individual data. A regular follow-up is strongly recommended. 
  • Partnering up with international standardization authorities like International Organization for Standardization (ISO) helps developers to design a safe and efficient AI system. 
  • Adhering to the latest standards of ISO 27003 at all times is crucial for designing an enterprise-level AI system with utmost security.

Final Thoughts

Developing a robust and future-ready AI system comes with many challenges and responsibilities. It needs keen attention to the details and knowledge of the latest AI trends to get it done correctly. We hope these practices help you in the AI application development process to give you a much-needed competitive edge.

VBeyond Digital helps you achieve more with your IT infrastructure with state-of-the-art, bespoke application development, implementation, and maintenance services.

Reach out to us today.

This blog was created by referring to a handbook on developing Artificial Intelligence solutions published by GIZ India, and co-authored by experts from KOAN Advisory and Digital India Foundation. To learn more about the guidelines and frameworks for ethical AI development, you can read the handbook here.