Responsible AI Practices Your Organization Should Follow For Better Trust

AI’s transformative potential has been the prime mover for its widespread adoption among organizations across the globe and continues to be the utmost priority for business leaders. PwC’s research estimates that AI could contribute $15.7 trillion to the global economy by 2030, as a result of productivity gains and increased consumer demand driven by AI-enhanced products and services.

While artificial intelligence (AI) is quickly gaining ground as a powerful tool to reduce costs, automate workflows and improve revenues, deploying AI requires meticulous management to prevent unintentional ramifications. Beyond the compliance to the laws, CEOs bear a great onus to ensure a responsible and ethical use of AI systems. With the advent of powerful AI, there has been a great deal of concern and skepticism regarding how AI systems can be aligned with human ethics and integrated with softwares, when moral codes vary with culture.

Creating responsible AI is imperative to organizations and instilling responsibility in a technology requires following criteria to be fulfilled —

  • It should comply with all the regulations and operate on ethical grounds
  • AI needs to be reinforced by end-to-end governance
  • It should be supported by performance pillars that address subjects like bias and fairness, interpretability and explainability, and robustness and security.

Key Dimensions For Responsible Ai

1. Guidance On Definitions And Metrics To Evaluate AI For Bias And Fairness

  • Disparate Impact: The ratio in the probability of favorable outcomes between the unprivileged and privileged groups.
  • Equal Opportunity Difference: The ratio of true positive rates between the unprivileged and privileged groups
  • Statistical Parity Difference: The difference of the rate of favourable outcomes received by unprivileged group and the privileged group
  • Average Odds Difference: The average difference of false positives and true positives between unprivileged group and the privileged group
  • Theil Index: The inequality of benefit allocation for individuals

2. Governance

End-to-end enterprise governance is extremely critical for Responsible AI. Organizations should be able to answer the following questions w.r.t AI initiatives:

  1. Who takes accountability and responsibility
  2. How can we align AI with our business strategy
  3. Which processes can be optimized and improved
  4. What are the essential controls to monitor performance and identify problems

3. Hierarchy Of Company Values

Likewise, trade-off exists while training AI. As AI models get more accurate with more data, gathering a large volume of data itself can increase privacy concerns. Formulating thorough guidelines and hierarchy of values is vital to shape responsible AI practices during model-development.

4. Security And Reliance

  • Resilience: Next-generation AI systems are going to be increasingly “self-aware,” with a capability to evaluate unethical decisions and correct faults.
  • Security: AI systems and development processes should be protected against potential fatal incidents like AI data theft, breaches in security that lead to systems being compromised or “hijacked”.
  • Safety: Ensure AI systems are safe to use for the people whose are either directly impacted by them or will be potentially affected by AI-enabled decisions. Safe AI is critical in areas related to healthcare, connected workforce, manufacturing applications etc.

5. Monitoring AI

A continuous monitoring of AI will ensure that the models will recreate an accurate real-world performance and take user feedback into account. While issues are bound to occur, it is necessary to adopt a strategy that comprises short-term simple fixes and longer-term learned solutions to address the issues. Prior to deploying an AI model, it is essential to analyze the differences and understand how the update will affect the overall system quality and user experience.

6. Explainability

AI’s Explainability is a major factor to ensure compliance with regulations, manage public expectations, establish trust and accelerate adoption. And it offers domain experts, frontline workers, and data scientists a means to eliminate potential biases well before models are deployed. To ensure that model outputs are precisely explainable, data-science teams should clearly establish the types of models that are used.

7. Privacy

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