How to build transparent AI to enable more equitable products by Dipanwita Das

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Unlocking Equitable Products with Transparent AI: An Insight from CEO and Co-founder of Soro, Deana Das

In today's world, AI has progressively intertwined with several aspects of life, raising critical questions on transparency and equity. Deana Das, Soro's CEO and Co-founder, reveals why and how taking a transparent approach to AI can enable more equitable products in this enlightening discussion.

Importance of Transparency and Equity in AI

First and foremost, transparent AI is crucial for equitable outcomes. Transparency permits introspection of the decision-making process of AI models, which helps in improving their performance. On the other hand, explainability, often also referred to casually as XAI (Explainable Artificial Intelligence), is about making the decision-making outcome understandable to humans.

Risk Levels in the Application of AI in Business

The application of AI has different levels of risk involved, and it’s important to design systems that take risk into account. More transparency is needed where decisions made by the AI have considerable impact on human lives.

The financial industry, health sector, and credit scoring are examples of high-risk areas hence emphasize transparency and have a human in the loop.

Keeping the Human Element

Soro retains the Human in the Loop (HITL) to augment the accuracy and reliability of its models. It is crucial to note that this doesn’t slow decision-making but rather boosts efficiency.

HITL involves various levels of human involvement based on the risk involved. In high-risk situations like medical diagnosis, the final decision must be from a trained physician, taking into account the situation's complexity and ethics.

Fixing the Data: The Foundation of Unbiased AI Models

AI models perform as well as the quality of data on which they are trained. Domain-specific languages and an understanding of the inadequacy of data for key populations and unmet needs is essential.

Avoiding Bias and Increasing Transparency

  • Breaking down complex decisions into simple indicators: Deconstructing intricate decisions into less complicated indicators facilitates understanding of the final outcome.
  • Increasing redundancy in your AI workflow: Multiple AI models for the same decision ensures a multifaceted view, allowing adjustments as required.
  • Presenting unequivocal and clear information to the end user: Providing an understandable representation of the decision made by the model prevents bias.
  • Conclusion

    Ensuring transparency and equity in AI is not just a theoretical discussion, but rather a movement towards a more fair and just technological world. An AI that respects diversity and differences, coupled with retaining the human element and continuous data improvement, paves the way for more equitable outcomes.

    Deana Das emphasizes that context is everything and explains that it's crucial to build effective AI that is both explainable and transparent. As such, transparency and explainability in AI are indispensable aspects that can enable a more equitable world.


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