GenAI: The Future of Decision Making

How the decisions made using GenAI today will shape our future, and how trustworthy are those decisions?

This question was asked by a client as we were discussing Generative AI and its adoption by their organization. I am sure it is on the minds of many others as the use of Generative AI continues to increase and enter more and more into our daily lives and our decision-making.

As I think about it, a series of questions come to mind as we dive deep into using Generative AI. How do we see that the decisions made today based upon the knowledge of today will affect the future? Where does the accountability lie, and who is ultimately responsible for the outcome of those decisions? How can we trust the data that we have available and make decisions on?

In today’s world, our reliance on artificial intelligence (AI) and machine learning (ML) to make data-driven decisions continues to increase. No doubt, GenAI has a huge potential to revolutionize the way businesses operate, but it also raises important questions about trust and accountability. As our trust in GenAI is increasing more and more for some of the most critical decision-making responsibilities, it's essential to also consider any long-term implications of those decisions in the future.

So, what are the risks?? We know AI systems are as good as the data they're trained on. If the data is biased, incomplete, or inaccurate, the decisions made by AI will be biased, inaccurate, or incomplete. Additionally, if the processing of these data and implementation of these decisions are automated, then the additional concerns will be for accountability, transparency, and explainability.

Generative AI is the future, and it will continue to get more and more involved in our daily lives and decision-making

It is not my intention to raise concerns about Generative AI but to highlight the importance of transparency, drift, bias, and accuracy of the data and its use. Generative AI is the future, and it will continue to get more and more involved in our daily lives and decision-making.

As GenAI gets more involved, it’s essential for organizations to ensure they have the right governance model implemented so their decision-making is transparent and explainable. This means that organizations must be able to understand how AI systems arrive at decisions and identify any biases or errors. They should also be able to understand the quality of the data being used for making these decisions.

Transparency, the right governance model, and a clear understanding of the data quality are essential to building trust.

Transparency and explainability are critical for building trust in AI systems and ensuring that decisions are fair, unbiased, and reliable.

The Role of Human Oversight

AI systems can process vast amounts of data quickly and efficiently. However, human oversight will remain essential to address any nuances and to ensure that AI-driven decisions align with organizational goals and values. By combining the AI’s strengths with human judgment and oversight, organizations can create a more robust and reliable decision-making process.

Building Trust in Data

Given the potential risks associated with relying solely on AI for decision-making, it is crucial to build trust in data. A few things to consider in order to build trust in data:

1. Data Governance: Establishing the right data governance policies and procedures ensures that data is accurate, complete, and reliable. Organizations should implement data quality controls, data security measures, and ethical guidelines for data use as per their policies.
2. Data Transparency: Organizations should be transparent about their data sources, data processing methods, and data-driven decision-making processes.
3. Data Quality: Ensuring the quality of data is important, and organizations should validate data sources, monitor data quality, and correct errors or inaccuracies in data.
4. Ethical Use of Data: Ethical use of the data, respecting privacy and security, and avoiding biases and discrimination are among the most important aspects that organizations must adopt to build trust in data.

Conclusion

The decisions we make today about how to use AI in decision-making will have a profound impact on the future of organizations. It's essential to prioritize transparency and explainability, improve the quality of the data, remove bias, address drift, and provide human oversight for some of the critical decisions.

IBM offers, as part of our watsonx platform, IBM? watsonx.governance??that is built to direct, manage, and monitor the artificial intelligence (AI) activities of your organization by using IBM?watsonx?, one integrated platform, which can be deployed on cloud or on-premises, an organization can:

• Govern generative AI (GenAI) and machine learning (ML) models from any vendor, including IBM ??watsonx.ai?, AWS Sagemaker and Bedrock, Google Vertex, and Microsoft Azure.
• Evaluate and monitor for model health, accuracy, drift, bias, and GenAI quality.
• Access powerful governance, risk, and compliance capabilities featuring workflows with approvals, customizable dashboards, risk scorecards, and reports.
• Use factsheet capabilities to collect and document model metadata automatically across the AI model lifecycle.

Finally, would love to hear what other people’s experiences are and what the clients are saying on this topic.