The building blocks of responsible AI
In the recent years, we have witnessed a boom in AI, with organizations using AI and machine learning (ML) in attempts to solve all sorts of business problems. Whether its finding anomalies in financial systems to prevent fraud, performing predictive maintenance for machinery, or predict the likelihood of patient outcomes, AI is increasingly playing a role.
While AI makes decisions based on certain rules, these rules can be influenced by the biases of the humans who design them. This is an issue that organizations are increasingly needing to address.
Companies need a responsible AI framework that overcomes the obstacles around AI from both ethical and legal perspectives to ensure secure and fair AI systems. Some broad concepts companies should keep in mind while building an AI system are listed below.
Principles of responsible AI
The human element should always be at the heart of every AI solution, taking into account what decisions would most benefit the people it serves. For example, an AI for a ridesharing application could prioritize booking a driver for a person going to a hospital without charging a higher premium due to the urgency of situation.
AI solutions should not be biased to any category, gender, ethnicity, or other demographics. It is quite possible that biases in the data could create bias in the AI models, causing the AI to discriminate against certain groups of people. To ensure the integrity in the AI system development, there should be an independent audit committee to assess biases in the data and in the model.
At the end of the day, responsible AI is the result of a set of guiding principles, best practices, and governance.
Interpretability and explainabiity
AI models can frequently function like a black box, making it impossible to understand or fully trust the decisions they arrive at. This issue can be solved by ensuring interpretability and explainability are built into the system. AI systems should be able to explain why certain outcomes are expected given certain inputs. AI-based decisions should not leave the people interpreting confusion and doubts, and they should be explainable to all stakeholders.
Privacy and security
The backbone of all AI systems is data. Because of this, privacy and security are among the utmost concerns due to the sensitive nature of the data AIs often use to arrive at their decisions. In all circumstances, the privacy of individuals should not be compromised. Data should be securely stored and encrypted whether it is at rest or in transition.
Any system does not always behave the way it is supposed to. What if an AI system fails to solve the purpose it was built for, or is found to have built-in biases? Who will take the accountability of the failure? To handle this situation, an organization’s operating model should identify the various roles and stakeholders who are accountable for, provide oversight of, and conduct due diligence into AI projects throughout their implementation and use.
Enable responsible AI for your organization
At the end of the day, responsible AI is the result of a set of guiding principles, best practices, and governance. While no fixed framework can account for the different guiding principles each situation calls for, organizations can make sure they have certain best practices in place to combat bias and ensure an ethical, effective AI implementation.
Key capabilities for enabling responsible AI include:
Understand the complete AI lifecycle:
Assessments of an AI system should be made at every stage of AI lifecycle. This can assist in locating larger issues that can be overlooked during point-in-time checks. Businesses must create the right lifecycle activities that bring together planning, design, creating, and testing to continually measure results, lower risks, and react to stakeholder feedback.
We have heard it multiple times that when you put garbage data into an AI model, you’ll receive garbage out. As data is most important building block of AI system, data quality is extremely important for intelligent system building. Consider an end-to-end data quality capability that delivers clean, trusted data.
Good data governance helps you improve data processing time, remove bias, align data with the intended use case, and deliver high-quality, precise AI.
Data and AI governance
Data and AI governance play a key role in building scalable, reliable, and secure AI solutions. Good data governance helps you improve data processing time, remove bias, align data with the intended use case, and deliver high-quality, precise AI. Solid AI governance helps you deploy scalable, secured solutions with continuous monitoring.
Data operation and model operation frameworks enables organizations to build agile AI systems that seamlessly operationalize, manage, and monitor AI solutions.
If an AI system is not responsible, it will not generate the results businesses are looking for. Organizations should not only focus on building AI systems, but also building frameworks that ensure responsible, biasfree AI models that benefit society.
Head of AI/ ML