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 —
Value statements often lack proper definitions of concepts like bias and fairness in the context of AI. While it’s possible to design AI to be fair and in line with an organisation’s corporate code of ethics, leaders should lead their organizations towards establishing metrics that align their AI initiatives with company’s values and goals. CEOs should comprehensively lay down company’s goals and values in the context of AI and encourage collaboration across the organization in defining AI fairness . Some examples of metrics include:
Building Responsible AI practices in the organization requires the leadership to build proper communication channels, cultivate a culture of responsibility, and build internal governance processes that match with the needed regulations and industry’s best practices.
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:
AI development isn’t devoid of trade-offs, in fact, while developing AI models there is often a perceived trade-off between the accuracy of an algorithm and the transparency of its decision making i.e, how explainable its predictions are for stakeholders. A high accuracy AI model can lead to the creation of “black box” algorithms, which makes it difficult to rationalize the decision making process of the AI system.
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.
Resilience, security and safety are essential elements of AI for it to be effective and reliable.
AI needs to be closely monitored with human supervision and its performance should be audited against key metrics like accountability, bias, and cybersecurity. A diligent evaluation is needed as biases can be subtle and hard to discern and a feedback loop needs to be devised to effectively govern AI and fine tune biases.
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.
Explainable AI (XAI) is defined as systems with the ability to explain their rationale for decisions, characterize the strengths and weaknesses of their decision-making process, and convey an understanding of how they will behave in the future. While it’s desirable to have complex models that perform exceedingly well, it would be erroneous to assume that the benefits derived from the model outweigh its lack of 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.
Machine Learning models essentially need large sets of data to train and work accurately. A caveat to this process is that a lot of data can be sensitive. It is essential to address the potential privacy implications in using such data. This demands enterprises to adhere to legal and regulatory requirements, be sensitive to social norms and individual expectations, and finally have adequate transparency and control of their data.
A Responsible AI framework enables organizations to build trust with both employees and customers. In doing so, employees will rest their faith in the insights delivered by AI, willingly use it in their operations and ideate new ways to leverage AI in creating greater value.
Building trust with customers, opens the floodgates to use consumer data that can be used to continually improve AI and consumers will be more willing to use your AI-infused products because of the trust in the product and the organization. This also improves brand reputation, allows organizations to innovate, compete and most importantly, enables society to benefit from the power of AI than be paranoid about the technology.
If you’d like to learn more about this topic, please feel free to get in touch with one of our enterprise AI consultants for a personalized consultation!