The Future Of AI
Artificial Intelligence (AI) has become a widespread force driving transformation across industries. However, with its rapid adoption and increasing complexity, the necessity for robust AI governance has grown. AI governance refers to the rules and guidelines that control the development, use, and implementation of AI technologies. It ensures responsible and ethical development of AI technologies that stay in compliance with relevant laws and regulations.
Artificial intelligence, automation and robotics are changing nearly every industry. In the past year, the world got a firsthand look at remarkable advances in AI technology from OpenAI, Microsoft and Google.
The Challenge of Bias
Human biases related to age, gender, nationality, and race can affect data collection and potentially lead to biased AI models.
For instance, a US Department of Commerce study found that facial recognition AI often misidentifies individuals of color. This could lead to wrongful arrests if used indiscriminately in law enforcement. Further complicating matters, ensuring fairness in an AI model is challenging. There are 21 parameters to define fairness; often, meeting one parameter may mean sacrificing another.
The Challenge of Regulation
As AI systems become more common, the need for regulations that consider ethical and societal values grows. However, the challenge lies in creating regulation that doesn’t hinder AI innovation. Despite regulatory bodies like the GDPR, CCPA, and PIPL, AI researchers have found that most EU websites fail to meet the GDPR’s legal requirements. Furthermore, reaching a consensus on a comprehensive definition of AI that covers both traditional AI systems and the latest AI applications presents a significant challenge for legislators.
The importance of AI
In the ever-changing world of technology, explainable AI is becoming more important due to these factors:
Growing complexity with adoption of AI systems
When people say robots are taking over the world, they’re not wrong. More sectors are adopting AI every day (think generative AI like ChatGPT and DALL-E), and the logistics are becoming more intricate as engineers continue to explore the capabilities. Autonomous vehicles are a notable example. They use AI systems to make critical decisions in real time. Without explainable AI in the mix, it would be difficult for engineers and developers to understand how these cars make decisions such as when to brake or swerve.
Trust and confidence
Would you trust a robot to look after your wallet? OK, it’s not quite like that (but not far off, either). With use of AI, trust of machines will always be an issue. If you don’t know how it’s joining the dots — the feature attribution you don’t know exactly how the AI algorithms are working, how can you trust the results?
In the retail world, AI-powered systems can help managers improve supply-chain efficiency by forecasting product demand to aid decisions about inventory management, for example. Highlighting key metrics, such as the average footfall in seasonal periods and popular trends, makes for confident decisions that can substantively lead to improved sales and customer satisfaction.