3 ways to implement AI safety

AI, also known as artificial or augmented intelligence is focused on improving and scaling human expertise, by attempting to replicate human intelligence. AI systems have the ability to transform vast amounts of complex and ambiguous information into insights that have the potential to solve many business problems. It is an undeniably powerful tool. And like all powerful tools, AI safety must be given great priority.

From Siri to self-driving cars, Artificial Intelligence (AI) has come a long way. Today, AI encompasses anything from simple search algorithms to autonomous weapons. To gain the business benefits of an AI system, we will first need to implement AI safety practices. It means that businesses must implement best practices that can help in guiding safe and ethical management of AI systems. This includes alignment with business norms and objectives, algorithmic responsibility, compliance with existing business policies, assurance of the data integrity, and protection of privacy and personal information.

Let’s take a look at three useful practices to implement AI safety.

Develop Guidelines for AI Safety

Any tool, physical or digital, including AI, can be used unethically. Organizations should ensure that AI systems are utilized in a right way and for the right reasons. Companies can enforce some guidelines regarding the use of AI systems. These guidelines comprise of rules and regulations that can help engender trust among the developers, users and beneficiaries of artificial intelligence. These guidelines should govern the ethical management an AI system’s operations as well as the conduct of its employees. Organizations must also have a governance system that helps ensure compliance. These guidelines should also restrict the company from knowingly engaging in any business that can be detrimental to society. The business intent and the requirements of any AI system should be stated clearly before any data analyzing or business work begins. AI systems enhance a business’s ability to learn and discover and help in opening new revenue streams and gaining meaningful data insights. Thus, it is necessary that these AI systems should be used ethically.

Manage Integrity of Data

For assuring the integrity of AI systems as a whole, businesses must first carefully manage the integrity of the data and models underlying AI systems. Anomalies can be introduced through several factors ranging from incompleteness to malicious attacks. Organizations should implement techniques and processes to protect, detect, correct and mitigate risks due to anomalies. These techniques must be integrated end-to-end within the AI platform. Careful, risk-mitigating actions of an AI system can help in providing higher value than highly-tuned but brittle analytics, or confident but unreliable decisions. Managing data integrity and implementing risk mitigation techniques is essential not just in autonomous systems, but also in those systems which are based on human-machine collaboration.

Validate and Verify

Validating and verifying is a measurement of the reliability and predictability of AI systems. For achieving robustness and safety, all AI systems should be verified, validated and tested, both probabilistically and logically before they are deployed. Verification consists of techniques to confirm that a system can satisfactorily perform its tasks. As AI systems operate in partially unknown environments and act upon ambiguous information, it is necessary to implement new verification techniques. Validity is another technique critical to gauge predictability, and thus confirm that an AI system does not have unwanted behaviors. To define those unwanted behaviors, organizations must need to know what is good or bad in a particular situation.

AI systems facilitate accurate procedural logic, analytics, reasoning and sense-making with unique human qualities such as empathy, value judgment, and esthetics. If AI systems are safely implemented, businesses can gain ample of valuable insights and can make more informed business decisions.