^{Manage Your Biggest AI Risk}

_Introduction

Organizations are generating significant value from AI. All modern industries are leveraging AI as the key ingredient for the advanced tech space. From wearables to robotics, AI is almost everywhere and in every sector. Most companies extend their hands with AI vendors to adopt AI into their workflow. But at the same time, organizations have discovered risks and pitfalls that AI could expose to the ever-changing landscape of technology.

This article will address some of the most significant AI risks and manage them.

Identifying the risks of AI -

Every organization requires a tech team that will precisely analyze and delineate all the adverse events an AI project deployment can cause. The team should also find solutions to how to mitigate the risks following the specific industry standards.

Here are the six pillars that an organization can focus on to identify AI risks systematically.

 Privacy

Many business leaders and executives heed attention to user privacy amidst the unprecedented possibilities of AI. Even the users are aware of privacy these days. Even though data is the vital ingredient of AI systems, organizations leveraging these data should follow normative standards to deal with customer data. Working with these data abruptly against the norms can cause harm to the customer and damage the organization's reputation.

Security

With the increase in the complexity of technology, new vulnerabilities pop up. AI-based models such as data poisoning and model extraction can pose new threats and challenges to the business and the general security mechanisms.

 Fair play

It is easy to inject unlawful code into AI and encode it towards a biased system. It is possible by feeding a specific set of data into the model. Such a bias system could potentially harm a particular group or class. Thus, organizations should harness the culture of non-bias and fair use of AI.

 Explainability in AI workflow

It is essential to have a clear idea of how AI systems work. A explainable model of how the AI system developed and what type of datasets it is leveraging is essential to reduce AI-driven risks. Explainability also helps other witnesses the internal scenario helps in understanding whether the compliances are working with the legal mandates.

 Safety in Performance

A poorly tested AI system can suffer from performance issues. An abruptly-functional AI will not only render poor performance but can also breach contractual agreements. In an extreme situation, it can pose threats to personal safety also.

Third-party risks

Most companies extend their hands with AI vendors and third-party organizations to develop AI systems. The third party must also know and comprehend the risk-mitigation and governance standards against these AI systems.

Conclusion

In addition to all these vectors of AI-based risks, the risk assessment team should also consult publicly available databases of previous AI incidents.

To read more articles like this, visit the Cogent Infotech website.