AI ethics: 5 key pillars

As artificial intelligence (AI) gains momentum in mainstream business operations, CIOs must consider these ethical guideposts in their strategy
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Artificial intelligence and machine learning (AI/ML), often imagined as self-driving cars or human-like robots, are flourishing at the enterprise level as practical business use cases increase. Many companies rely on AI/ML to expedite internal processes, automate mundane tasks, and reduce human error.

But some businesses fail to consider that AI ethics are essential to ensure that the technology is used appropriately and securely and that it does not pose risks to businesses.

The growing importance of AI in nearly all enterprises brings into question how CIOs can ensure that the processes align with ethical and responsible AI. Because AI will likely continue to pave the way for additional technological advancements and is crucial to ensuring efficient and effective internal processes, CIOs and business leaders must pay attention to the following key pillars of ethical AI.

1. Accountability

The first pillar of ethical AI is accountability. Relying on AI can speed up internal processes and ensure faster workflows, but only if it is accountable and dependable. The AI/ML must be trustworthy, based on the processes it’s designed to complete, to be valid.

[ Also read AI ethics: 4 things CIOs need to know. ]

If AI is not accountable for completing tasks, then its use cases essentially go out the window. CIOs should continuously check on AI to evaluate success rates and ensure that business processes operate correctly.

2. Reliability

In a similar vein, AI must be reliable. Data sources are constantly changing, and as new sources of data are added, outputs from AI/ML must also be monitored and validated. As AI/ML is increasingly deployed, the reliability of algorithms becomes even more critical considering the vast array of processes that leverage AI/ML across the enterprise.

CIOs and businesses rely on the standardization of processes, data collection, and organization to ensure that the technology managing this data, including AI, can run smoothly, without producing errors.

3. Explainability

Explainability ensures that AI and ML models are understood and can be explained across departments and organizations. The benefits of AI at an enterprise level become irrelevant if the technology cannot be translated, which could result in confusion and siloed processes.

[ Want best practices for AI workloads? Get the Ebook: Top considerations for building a production-ready AI/ML environment. ]

Consider industries such as banking or healthcare. The predictions from AI/ML models used by these industries must be explainable to ensure that there is no inherent bias and that the technology is creating actionable results.

4. Security

Tech security is a growing concern as ransomware attacks threaten organizations and protected data. Protecting AI models against these attacks is essential, and CIOs need to understand the potential risks and how they may impact the technology in use.

If AI does not guarantee privacy, businesses will struggle to keep customers' trust and protect internal property.

Many businesses and customers are evaluating AI with a critical eye on security. Businesses and CIOs must ensure that the AI they rely on is dependable and secure to minimize risks.

5. Privacy

Protecting customer data, especially when AI is used in data-sensitive industries or business processes such as healthcare and banking sectors, must be top of mind for CIOs. CIOs must ensure that the AI technology has measures to protect sensitive data and provide business and customer privacy.

As individuals and businesses increasingly rely on the cloud to conduct business, transactions, store private information, and more, they are paying more attention to the privacy granted to them through their technology. If AI does not guarantee privacy, businesses will struggle to keep customers’ trust and protect internal property.

[ Related read 5 ways to embed privacy compliance into your culture ]

The use of AI will eventually become table stakes for businesses. While it can solve business challenges by expediting internal and external processes and eliminating human error, it is only as good as the CIOs who manage the AI processes, as they are responsible for ensuring that this next-level technology meets ethical standards and solves business challenges.

The key to relying on AI and enforcing AI ethics is continuous improvement and ongoing checks and balances to ensure that the AI is working properly and is correctly maintained. AI is a constantly evolving technology, and its use cases will continue to develop, so CIOs should build AI’s foundation with these five ethical pillars in mind.

[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]

Vikas is part of the executive leadership team at Persistent reporting to the CEO. He has partnered with CXOs across the globe to deliver several transformational programs leveraging data, analytics, and digital technologies. At Persistent, his role is to bring digital capabilities together to the benefit of our clients.