4 Factors That Define Responsible AI

Learn how to ensure your AI models are providing trustworthy, accurate outcomes for your customers and partners

Ram Bala
4 Factors That Define Responsible AI

For years now, business leaders have been dreaming about how artificial intelligence would one day transform their operations to be smarter and more efficient. Now that AI adoption has finally gone mainstream, those leaders must also be aware of the wider implications of that fundamental shift.

Specifically, businesses are now sharing the outcomes of their AI models with business partners and customers. As a result, they must extend the same care and consideration to those outcomes as they would any other product or service they offer. They have a responsibility to do AI the right way—on behalf of anyone who may be impacted by their AI models, whether directly or indirectly.

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At Equinix, we believe a responsible approach to AI must account for each of these four factors:

  1. Security
  2. Data governance
  3. Business value
  4. Inclusivity

1. Ensuring the security and integrity of AI data sets and models

AI is the ultimate team sport. Whether it’s taking advantage of open-source packages for model development or feeding cross-functional and external data sets into those models, no business is ever an island when it comes to AI.

In order to ensure the integrity of their AI outcomes, businesses must verify that none of these inputs have been corrupted, and put rigorous checks in place to ensure data security and integrity. So-called data poisoning can happen either intentionally or unintentionally. That is, it could be the result of a malicious third party deliberately tampering with important business information, or just a careless person hitting the wrong key during manual data entry.

The implications are clear: AI models that use the wrong data will inevitably come to the wrong conclusions. When these conclusions are passed on to end users, they could use them to make the wrong decisions, potentially costing them millions of dollars and doing irreparable damage to their business reputation.

2. Being a good steward for partner data

In every data-sharing partnership, there are two sides: You depend on your partners, both internal and external, to ensure that all relevant data inputs are gathered to build effective AI models, but they also depend on you to ensure their inputs are properly protected. Protecting third-party data from unauthorized access is not only the responsible thing to do, but it may be explicitly required by the terms of your contract. Furthermore, data privacy regulations in many jurisdictions highlight the need for good data governance. Failing to comply with these regulations could lead to stiff financial penalties and other legal trouble. Developing and maintaining data catalogues of attributes and features that feed into AI models can improve the transparency of data sources for both internal and external data sets, thereby easing the path to compliance and governance.

AI models that use the wrong data will inevitably come to the wrong conclusions."

Effective data governance starts with carefully considered access controls, based on the principle of least privilege: Internal stakeholders should have access to data they need to fulfil their specific job roles, and nothing else. In addition to protecting data integrity, data governance is also essential to providing the context that goes along with your AI outcomes. Being able to provide good context for your AI models—what data and assumptions went into the models, and how business users should interpret the outcomes—is every bit as important as the quality of the models themselves.

3. Building AI models to align with business outcomes

Business value is not something that people typically associate with responsible AI; however, a closer look reveals why they should. If your partners and customers are using your AI models in a way that directly impacts their bottom line, you have a responsibility to align those models to their objectives and key results, and provide the context that helps enterprise leaders pursue those results.

In addition, you have a responsibility to your own internal stakeholders to use the organization’s resources in a manner that maximizes business value. If you’re budgeting resources to build AI models, it’s essential that those models support initiatives that are important to the business. Identifying and periodically revisiting Key Performance Indicators (KPIs) and model success criteria for potential adoption of AI models help amplify business value.

4. Accounting for inclusivity and fairness across the AI lifecycle

Since AI models are increasingly used to make decisions that impact the lives of real people, it’s your responsibility to ensure those models aren’t biased against certain data groups or subsets. AI models must be fair in order to be accurate, and fairness starts with the data you feed into your models. If different subsets are overrepresented or underrepresented in your data sets, then the outcomes of your AI models will be tainted by that fact.

Being able to provide good context for your AI models...is every bit as important as the quality of the models themselves."

Any team that adopts AI models is also adopting AI risk. Before you make any of your model outcomes available for consumption—whether by customers, partners or internal teams—you must pass the input data and those model outcomes through rigorous testing to ensure you feel confident sharing them. In addition, you must do everything you can to address risk across every step of the AI lifecycle. Whether you’re building new AI models, gathering data to feed into the models or performing ongoing operations and maintenance over time, failing to properly ensure fairness and transparency at any one stage will create risk that reverberates throughout the entire lifecycle.

Put the Equinix approach to responsible AI to work for you

When it comes to our own AI models, we at Equinix believe that our ultimate responsibility is to our customers—both current and future. We’re dedicated to using these models in a way that maximizes business value for customers, whether it’s:

  • Performing predictive capacity analytics to determine where to expand our Equinix IBX® data center footprint to meet growing demand
  • Recommending the right digital services to meet specific customer needs
  • Helping customers identify the right ecosystem partners to connect with

As the world’s digital infrastructure company™, we understand better than anyone that AI models have inherently different infrastructure needs than conventional IT workloads do. Our global data center footprint and dense partner ecosystem of cloud and network service providers make us well suited to help our customers address the costs and complexity that go along with designing digital infrastructure for AI workloads.

To learn more about how you can get started putting our AI expertise and resources to work for you, read the ESG white paper “Enable AI at Scale with NVIDIA and Equinix.”

 

Any team that adopts AI models is also adopting AI risk."
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