By Aparna Dhinakaran

Today, nearly any company – even enterprises with large teams of sophisticated data scientists and engineers – can be brought to heel by machine learning (ML) models that fail spectacularly in the real world. Whether it’s demand forecasting models upended by Covid economics or models that power HR software inadvertently discriminating against potential job seekers, problems with models are as common as they are dangerous when not monitored and caught early.

In recognition of this fact, an increasing number of companies – from Alphabet and Amazon to Bank of AmericaIntelMeta and Microsoft – quietly disclose their use of AI (or its potential regulation) as a risk factor in their most recent annual financial reports.

Despite the risks, most enterprises are confidently (and rightly) plowing ahead. Today, ML-powered systems are relied on by nearly every industry to increase profitabilityproductivity and even save lives. In all, IDC forecasts that global enterprise spending on AI will top US$204 billion by 2025.

So how can enterprises balance the tremendous power and potential peril of AI, maximizing positive outcomes for customers and society at large?

Here are three things every company can address to ensure sustainability when scaling AI initiatives.

1) The teams building and deploying AI – and the datasets used to train models – must be representative of the diversity of customers and society at large. Explicit hiring goals and ongoing data fairness audits are table stakes

In the world of AI and machine learning, data and models can sometimes obscure the hard truths of a person’s lived experience. Since ML models are trained on historical data, they can amplify any discrimination or unequal power structures present in that historical data. Models trained on the past few years worth of housing data, for example, might reflect the continued legacy of redlining.

While most teams believe this is a problem and want to solve it, even sophisticated data scientists can find it challenging to detect and mitigate every possible fairness issue to retrain models accordingly. Often, it’s not because of bad intentions on the part of the data scientist – rather, it’s a blind spot that is a corollary of the lack of diversity on teams.

The only real remedy to this long term is diversity – and explicit hiring goals along with accountability to get there in the form of executives being measured on the success of these efforts and transparency to the board or public.

2) Not unlike how organizations manage privacy, enterprises should develop a codified ethical and risk governance framework for AI.

A siloed, technology-centric approach alone cannot mitigate every risk or make AI ethically responsible. The answer must involve implementing systems that identify both ethical and organizational risks throughout the company, from IT to HR to marketing to product and beyond, and incentivizing people to act on them. While technology is often a necessary prerequisite to surface the right problems, employees need to be empowered to act on these insights.

The good news is that there are a wealth of resources for an enterprise to kick off this process, from developing an organization-specific plan to operationalize AI ethics to ensuring technical teams implement procurement frameworks specifically designed with proactive model monitoring and ethics in mind.

3) Enterprises should ensure they have a modernized data policy that grants AI practitioners access to protected data where needed.

Data scientists and machine learning engineers can’t fix what they can’t see. According to a survey of over 600 data scientists and ML engineers by Arize AI, 79.9% of teams report that they “lack access to protected data needed to root out bias or ethics issues” at least some of the time. Nearly half (42.1%) say this is a frequent issue.

Moving toward a responsible AI framework means modernizing policies around access to data and in some cases expanding permissions by role. Most enterprises are good at this in software development – where access to production systems is tightly managed – but fewer have detailed governance around access to customer data in machine learning.

It is worth noting that expanding data access need not conflict with broader compliance or privacy goals. While many ML teams historically lacked access to protected class data for legal liability reasons, that’s beginning to change precisely because such data across the full ML lifecycle is critical to delivering accountability and ensuring a model’s outputs are not biased or discriminatory.


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