Successfully leveraging artificial intelligence (AI) and machine learning (ML) to deliver insights that drive better business decisions is at the top of modern enterprise agendas. In fact, Gartner has reported that by the end of 2024, 75% of businesses will shift from piloting to operationalizing AI, and for good reason. AI models have been proven to enhance critical processes that influence bottom lines, from predicting and preventing churn to detecting instances of fraud.
But AI has also made headlines for producing harmful business and societal results, such as discriminating against individuals based on race or gender. In most cases, these instances are the result of organizations having limited to no insight into why and how their models are making certain decisions. And without having visibility into how the model is working and how it was built, it is difficult to ensure the AI is being deployed in a meaningful way.
The challenge lies in that most AI tools today offer limited visibility into the entire model development lifecycle. To deploy trustworthy, safe, and transparent models, companies need to be able to monitor each and every step. On top of having the right tools in place, organizations also need to adopt a new mindset and set of principles to ensure long-term, enterprise-wide AI success.
What Is Responsible AI and Why Does It Matter?
Every model an organization deploys should be grounded in responsible AI. Responsible AI is the practice of building AI that is transparent, accountable, ethical, and reliable.
When AI is developed responsibly, users will be able to govern and audit models to understand how and why a decision is made. As a result, organizations have greater visibility into the AI post-deployment, the AI system continuously performs as expected in production, and outcomes are fair and more reliable.
This becomes even more important when considering the implications of AI bias, model drift, and AI regulations – all of which create significant challenges for businesses that aren’t focused on responsible AI.
Use Responsible AI Practices to Address:
1. AI Bias
The problem with model biases is that they can be hard to detect until AI initiatives are already deployed at scale. Whether the model was trained based on biased or incomplete data or the individual training the model exhibited inherent bias, the end result is harmful to both brands and society. Take Apple’s AI-backed credit card application process, which was charged with discriminating against women back in 2019. The problem came to light when one software developer was approved for 20 times more credit than his spouse even though she had a higher credit score.
There’s also the possibility of proxy bias entering into a model – such as when users apply a zip code feature. Zip codes have a high proxy given their high correlation with race and ethnicity. As a result, users unknowingly introduced a proxy bias into the model that could discriminate against certain groups based on their location.
The financial and reputational repercussions of this type of model behavior could be detrimental for some brands. Responsible AI ensures any instance of bias is caught before the model ever makes its way into production, allowing for adjustments that will prevent unwanted, inaccurate, and unfair results.
2. Model Drift
In addition to avoiding instances of bias, organizations must also be prepared to handle model drift. Whether a model is responsible for predicting fraud, approving loans, or targeting ads, small changes in model accuracy can result in significant impacts to the bottom line. Over time, even highly accurate models are prone to decay as the incoming data shifts away from the original training set.
There are three types of model drift that can occur: concept drift, feature drift, and label drift. In instances of concept drift, there’s been a change in the underlying relationships between features and outcomes. In the case of a loan application, concept drift would occur if there was a macro-economic shift that made applicants with the same feature values (e.g., income, credit score, age) more or less risky to receive a financial loan.
Feature drift occurs when there are changes in the distribution of a model’s inputs. For example, over a specific time frame, the loan application model might receive more data points from applicants in a particular geographic region.Label drift, on the other hand, indicates there’s been a change in a model’s output distribution – which might be a higher-than-normal ratio of approval predictions to non-approval predictions.
Ultimately, model drift can lead to outcomes that no longer align with the original purpose of the AI model without the organization even being made aware. The production quality dwindles, and organizations are left with untrustworthy models that deliver inconsistent, inaccurate predictions – which can lead to financial losses, customer complaints, and brand damage. Responsible AI will detect and alert users of model drift before the model fully decays, allowing for faster root cause determination and resolution so the model can be put back into production.
3. AI Regulations
The regulatory environment around AI and ML has continued to evolve, particularly following the European Commission’s AI legal framework published last year. The framework assigns different risk levels for various AI applications, including self-driving cars and job applicant scanning systems.
The U.S. followed suit later in the year when the White House Office of Science and Technology Policy group released a proposal that would define a Bill of Rights for the AI age. The document includes language that aims to protect people from being affected by AI unknowingly or by AI that hasn’t undergone stringent auditing.
Though compliance dates might be a year or two in the future, preparing for these regulations to go into effect should happen now. Algorithms that can’t pass regulatory audits or demonstrate how they arrived at a specific conclusion won’t survive in a more tightly regulated environment – and brands will be looking at significant fines if they’re caught leveraging opaque models.
How to Bring Responsible AI to Life
Adopting a new, enterprise-wide cultural mindset is an essential piece of ensuring AI success. But just adopting the practice of responsible AI to identify potential risks to models isn’t enough. Actively monitoring the hundreds of thousands of algorithms most enterprises have in production today requires an advanced AI explainability and MLOps solution known as Model Performance Management (MPM).
MPM solutions have the power to monitor, explain, analyze, and improve models through the entire ML lifecycle. From a single viewpoint, organizations can record their AI models and training data; conduct an automated assessment of feature quality, bias, and fairness; ensure human approval of models prior to launch; continuously stress test models; and gain actionable insights to improve models as data changes. MLOps engineers and data scientists are provided with a tool that can actively monitor every model in training and production, allowing for earlier detection of hidden biases, drift, and non-compliance.
Algorithms have the power to deliver incredible business results, but only if they are continuously monitored. As every organization seeks out more opportunities to successfully leverage AI and ML models, adopting responsible AI practices will be a necessity. Companies that have a clear understanding of how and why their models have come to certain conclusions – and can confirm those models have not drifted and are not exhibiting bias – will be the ones to evolve in their AI journey.