KPMG and IBM Watson OpenScale™: Establishing trust in AI for business success
Trust in AI is a hot topic in the headlines. Can we trust the decisions AI models recommend? Can we trust that AI will not perpetuate bias? Can we explain the inner workings of AI to auditors and key stakeholders in a business? For multinational financial services enterprises like KPMG, trust in AI is critical to their adoption of the technology.
In this Data and AI Virtual forum, a Forrester-led panel of AI leaders discussed how establishing trust in AI – for internal and external stakeholders – promotes their competitive edge in the marketplace.
Moderated by Srividya Sridharan, a vice president of Research at Forrester, the panel features a discussion between Kelly Combs, Director of Emerging Technology Risk Services at KPMG, and Deborah Leff, Global Leader and Industry CTO for Data Science and AI at IBM.
Watch the session replay or continue reading for highlights from the panel discussion.
It’s really all about trust
“Only about 5 percent of KPMG clients are heavily adopting AI at this point,” says Kelly Combs. “Why is that? And what’s holding them back?”
According to Combs, transparency and trust about how AI functions accounts for slower adoption. The panel discussion covers a number of topics that address trust in AI:
- AI black box concept – lack of understanding about what AI does creates mistrust.
- AI integrity – detecting and preventing AI bias and drift, which may undermine trust that models will perform as intended or arrive at fair conclusions.
- AI explainability – AI models and processes must be explainable to regulators, line of business and external stakeholders.
- AI and data and information architecture – whether businesses are structurally ready to experience the full benefits of AI.
The AI black box
“To many who aren’t data scientists,” says Combs, “AI still is a black box and that scares us.” Many are reluctant to adopt AI because they don’t fully understand it. And the fear is real. In the highly regulated financial industry, institutions face serious legal and financial consequences if AI models are incorrect or misinterpreted.
“What companies are really finding is that it’s not enough to bring on a data science team,” says Deborah Leff. “It’s that trust in what those models are saying is what’s causing tremendous heartburn. There’s a lot of fear they could do something very harmful to their company if they’re not careful.”
How can we foster trust in models and AI Systems? Data integrity.
AI integrity: preventing drift and bias
One of the most important concerns for organizations using AI is ensuring data integrity. To recognize how and why AI systems come to particular conclusions, enterprises need to understand the origin and quality of data and how AI models are trained. Fairness in AI begins with AI models built, trained and monitored to prevent bias and drift.
- Bias in AI occurs when models give preferential treatment to privileged groups. When models introduce bias into loan or credit approvals or other sensitive financial transactions, customers may be harmed and banks may be exposed to substantial legal and financial penalties.
- Drift occurs when AI models evaluate data that is different or has changed from the data on which the model was trained, degrading their accuracy over time. Learn more about AI Model Drift.
“Financial institutions are using AI and machine learning to determine the creditworthiness of loan applicants, looking at attributes such as FICO scores, age and income,” says Combs. “It is the responsibility of the financial institutions to explain why and how a person was declined for a loan.”
How can companies monitor models against bias?
“The first step is to actually think outside of the box and be vigilant and almost be paranoid.” says Leff. “Are we thinking about all of the things we should be thinking about? Is it possible that this model has inherent bias built into it?”
Bias is not always easy to detect and can creep into models even when the data scientists omit filters likely to discriminate such as gender, age, ethnicity or race. An AI model used in hiring that prefers candidates who own a car may inadvertently discriminate against people of color, who have a lower per-capita percentage of automobile ownership. Similarly, an AI model trained on the resumes of males may inadvertently perpetuate gender bias.
Forrester moderator Srividya Sridharan points out that “technologies and tools are actually giving us humans a great way to uncover the biases that may already have existed.” Among these solutions, IBM Watson OpenScale™, now a part of Watson Studio on IBM Cloud Pak for Data, helps weed out bias and drift in models.
“If I need to articulate how and why a system came to its determination and what were the data attributes it used,” says Kelly Combs, “IBM Watson OpenScale™ on IBM Cloud Pak for Data is one of the only technologies in the marketplace that is helping solve and give transparency in business terms to our clients.”
Learn more about how on IBM Cloud Pak for Data helps ensure that AI is trustworthy, transparent and compliant.
Explainability: Bridging the gap in AI understanding
Solving data integrity issues is just one part of establishing trust and getting buy-in from key stakeholders, agreement that is difficult to achieve without a clear and through explanation and understanding of the workings of the AI system.
“Data science is highly technical,” says Deborah Leff. “And a lot of folks in an organization tend to shut down when a conversation gets too technical.” Deborah describes a common scenario. Data science teams create groundbreaking projects, but business leaders reject the models and their conclusions. Why? Often they were not involved in the training process, don’t fully understand the AI, and therefore lack trust in the system.
“If there isn’t trust there, then you see humans starting to work against AI to try and circumvent it and go around it,” says Leff. “And that means that all of that investment that the company has made in developing those models is really almost for naught.”
To learn more on how to establish trust in AI, register for this Data and AI Virtual Forum and watch the keynote “Trust – Hard to build, easy to break” by Ritika Gunnar, IBM VP of Data and AI.
Creating trust in AI requires uniting minds across the organization, from the C-suite, to legal compliance, to security and to line of business. Stakeholders must understand from the inside out what AI can do for their organizations so they can structure their operations and processes to accommodate AI. “AI needs to be elevated to a strategic area in the organization and cut across the entire fabric of the enterprise to make sure you have the right collaborators,” says Deborah Leff. Kelly Combs agrees. “There needs to be a tone at the top from leadership varying stakeholders and a division of responsibility between all those that are invested in AI capabilities.”
Data and Information Architecture and scaling AI
“Companies are having to balance between ‘how do we go faster, but how do we also make sure that it is trusted and is safe?’” says Leff.
A recent report by the MIT Sloan Management Review and The Boston Consulting Group found that 81 percent of business leaders do not understand the data and infrastructure required for AI. “Everyone has a data architecture,” says Deborah Leff, “but is it the right data architecture to support machine learning? Most companies find that it isn’t.”
IBM Cloud Pak for Data provides a solution to help create the trusted environment to inspire confidence in the integrity of data in a platform ready to support machine learning and deep learning processes.
“At the end of the day, this is about humans trusting the output of machines,” says Leff. “And if humans don’t understand well how that decision was arrived at, that brings into question whether or not they can trust that model.”
To learn more about how you can tackle the most common AI challenges around talent, trust, and data, register for IBM’s September Data and AI Virtual forum.