While AI has had dramatic successes in recent years, Fiddler Labs was started to address an issue that is critical — that of explainability in AI. Complex AI algorithms today are black-boxes; while they can work well, their inner workings are unknown and unexplainable, which is why we have situations like the Apple Card/Goldman Sachs controversy. While gender or race might not be explicitly encoded in these algorithms, there are subtle and deep biases that can creep into data that is fed into these complex algorithms. It doesn’t matter if the input factors are not directly biased themselves — bias can, and is, being inferred by AI algorithms.
Companies have no proof to show that the model is, in fact, not biased. On the other hand, there’s substantial proof in favor of bias based on some of the examples we’ve seen from customers. Complex AI algorithms are invariably black-boxes and if AI solutions are not designed in such a way that there is a foundational fix, then we’ll continue to see more such cases. Consider the biased healthcare algorithm example above. Even with the intentional exclusion of race, the algorithm was still behaving in a biased way, possibly because of inferred characteristics.
Prevention is better than cure
One of the main problems with AI today is that issues are detected after-the-fact, usually when people have already been impacted by them. This needs to be changed: explainability needs to be a fundamental part of any AI solution, right from design all the way to production – not just part of a post-mortem analysis. We need to have visibility into the inner workings of AI algorithms, as well as data, throughout the lifecycle of AI. And we need humans-in-the-loop monitoring these explainability results and overriding algorithm decisions where necessary.
What is explainable AI and how can it help?
Explainable AI is the best way to understand the why behind your AI. It tells you why a certain prediction was made and provides correlations between inputs and outputs. Right from the training data used to model validation in testing and production, explainability plays a critical role.
Explainability is critical to AI success
With the recent launch of Google Cloud’s Explainable AI, the conversation around Explainable AI has accelerated. Google’s launch of Explainable AI completely debunks what we’ve heard a lot recently – that Explainable AI is two years out. It demonstrates how companies need to move fast and adopt Explainability as part of their machine learning workflows, immediately.
But it begs the question, who should be doing the explaining?
What do businesses need in order to trust the predictions? First, we need explanations so we understand what’s going on behind the scenes. Then we need to know for a fact that these explanations are accurate and trustworthy, and come from a reliable source.
At Fiddler, we believe there needs to be a separation between church and state. If Google is building AI algorithms and also explains it for customers -without third party involvement – it doesn’t align with the incentives for customers to completely trust their AI models. This is why impartiality and independent third parties are crucial, as they provide that all important independent opinion to algorithm-generated outcomes. It is a catch-22 for any company in the business of building AI models. This is why third party AI Governance and Explainability services are not just nice-to-haves, but crucial for AI’s evolution and use moving forward.
Google’s change in stance on Explainability
Google has changed their stance significantly on Explainability. They also went back and forth on AI ethics by starting an ethics board only to be dissolved in less than a fortnight. A few top executives at Google over the last couple of years went on record saying that they don’t necessarily believe in Explainable AI. For example, here they mention that ‘..we might end up being in the same place with machine learning where we train one system to get an answer and then we train another system to say – given the input of this first system, now it’s your job to generate an explanation.’ And here they say that ‘Explainable AI won’t deliver. It can’t deliver the protection you’re hoping for. Instead, it provides a good source of incomplete inspiration’.
One might wonder about Google’s sudden launch of an Explainable AI service. Perhaps they got feedback from customers or they see this as an emerging market. Whatever the reason, it’s great that they did in fact change their minds and believe in the power of Explainable AI. We are all for it.
Our belief at Fiddler Labs
We started Fiddler with the belief of building Explainability into the core of the AI workflow. We believe in a future where Explanations not only provide much needed answers for businesses on how their AI algorithms work, but also help ensure they are launching ethical and fair algorithms for their users. Our work with customers is bearing fruit as we go along this journey.
Finally, we believe that Ethics plays a big role in explainability because ultimately the goal of explainability is to ensure that companies are building ethical and responsible AI. For explainability to succeed in its ultimate goal of ethical AI, we need an agnostic and independent approach, and this is what we’re working on at Fiddler.
Founded in October 2018, Fiddler's mission is to enable businesses of all sizes to unlock the AI black box and deliver trustworthy AI experiences for their customers. Fiddler’s next-generation Explainable AI Engine enables data science, product and business users to understand, analyze, validate, and manage their AI solutions, providing transparent and reliable experiences to their end users. Our customers include pioneering Fortune 500 companies as well as emerging tech companies. For more information please visit www.fiddler.ai or follow us on Twitter @fiddlerlabs.
It is a bipartisan sentiment that, left unchecked, AI can pose a risk to fairness in financial services. While the exact extent of this danger might be debated, governments in the US and abroad acknowledge the necessity and assert the right to regulate financial institutions for this purpose.
The June 26 hearing was the first wake-up call for financial services: they need to be prepared to respond and comply with future legislation requiring transparency and fairness.
In this post, we review the notable events of this hearing, and we explore how the US House is beginning to examine the risks and benefits of AI in financial services.
Two new House Task Forces to regulate fintech and AI
The fintech task force should have a nearer-term focus on applications (e.g. underwriting, payments, immediate regulation).
The AI task force should have a longer-term focus on risks (e.g. fraud, job automation, digital identification).
And explicitly, Chairwoman Waters explained her overall interest in regulation:
Make sure that responsible innovation is encouraged, and that regulators and the law are adapting to the changing landscape to best protect consumers, investors, and small businesses.
The appointed chairman of the Task Force on AI, Congressman Bill Foster (D-IL), extolled AI’s potential in a similar statement, but also cautioned,
It is crucial that the application of AI to financial services contributes to an economy that is fair for all Americans.
This first hearing did find ample AI applications in financial services. But it also concluded that these worried sentiments are neither misrepresentative of their constituents nor misplaced.
Risks of AI
In a humorous exchange later in the hearing, Congresswoman Sylvia Garcia (D-TX) asks a witness, Dr. Bonnie Buchanan of the University of Surrey, to address the average American and explain AI in 25 words or less. It does not go well.
DR. BUCHANAN I would say it’s a group of technologies and processes that can look at determining general pattern recognition, universal approximation of relationships, and trying to detect patterns from noisy data or sensory perception.
REP. GARCIA I think that probably confused them more.
DR. BUCHANAN Oh, sorry.
Beyond making jokes, Congresswoman Garcia has a point. AI is extraordinarily complex. Not only that, to many Americans it can be threatening. As Garcia later expresses, “I think there’s an idea that all these robots are going to take over all the jobs, and everybody’s going to get into our information.”
In his opening statement, task force ranking member Congressman French Hill (R-AR) tries to preempt at least the first concern. He cites a World Economic Forum study that the 75 million jobs lost because of AI will be more than offset by 130 million new jobs. But Americans are still anxious about AI development.
overwhelming support for careful management of robots and/or AI (82% support)
more trust in tech companies than in the US government to manage AI in the interest of the public
mixed support for developing high-level machine intelligence (defined as “when machines are able to perform almost all tasks that are economically relevant today better than the median human today”)
This public apprehension about AI development is mirrored by concerns from the task force and experts. Personal privacy is mentioned nine times throughout the hearing, notably in Congressman Anthony Gonzalez’s (R-OH) broad question on “balancing innovation with empowering consumers with their data,” which the panel does not quite adequately address.
But more often, the witnesses discuss fairnessand how AI models could discriminate unnoticed. Most notably, Dr. Nicol Turner-Lee, a fellow at the the Brookings Institution, suggests implementing guardrails to prevent biased training data from “replicat[ing] and amplify[ing] stereotypes historically prescribed to people of color and other vulnerable populations.”
And she’s not alone. A separate April 2019 Brookings report seconds this concern of an unfairness “whereby algorithms deny credit or increase interest rates using a host of variables that are fundamentally driven by historical discriminatory factors that remain embedded in society.”
So if we’re so worried, why bother introducing the Pandora’s box of AI to financial services at all?
Benefits of AI
AI’s potential benefits, according to Congressman Hill, are to “gather enormous amounts of data, detect abnormalities, and solve complex problems.” In financial services, this means actually fairer and more accurate models for fraud, insurance, and underwriting. This can simultaneously improve bank profitability and extend services to the previously underbanked.
Both Hill and Foster cite a National Bureau of Economic Research working paper finding where in one case, algorithmic lending models discriminate 40% less than face-to-face lenders. Furthermore, Dr. Douglas Merrill, CEO of ZestFinance and expert witness, claims that customers using his company’s AI tools experience higher approval rates for credit cards, auto loans, and personal loans, each with no increase in defaults.
Moreover, Hill frames his statement with an important point about how AI could reshape the industry: this advancement will work “for both disruptive innovators and for our incumbent financial players.” At first this might seem counterintuitive.
“Disruptive innovators,” more agile and hindered less by legacy processes, can have an advantage in implementing new technology. But without the immense budgets and customer bases of “incumbent financial players,” how can these disruptors succeed? And will incumbents, stuck in old ways, ever adopt AI?
Mr. Jesse McWaters, financial innovation lead at the World Economic Forum and the final expert witness, addresses this apparent paradox, discussing what will “redraw the map of what we consider the financial sector.” Third-party AI service providers — from traditional banks to small fintech companies — can “help smaller community banks remain digitally relevant to their customers” and “enable financial institutions to leapfrog forward.”
Enabling competitive markets, especially in concentrated industries like financial services, is an unadulterated benefit according to free market enthusiasts in Congress. However, “redrawing the map” in this manner makes the financial sector larger and more complex. Congress will have to develop policy responding to not only more complex models, but also a more complex financial system.
This system poses risks both to corporations, acting in the interest of shareholders, and to the government, acting in the interest of consumers.
Business and government look at risks
Businesses are already acting to avert potential losses from AI model failure and system complexity. A June 2019 Gartner report predicts that 75% of large organizations will hire AI behavioral forensic experts to reduce brand and reputation risk by 2023.
However, governments recognize that business-led initiatives, if motivated to protect company brand and profits, may only go so far. For a government to protect consumers, investors, and small businesses (the relevant parties according to Chairwoman Waters), a gap may still remain.
As governments explore how to fill this gap, they are establishing principles that will underpin future guidance and regulation. The themes are consistent across governing bodies:
AI systems need to be trustworthy.
They therefore require some government guidance or regulation from government representing the people.
This guidance should encourage fairness, privacy, and transparency.
In the US, President Donald Trump signed an executive order in February 2019 “to Maintain American Leadership in Artificial Intelligence,” directing federal agencies to, among other goals, “foster public trust in AI systems by establishing guidance for AI development and use.” The Republican White House and Democratic House of Representatives seem to clash at every turn, but they align here.
The EU is also establishing a regulatory framework for ensuring trustworthy AI. Likewise included among the seven requirements in their latest communication from April 2019: privacy, transparency, and fairness.
And June’s G20 summit drew upon similar ideas to create their own set of principles, including fairness and transparency, but also adding explainability.
These governing bodies are in a fact-finding stage, establishing principles and learning what they are up against before guiding policy. In the words of Chairman Foster, the task force must understand “how this technology will shape the questions that policymakers will have to grapple with in the coming years.”
Conclusion: Explain your models
An hour before Congresswoman Garcia’s amusing challenge, Dr. Buchanan reflected upon a couple common themes of concern.
Policymakers need to be concerned about the explainability of artificial intelligence models. And we should avoid black-box modeling where humans cannot determine the underlying process or outcomes of the machine learning or deep learning algorithms.
But through this statement, she suggests a solution: make these AI models explainable. If humans can indeed understand the inputs, process, and outputs of a model, we can trust our AI. Then throughout AI applications in financial services, we can promote fairness for all Americans.
Zhang, Baobao and Allan Dafoe. “Artificial Intelligence: American Attitudes and Trends.” Oxford, UK: Center for the Governance of AI, Future of Humanity Institute, University of Oxford, 2019. https://ssrn.com/abstract=3312874