What is AI?

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry. 

The different types of AI:

At a very high level, artificial intelligence can be split into two broad types: narrow AI and general AI.

• Narrow AI – Narrow AI is what we see all around us in computers today: intelligent systems that have been taught or learned how to carry out specific tasks without being explicitly programmed how to do so. This type of machine intelligence is evident in the speech and language recognition of the Siri virtual assistant on the Apple iPhone, in the vision-recognition systems on self-driving cars, in the recommendation engines that suggest products you might like based on what you bought in the past. Unlike humans, these systems can only learn or be taught how to do specific tasks, which is why they are called narrow AI.

There are a vast number of emerging applications for narrow AI: interpreting video feeds from drones carrying out visual inspections of infrastructure such as oil pipelines, organizing personal and business calendars, responding to simple customer-service queries, coordinating with other intelligent systems to carry out tasks like booking a hotel at a suitable time and location, helping radiologists to spot potential tumors in X-rays, flagging inappropriate content online, detecting wear and tear in elevators from data gathered by IoT devices, and so much more.

• General AI – Artificial general intelligence is very different, and is the type of adaptable intellect found in humans, a flexible form of intelligence capable of learning how to carry out vastly different tasks, anything from haircutting to building spreadsheets, or to reason about a wide variety of topics based on its accumulated experience. 

What is machine learning?

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

One of the most common mistakes among machine learning beginners is testing training data successfully and having the illusion of success; Domingo (and others) emphasize the importance of keeping some of the data set separate when testing models, and only using that reserved data to test a chosen model, followed by learning on the whole data set.

In terms of purpose, machine learning is not an end or a solution in and of itself. Furthermore, attempting to use it as a blanket solution.

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There is clearly a mixture of excitement about a future driven by digital and AI and a desire to better understand what it means and how to prepare for it. Everybody is discerning and working on AI and their digital future. Many executives have come to terms with the idea that disruption is a fact of life and that their companies need to transform.

But what exactly is AI and how can it shape the future of GRC?

Artificial intelligence (AI) deals with building smart machines capable of performing tasks that typically require human intelligence. Patrick Winston, the Ford professor of artificial intelligence and computer science at MIT, defines AI as “algorithms enabled by constraints, exposed by representations that support models targeted at loops that tie thinking, perception and action together.”

According to Wikipedia, AI, sometimes called machine intelligence, is intelligence validated by machines, in contrast to natural intelligence displayed by humans and animals. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.

The term is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving”

Although artificial intelligence educes thoughts of science narrative, studies have shown that it already has many usages today:

• Spam filters on email;

• Personalization: Online services use artificial intelligence to personalize experience. Services, like Amazon or Netflix, “learn” from an individual’s previous purchases and the purchases of other users in order to recommend relevant content;

• Fraud detection: Banks, for instance, use artificial intelligence to determine if there is strange activity on an account. Unexpected activity, such as foreign transactions, could be flagged by the algorithm;

• Smart assistants (like Siri and Alexa);

• Disease mapping and prediction tools;

• Manufacturing and drone robots;

• Optimized, personalized healthcare treatment recommendations;

• Conversational bots for marketing and customer service;

• Robo-advisors for stock trading;

• Social media monitoring tools for dangerous content or false news; and

• Song or TV show recommendations from Spotify and Netflix.

Hardly a day passes without a news story about a high-profile data breach or a cyber-attack costing millions and millions of dollars in damages. Cyber losses are difficult to approximate, but the International Monetary Fund [IMF] places them in the range of US$100–$250 billion annually for the global financial services industry.

Furthermore, with the ever-growing pervasiveness of computers, mobile devices, servers and smart devices, the cumulative threat exposure grows each day.

While the business and policy groups are still beleaguered to shawl their heads around the cyber realm’s brand-new importance, the use of AI to cyber security is foreshowing even greater changes.

One of the fundamental purposes of AI is to automate tasks that heretofore would have required human intelligence. Cutting down on the labor resources an organization must employ to complete a project, or the time an individual must devote to routine tasks, enables terrific gains in efficiency.

The nature of risks is unremittingly fluctuating and evolving at unprecedented levels and hence implementing a successful risk management program is the call for organizations looking to safeguard their hard-earned reputation. Failure to do so could be injurious, as many organizations in the past have realized the hard way.

The standard organizational framework used to manage risk and compliance are the three [3] lines of defense:

• The first line of defence (functions that own and manage risks);

• The second line of defence (functions that oversee or who specialise in compliance or the management of risk); and

• The third line of defence (functions that provide independent assurance).

A key requirement of the lines of defense is the assistance provided to various levels of management. While first and second lines of defense are archetypally organized to support levels of management, the 3rd line of defense classically works with management and the board to surface risks and compliance issues and works to address slits and deficiencies.

In order to provide proper assistance for these levels of management, the lines of defense need to provide insights that enable:

• Enriched execution on a daily basis of the performance of risk and control activities;

• Finer and tenacious control and management of the activities, and

• Forward and outward looking comprehensions for strategic risk management.

Integrated GRC platform is the only solution to help businesses manage risks across the organization while driving overall enterprise performance and being flexible enough to keep pace with a rapidly-changing environment.

As these platforms allow companies to meet their GRC targets by automating the workflow, many organizations are espousing GRC platforms to augment their operational activities.

In this day and age of disruption, technology is a sturdy enabler of business. And arguably, few developments in technology have generated as much interest as AI. From digital assistants to streaming services, AI is ubiquitous, with seemingly endless possibilities. But beyond all the flimflam, what are the practical applications of AI in GRC?

Artificial Intelligence (AI) in GRC is the need of the hour. As companies expand their digital footprints, cyber security vulnerabilities increase due to huge amount of data being produced. Surely, the demand for intelligent use of accumulated risk data will only increase.

GRC solutions that incorporate AI and its application Machine Learning (ML), will play a key role. The key players in GRC industry are working hard to offer AI-as-a-Service (AlaaS), particularly to industries where data is too valuable.

A recent report  found that the use of artificial intelligence will bring about massive changes to GRC. By automating payments, calculating risk, and maintaining records, the study broke down how the technology will influence each role within GRC:

• Risk manager –With the rise of AI, risk managers’ tasks will fundamentally shift to data-based identification and interpretation of changes in risk exposures. This includes the ability to assess trends by exploring existing facts and applying cognitive skills to understand the analyses of large volumes of data;

• Compliance manager –With automated reports, the future responsibility of compliance managers goes one step further: identifying internal or external dangers as well as the management of cybercrime. This will require the ability to work adroitly and to solve problems independently;

• Fraud examiner – The role of the fraud examiner will shift intensely as artificial intelligence becomes more ubiquitous. The main tasks will move from reviewing reports to performing fraud assessments and developing KRIs for avoiding future cases of fraud;

• Auditor – The role of the auditor may not change markedly; and

• Treasury manager –With AI, the treasury manager must build up new expertise to be able to utilize technology to monitor liquidity and risk management, to monitor and optimize cash-flow streams, and to give recommendations to the executive board with regard to strategy development.

While many fear that the widespread use of automation will displace white-collar jobs, AI is far more likely to be used as an augmentation tool.

Overall, productivity will improve and fast-track implementation of elementary financial tasks. It will also impact almost every role within finance and GRC, and rather than hiding behind fear, should motivate everyone to further develop their methodological skills to keep pace with transformation.

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It is not that smart algorithms will one day become too smart, as some fear; not that smart machines will one day overshadow human intellect. Rather the danger is that artificial intelligence (AI) machines are viewed by people as more impartial than they are; that their decisions are more objective than those of people. They are not.

I have heard wise people speak of AI with reverence, almost as if it were superhuman. They are wrong to do so. This is not to say that AI should be trivialized. AI can offer important clinical insights. And smart algorithms can save time.

Some pundits predict that AI will be fundamentally necessary for the next generation of physicians. While smart algorithms may not replace physicians, those who use them may replace those who don’t. If this statement is true, it is all the more important that the limitations of AI be appreciated.

SEEING AI CLEARLY

Put simply, AI has the same vulnerabilities as people do. This applies especially to machine learning (ML), the most modern form of artificial intelligence. In ML, algorithms dive deep into data sets. Their development may be weakly supervised by people. Or it may not be supervised at all.

This laissez-faire approach has led some to believe that the decisions of ML algorithms are free from human failings. But they are wrong. Here’s why.

First, even among self-taught deep learning algorithms, the parameters of their learning are established by people. Second, the data used to train these algorithms is gathered by people. Either instance can lead to the incorporation of human biases and prejudices in algorithms.

This has already happened — with negative results — in other fields of work. For example, algorithms intended as sentencing aides for judges have shown “an unnerving propensity for racial discrimination,” wrote David Magnus, Ph.D., director of the Stanford University Center for Biomedical Ethics, and his Stanford colleagues in a March 2018 issue of the New England Journal of Medicine.1 Healthcare delivery already varies by race. “Racial biases could inadvertently be built into healthcare algorithms, wrote Magnus and colleagues. And there is strong potential for purposeful bias.  

A third-party vendor, hoping to sell an algorithm to a healthcare system, could design an algorithm to align with the priorities of the health systems — priorities that may be very different from those of patients or physicians. Alignment of product and buyer is an accepted tenet of commerce.

One high priority of health systems might be the ability of the patient — either personally or through insurance — to pay for medical services. It is hard to believe that for-profit developers of algorithms would not consider this. And institutional priorities may not even be knowingly expressed.

Magnus and colleagues wrote in the NEJM article that ethical challenges to AI “need to be guarded against.” Unfortunately, such challenges could arise even when algorithms are not supervised by people. When algorithms do deep dives into data sets to discover “truths” on their own, the data might not have included some patient populations.

This could happen due to the influence of the “health-wealth” gradient, Magnus said last fall during his presidential keynote delivered at the annual meeting of the American Society for Radiation Oncology (ASTRO). This gradient can occur when patient data is only included if patients had the ability or insurance to pay for care.

And this is what could happen inadvertently. What if algorithm developers give in to greed and corruption? “Given the growing importance of quality indicators for public evaluations and determining reimbursement rates, there may be a temptation to teach machine learning systems to guide users toward clinical actions that would improve quality metrics, but not necessarily reflect better care,” the Stanford authors wrote in the NEJM. “Clinical decision support systems could also be programmed in ways that would generate increased profits for their designers or purchasers without clinical users being aware of it.”

FACTORING IN QUALITY OF LIFE

Even if precautions are taken, and the developers of ML algorithms are more disciplined than software engineers elsewhere, there is still plenty of reason to be wary of AI. It bears noting again that the data on which ML algorithms are trained and/or do their analyses are gathered by people. As a result, this data may reflect the biases and prejudices of these people.  

Additionally, results could be skewed if data is not included on certain specific patient groups, for example, the elderly or very young. It should be noted that most clinical testing is done on adults. Yet that doesn’t keep the makers of OTC drugs from extrapolating dosages for children. 

But algorithms trained on or just analyzing incomplete data sets would not generate results applicable to the very young or very old. Notably I was told by one mega-vendor that its AI algorithm had not been cleared by the FDA for the analysis of pediatric cases. Its workaround for emergency departments? Report the results and state that the age of the patient could not be identified, leaving the final decision up to the attending physician.

As this algorithm is intended to identify suspicious cases, it seems reasonable to do so. But would the same apply if the algorithm is designed to help radiologists balance the risk and benefit of exposing patients to ionizing radiation? If cancer is suspected, doing so makes sense. But what about routine screening for the recurrence of cancer? What if the patient is very young? Or very old? These are just some of the myriad concerns that underscore the main point — that smart algorithms may not be so smart. At the very least, they are vulnerable to the same biases and prejudices as people are, if not in their actual design then in their analysis of clinical data. Recognizing these shortcomings is all the more important when radiologists are brought in to help manage patient care.

SEEING AI FOR WHAT IT IS

In summary, then, AI is not the answer to human shortcomings. Believing it is will at best lead to disappointment. The deep learning algorithms that dive into data sets hundreds, thousands or even millions of times will be only as good as the data into which they dive. The Stanford wrote that it may be difficult to prevent algorithms from learning and, consequently, incorporating bias. If gathered by people, the data almost assuredly will reflect the shortcomings of those who gathered it.

So, while ML algorithms may discover patterns that would otherwise escape people, their conclusions will likely be tainted. The risk presented by AI, therefore, is not that its algorithms are inhuman — but that they are, in fact, too human.

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Machine learning is exploding, with smart algorithms being used everywhere from email to smartphone apps to marketing campaigns. Translation: if you’re looking for an in-demand career, setting yourself up with the skills to work with smart machines/artificial intelligence is a good move.

With input from Florian Douetteau, CEO of Dataiku, here are some things you can start doing today to position yourself for a future career in machine learning.

1. Understand what machine learning is.

This may sound obvious, says Douetteau, but it’s important. “Having experience and understanding of what machine learning is, understanding the basic maths behind it, understanding the alternative technology, and having experience — hands-on experience — with the technology is key.”

2. Be curious.

Machine learning and AI are modern things that will only continue to evolve in the future, so having a healthy sense of curiosity and love of learning is essential to keep learning new technologies and what goes with them.

“Machine learning, as a demand, evolved quite rapidly in the last few years with new techniques, new technology, new languages, new frameworks, new things to learn, which made it very important for people to be eager to learn,” says Douetteau. “Meaning, get online, read about new frameworks, read new articles, take advantage of online courses and Coursera, and so forth. Trait number one if you want to be successful as someone working in machine learning is to be curious.”

3. Translate business problems into mathematical terms.

Machine learning is a field practically designed for logical minds. As a career, it blends technology, math, and business analysis into one job. According to Douetteau, “You need to be able to focus on technology a lot, and to have this intellectual curiosity, but you must also have this openness toward business problems and be able to articulate a business problem into a mathematical machine learning problem, and bring value at the end.”

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