Controlling machine-learning algorithms and their biases

 

Here is a brief excerpt from an article written by Tobias Baer and Vishnu Kamalnathfor the McKinsey Quarterly, published by McKinsey & Company. To read the complete article, check out other resources, learn more about the firm, obtain subscription information, and register to receive email alerts, please click here.

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Myths aside, artificial intelligence is as prone to bias as the human kind. The good news is that the biases in algorithms can also be diagnosed and treated.
Companies are moving quickly to apply machine learning to business decision making. New programs are constantly being launched, setting complex algorithms to work on large, frequently refreshed data sets. The speed at which this is taking place attests to the attractiveness of the technology, but the lack of experience creates real risks. Algorithmic bias is one of the biggest risks because it compromises the very purpose of machine learning. This often-overlooked defect can trigger costly errors and, left unchecked, can pull projects and organizations in entirely wrong directions.
Effective efforts to confront this problem at the outset will repay handsomely, allowing the true potential of machine learning to be realized most efficiently.Machine learning has been in scientific use for more than half a century as a term describing programmable pattern recognition. The concept is even older, having been expressed by pioneering mathematicians in the early 19th century. It has come into its own in the past two decades, with the advent of powerful computers, the Internet, and mass-scale digitization of information. In the domain of artificial intelligence, machine learning increasingly refers to computer-aided decision making based on statistical algorithms generating data-driven insights (see sidebar, “Machine learning: The principal approach to realizing the promise of artificial intelligence”).Among its most visible uses is in predictive modeling. This has wide and familiar business applications, from automated customer recommendations to credit-approval processes. Machine learning magnifies the power of predictive models through great computational force.
To create a functioning statistical algorithm by means of a logistic regression, for example, missing variables must be replaced by assumed numeric values (a process called imputation). Machine-learning algorithms are often constructed to interpret “missing” as a possible value and then proceed to develop the best prediction for cases where the value is missing. Machine learning is able to manage vast amounts of data and detect many more complex patterns within them, often attaining superior predictive power.In credit scoring, for example, customers with a long history of maintaining loans without delinquency are generally determined to be of low risk.
But what if the mortgages these customers have been maintaining were for years supported by substantial tax benefits that are set to expire? A spike in defaults may be in the offing, unaccounted for in the statistical risk model of the lending institution. With access to the right data and guidance by subject-matter experts, predictive machine-learning models could find the hidden patterns in the data and correct for such spikes.

The persistence of bias

In automated business processes, machine-learning algorithms make decisions faster than human decision makers and at a fraction of the cost. Machine learning also promises to improve decision quality, due to the purported absence of human biases. Human decision makers might, for example, be prone to giving extra weight to their personal experiences. This is a form of bias known as anchoring, one of many that can affect business decisions. Availability bias is another. This is a mental shortcut (heuristic) by which people make familiar assumptions when faced with decisions. The assumptions will have served adequately in the past but could be unmerited in new situations. Confirmation bias is the tendency to select evidence that supports preconceived beliefs, while loss-aversion bias imposes undue conservatism on decision-making processes.

Machine learning is being used in many decisions with business implications, such as loan approvals in banking, and with personal implications, such as diagnostic decisions in hospital emergency rooms. The benefits of removing harmful biases from such decisions are obvious and highly desirable, whether they come in financial, medical, or some other form.

Some machine learning is designed to emulate the mechanics of the human brain, such as deep learning, with its artificial neural networks. If biases affect human intelligence, then what about artificial intelligence? Are the machines biased? The answer, of course, is yes, for some basic reasons. First, machine-learning algorithms are prone to incorporating the biases of their human creators. Algorithms can formalize biased parameters created by sales forces or loan officers, for example. Where machine learning predicts behavioral outcomes, the necessary reliance on historical criteria will reinforce past biases, including stability bias.

This is the tendency to discount the possibility of significant change—for example, through substitution effects created by innovation. The severity of this bias can be magnified by machine-learning algorithms that must assume things will more or less continue as before in order to operate. Another basic bias-generating factor is incomplete data. Every machine-learning algorithm operates wholly within the world defined by the data that were used to calibrate it. Limitations in the data set will bias outcomes, sometimes severely.

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Here is a direct link to the complete article.

Tobias Baer is a partner in McKinsey’s Taipei office, and Vishnu Kamalnath is a specialist in the North American Knowledge Center in Waltham, Massachusetts.

 

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