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.
Sidebar
Machine learning: The principal approach to realizing the promise of artificial intelligence
Artificial intelligence is the science and engineering of automated problem solving. The object is to generate solutions by using computers to mimic the cognitive functions associated with deliberative thought, including perception, reasoning, and learning.
Machine learning is the most prevalent means by which the potential of artificial intelligence is being exploited. The term refers to the ability of computers to detect patterns in large data sets through the application of algorithms. In addition to uncovering potentially powerful insights in the data, computers can be programmed to train themselves to make data-driven predictions.
Predictive modeling, also called supervised learning, is a machine-learning approach that builds pattern-recognition models using sample data with known attributes and outcomes (labeled “training data”). Working from the known patterns, the model can predict outcomes for new observations. The form of data used to predict outcomes can be structured or unstructured, whether or not supervised learning is applied. However, unstructured data can be processed directly only through machine learning; when more traditional techniques such as regression are used, the data scientist must first aggregate unstructured data into structured data based on business rules or independent analyses and procedures.
Deep learning is the most advanced technique for predictive modeling. It connects software-based calculators to form a complex artificial “neural network,” often 50 or more layers deep. The simplest predictive-modeling techniques are regression modeling and simple decision trees. More advanced techniques include random forests (a more complex and sensitive decision-tree model) and support vector machines (for sophisticated data classification).
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.
