Traditional machine learning models often yield biased results, favouring groups with large populations or being influenced by unknown factors, and take extensive effort to identify from instances containing patterns and sub-patterns coming from different classes or primary sources. The medical field is one area where there are severe implications for biased machine learning results. Hospital staff and medical professionals rely on datasets containing thousands of medical records and complex computer algorithms to make critical decisions about patient care. Machine learning is used to sort the data, which saves time. However, specific patient groups with rare symptomatic patterns may go undetected, and mislabeled patients and anomalies could impact diagnostic outcomes.

AI has fueled efficiencies across industries for years. It's old news by now, but as I've said before, that's a good thing. Conversations about AI sound much different today than they did 10 years ago. Instead of wondering whether AI will help businesses grow or increase bottom lines, the proliferation of the technology has pushed AI conversations in more meaningful and complex directions. One area I'm particularly interested in is data privacy and biases in AI models.

The use of artificial intelligence technology in banking has great potential, much of it still untapped. It's use in powering chatbots and digital assistants using natural language processing is of the best-known AI applications. AI can also be used as part of data analytics, helping banks and credit unions detect fraud more quickly on the one hand and create more personalized customer messaging and offers on the other. Significantly, AI can help make institutions -- bank and nonbank -- make faster lending decisions. However, there is downside to the use of artificial intelligence, the consequences of which loom ominously for banks and credit unions.

Management guru W. Edwards Deming famously said: "In God we trust. All others must bring data." But how far can we trust the data? This is becoming an important question, as the artificial intelligence systems now being built and deployed across the business landscape are only as good as the data being fed into them, along with the algorithms running the data. AI systems are now making decisions on customer value, courses of action, and operational viability, just to name a few vital functions.

That the regulation of Artificial intelligence is a hot topic is hardly surprising. AI is being adopted at speed, news reports frequently appear about high-profile AI decision-making, and the sheer volume of guidance and regulatory proposals for interested parties to digest can seem challenging. What can we expect in terms of future regulation? And what might compliance with "ethical" AI entail? High-level ethical AI principles were made by the OECD, EU and G20 in 2019.

That the regulation of Artificial intelligence is a hot topic is hardly surprising. AI is being adopted at speed, news reports frequently appear about high-profile AI decision-making, and the sheer volume of guidance and regulatory proposals for interested parties to digest can seem challenging. What can we expect in terms of future regulation? And what might compliance with "ethical" AI entail? High-level ethical AI principles were made by the OECD, EU and G20 in 2019.

Artificial Intelligence (AI) has gained favor as the current buzzword for things related to technology in the popular press. Every day, we see news articles proclaiming that AI has solved some or the other problem in diverse fields. However, a natural question remains unanswered: What exactly is AI? Intelligence is normally associated with a capacity to learn from experience and making decisions based on the learned knowledge. It may also involve understanding complex ideas. But the most important aspect of intelligence is the capacity to apply the experience gained in one context to solve problems in a completely different context. A child quickly learns not to go near a situation that may be detrimental, for example, an open fire or a body of water.

Institutions that include everyone understand that great benefit comes from seeing complex issues in many different ways. The most life-changing, rapid, and one-off decisions people must make are those to do with their health, and the health of their loved ones. Here too, the benefits of diversity are well understood. In medicine, there is a culture of "second opinions" – you can always ask another doctor for their opinion on a choice. This is acknowledged as a great strength of the medical community; indeed, the seeking of diverse (even possibly contradictory) opinions is actively supported by professionals realistic and humble enough to accept that there may not be one single right answer.

Consider a person who applies for a loan with a financial company, but their application is rejected by a machine learning algorithm used to determine who receives a loan from the company. How would you explain the decision made by the algorithm to this person? One option is to provide them with a list of features that contributed to the algorithm's decision, such as income and credit score. Many of the current explanation methods provide this information by either analyzing the algorithm's properties or approximating it with a simpler, interpretable model. However, these explanations do not help this person decide what to do next to increase their chances of getting the loan in the future.

Show your working: generations of mathematics students have grown up with this mantra. Getting the right answer is not enough. To get top marks, students must demonstrate how they got there. Now, machines need to do the same. As artificial intelligence (AI) is used to make decisions affecting employment, finance or justice, as opposed to which film a consumer might want to watch next, the public will insist it explains its working.