The past year has seen artificial intelligence (AI) become a dinner-table topic of conversation around the world, thanks to bots such as ChatGPT, which dazzles users with its ability to compose lifelike text and even computer code. But what happens when AI makes wrong decisions? Bias – and gender bias in particular – is common in AI systems, leading to a variety of harms, from discrimination and reduced transparency, to security and privacy issues. In the worst cases, wrong AI decisions could damage careers and even cost lives. Without dealing with AI's bias problem, we risk an imbalanced future – one in which AI will never reach its full potential as a tool for the greater good.

Artificial intelligence has developed from a marginal concept of computer science in the 1950s to a well-known term throughout popular culture, science, and technology. Whether we are skeptical, excited, or afraid of artificial intelligence. This cannot stop the rapid development of artificial intelligence. Artificial intelligence began to play an important role in various fields, such as medicine; engineering; marketing; Siri; Alex, and so on. We seem to be inseparable from artificial intelligence.

A few years ago, when I was still working for IBM, I managed an AI project for a bank. During the final phase, my team and I went to the steering committee to present the results. Proud as the project leader, I have shown that the model has achieved 98 percent accuracy in detecting fraudulent transactions. In my manager's eyes, I could see a general panic when I explained that we used an artificial neural network, that it worked with a synapse system and weight adjustments. Although very efficient, there was no way to understand its logic objectively. Even if it was based on real facts, this raw explanation conditioned the project's continuity at that time, unless we could provide a full explanation that the senior executive could understand and trust.

A few years ago, when I was still working for IBM, I managed an AI project for a bank. During the final phase, my team and I went to the steering committee to present the results. Proud as the project leader, I have shown that the model has achieved 98 percent accuracy in detecting fraudulent transactions. In my manager's eyes, I could see a general panic when I explained that we used an artificial neural network, that it worked with a synapse system and weight adjustments. Although very efficient, there was no way to understand its logic objectively.

Explainable machine learning is a sub-discipline of artificial intelligence (AI) and machine learning that attempts to summarize how machine learning systems make decisions. Summarizing how machine learning systems make decisions can be helpful for a lot of reasons, like finding data-driven insights, uncovering problems in machine learning systems, facilitating regulatory compliance, and enabling users to appeal -- or operators to override -- inevitable wrong decisions. Of course all that sounds great, but explainable machine learning is not yet a perfect science. Figure 1: Explanations created by H2O Driverless AI. These explanations are probably better suited for data scientists than for business users.

In the ever-changing data science landscape, skillset evolves as new tools and techniques surface. Based on the trends, data scientists should focus on emerging and most in-demand skills to stay abreast of the changing needs. Although data scientists possess several skills, they utilise only a handful of them due to the current requirements. This makes them experts in some skills while being mediocre in others. However, with the shift in the market, they need to reposition themselves for being relevant and add value to the businesses.

Deep learning has the potential to revolutionise the insurance sector – but the challenge is how to make the artificial intelligence (AI) models auditable. Check out the latest findings on how the hype around artificial intelligence could be sowing damaging confusion. Also, read a number of case studies on how enterprises are using AI to help reach business goals around the world. You forgot to provide an Email Address. This email address doesn't appear to be valid.

A possibly lethal exception could be the error that leads a self-driving car's AI to make the wrong decision at the wrong time. That is why researchers developed a bug-hunting method that can systematically expose bad decision-making by the deep learning algorithms deployed in online services and autonomous vehicles. The new DeepXplore method uses at least three neural networks--the basic architecture of deep learning algorithms--to act as "cross-referencing oracles" in checking each other's accuracy. Researchers at Columbia University and Lehigh University designed DeepXplore to solve an optimization problem in which they looked to strike the best balance between two objectives: maximizing the number of neurons activated within neural networks, and triggering as many conflicting decisions as possible among different neural networks. By assuming that the majority of neural networks will generally make the right decision, DeepXplore automatically retrains the neural network that made the lone dissenting decision to follow the example of the majority in a given scenario.

Machine learning technologies can help companies spot suspicious user behaviors, malicious software, and fraudulent purchases -- but even as the defensive technologies are getting better, attackers are finding ways to get around them. Many defensive systems need to be tuned, or tune themselves, in order to appropriately respond to possible threats. Smoke alarms that go off each time someone microwaves popcorn get replaced with less sensitive ones, or are moved farther away from the kitchen. Old-school crowbar-and-ski mask crooks already know this. "If there's a motion detector and I ride my bike by innocently and set off their alarm, and do that every day for a month, they'll either turn the motion detector off or recalibrate it," said Steve Grobman, Intel Security CTO at Intel.

The next stage of evolution for AI is democratization. That means making it available to businesses of all sizes, and not just to companies like Microsoft, Google or Apple. The opportunity in front of us is using AI to transform how we operate our businesses, no matter the size or industry. Contemporary AI techniques have given us magic in areas like speech recognition and image labeling, but there is much more work to be done. Think of your business and where you or your team make decisions about resource allocation.