There are billions of people around the world whose online experience has been shaped by algorithms that utilize artificial intelligence (AI) and machine learning (ML). Some form of AI and ML is employed almost every time people go online, whether they are searching for content, watching a video, or shopping for a product. Not only do these technologies increase the efficiency and accuracy of consumption but, in the online ecosystem, service providers innovate upon and monetize behavioral data that is captured either directly from a user's device, a website visit or by third parties. Additionally, the collection and better utilization of data helps publishers generate revenue, minimize data risks and costs, and provide relevant consumer-preference-based audiences for brands. However, consumers and policymakers have been concerned in recent years about how AI algorithms and the biases they often contain might impact society.

Artificial intelligence (AI) has long been a feature of modern technology and is becoming increasingly common in workplace technologies. According to ManageEngine's recent 2021 Digital Readiness Survey, more than 86% of organisations in Australia and New Zealand reported increasing their use of AI even as recently as two years ago. But despite an increased uptake across organisations in the A/NZ region, only 25% said their confidence in the technology had significantly increased. One possible reason for the lack of overall confidence in AI is the potential for unethical biases to work their way into developing AI technologies. While it may be true that nobody sets out to build an unethical AI model, it may only take a few cases for disproportionate or accidental weighting to be applied to certain data types over others, creating unintentional biases.

But creating machine learning models is hard. You either have to employ data scientists, find a top AI consulting firm, or use automated machine learning. Machine learning (ML) is the process of getting a computer to learn to recognize patterns in data. There are three families of machine learning: supervised learning, unsupervised learning, and reinforcement learning. We discussed these in detail in another blog.

Artificial Intelligence is a hot topic and many organizations are now starting to exploit these technologies, at the same time there are many concerns around the impact this will have on society. Governance sets the framework within which organizations conduct their business in a way that manages risk and compliance as well as to ensure an ethical approach. AI has the potential to improve governance and reduce costs, but it also creates challenges that need to be governed. The concept of AI is not new, but cloud computing has provided the access to data and the computing power needed to turn it into a practical reality. However, while there are some legitimate concerns, the current state of AI is still a long way from the science fiction portrayal of a threat to humanity.

The gender pay gap has always been a topic of debate but never has it ever been able to bring the much-required change in the system. Time and again, feminists have raised their voices against such inequalities. Infact, they have been very right in stating that the women are sharing responsibilities equally then why not authority? Besides, many compensation guidelines and policies have also been articulated by the government but little did all of that benefit. Otherwise, the rate at which the global economy is embracing the removal of the gender pay gap can take the next 202 years to hit the equilibrium. In this blog, I will take you through the ways in which AI can be the most practical method to remove the gender pay gap.

John Mattison, MD, likes science fiction. In a speech at the AI in Healthcare Summit today in Boston, he brought up Niven's laws: Never fire a laser at a mirror. Giving up freedom for security is beginning to look naïve. It is easier to destroy than to create. The only universal message in science fiction: There exist minds that think as well as you do, but differently. All (except the first) played a part, but fourth in particular anchored his talk about the ethical challenges that artificial intelligence (AI) creates in healthcare.