While it's human to let our feelings influence our decisions, can we count on AI programs to be fair? As AI helps to automate tasks and use data to support human decision making, one form of it, deep learning, is helping computers think like humans. And, like humans, AI goes through a thought process to arrive at decisions – mostly driven by data. The question of the hour is: how do you make AI accountable for its decisions – and how do you know it's fair? As a recent Wall Street Journal article notes, that's the issue on the minds of major companies, such as Uber, Xerox and other companies that are looking into an area of AI called interpretability, and conducting research into how AI makes decisions.

Artificial intelligence is one of today's hottest buzzwords. As the technology becomes more integrated into our everyday lives, it will become as essential in casework. A basic understanding of AI and its various forms will prove helpful when building a fact-pattern and a strong legal case. Much of the hype around artificial intelligence (AI) has centered on one kind of technology: deep learning. But AI actually comes in a variety of forms, and many AI-based technologies tend to rely on more than one type.

MJ Insurance, headquartered in Indianapolis, has partnered with HealthJoy, a healthcare guidance platform that helps employees make informed healthcare decisions and provides a variety of cost containment strategies to help lower employer healthcare costs. HealthJoy advances current telemedicine offerings by providing an artificial intelligence virtual assistant along with a team of live doctors and healthcare professionals to help employees navigate the complexities of the healthcare system and decisions surrounding their employer-sponsored health plans. HealthJoy offers a direct line of communication between the employer and the user by connecting directly to the employee's smartphone device. HealthJoy integrates with any employer benefit plan and has shown a positive ROI for companies within 90 days. MJ Insurance is a commercial and personal insurance, risk management and employee benefits consulting agency that since 1964, has grown from a two-person start-up to an agency with more than 150 employees in both Phoenix and Indianapolis.

Microsoft is embarking on a five-year, $40m programme to explore how artificial intelligence (AI) can be used to bolster the response of non-governmental organisations (NGOs) to humanitarian disasters. The AI for Humanitarian Action programme will focus on using AI technologies to assist select NGOs throughout the world working on projects in four key areas. These include NGOs and humanitarian organisations involved in responding to natural disasters, child protection incidents, refugee crisis situations and human rights abuses, who Microsoft will support by offering grants and investments. "While global relief organisations scramble to respond to these events, their work by definition is often reactive and difficult to scale. We believe that technology, like artificial intelligence combined with cloud, can be a game changer, helping save more lives, alleviate suffering and restore human dignity by changing the way frontline relief organisations anticipate, predict and better target response efforts," said Microsoft president, Brad Smith, in a blog post announcing the initiative's launch.

Using data science in the banking industry is more than a trend, it has become a necessity to keep up with the competition. Banks have to realize that big data technologies can help them focus their resources efficiently, make smarter decisions, and improve performance. Here is a list of data science use cases in banking area which we have combined to give you an idea how can you work with your significant amounts of data and how to use it effectively. Machine learning is crucial for effective detection and prevention of fraud involving credit cards, accounting, insurance, and more. Proactive fraud detection in banking is essential for providingsecurity to customers and employees.

Shoplifting is a big issue and there are various ways you can prevent shoplifting. In this Video, I talk about Artificial Intelligence Applications, more specifically about: How can Artificial Intelligence END Shoplifting in the next 3-5 years? The AI application is simplified into 3 easy steps so you can understand the concepts. There are companies that are taking a leap in this and using technologies by ProductAI, Clarifai, AiFi etc. If you are a Retailer, Boutique Owner, etc. this is a MUST SEE for you because it could save you thousands of dollars.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule-making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Because the paragraph is written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime targets.

China is promoting artificial intelligence (AI) in an open environment and it seeks to work with other countries to develop the field, Chinese Vice-Premier Liu He told tech honchos in Shanghai on Monday (Sept 17). It supports Chinese firms and research institutes to cooperate widely with their foreign counterparts and also welcomes countries to invest in China to tap its vast market of 1.4 billion people. Mr Liu, who is also the Chinese point man for trade talks with the United States, said China wants to work with all countries "to cope with the double-edged sword brought by new technologies", especially in security, ethical and legal issues.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and as well as risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime target.

Artificial intelligence is transforming the traditional delivery of legal services. In general terms, the set of tools broadly called "legal analytics" promises to do two things: increase the efficiency of tasks that once required substantial time and human effort, and mine masses of data to discover new insights that were previously inaccessible. As legal scholars, we're excited about the promise of applying these tools to legal research questions. Students are involved too, so that we can educate the next generation of lawyers to leverage these tools in their own practices. Suppose that a company wants to forecast which employee complaints lead to lawsuits.