AI can play many roles in the technology stack of a modern enterprise. Its performance as a neutral, data-based, analytical advisor could allow businesses to use algorithms to predict whether a decision is the right one. AI-based decisions are part of an arsenal of tools leveraged by technology high performers. Businesses led by digitally savvy leaders, those who champion emerging technologies such as AI, outperform other like-sized businesses by 48% on valuation and revenue growth, according to one MIT research study. "The integration of traditional decisioning into AI is really just starting to hit its stride right now," said Rowan Curran, analyst at Forrester.

Written by Zest AI CTO Jay Budzik. Zest's ZAML software uses machine learning technology to help lenders make more effective credit decisions safely, fairly and transparently. Founded by Google CIO Douglas Merrill and backed by Matrix Partners, Lightspeed, Upfront, Flybridge and Baidu, Zest works with finance companies worldwide to help more people access fair and transparent credit. It's been a year since MMC Ventures printed the accidental finding that 40 percent of AI startups had no material use of AI in their tech stack. As an AI company CTO, I can tell you the buzz can be deafening.

These concerns have been present whenever we make important decisions. What's new is the much, much larger scale at which we now rely on algorithms to help us decide. Human errors that may have once been idiosyncratic may now become systematic. "Artificial intelligence is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals," wrote Harvard University Professor David Parkes in "A Responsibility to Judge Carefully in the Era of Decision Machines," an essay recently published as part of Harvard's Digital Initiative. "Machines need to be able to predict to decide, but decision making requires much more," he wrote.

When organizations use algorithms to make decisions, biases built into the underlying data create not just challenges but also engender enormous risk. What should companies do to manage such risks? The way forward is to conduct artificial intelligence (AI) audits, according to this opinion piece by Kartik Hosanagar, a Wharton professor of operations, information and decisions who studies technology and the digital economy. This column is based on ideas from his book, A Human's Guide to Machine Intelligence. Much has been written about challenges associated with AI-based decisions. Some documented failures include gender and race biases in recruiting and credit approval software; chatbots that turned racist and driverless cars that fail to recognize stop signs due to adversarial attacks; inaccuracies in predictive models for public health surveillance; and diminished trust because of the difficulty we have interpreting certain machine learning models.

Ethical AI ensures more socially conscious approaches to customer and employee interactions, and in the long run, may be the ultimate competitive differentiatior as well, a recent survey suggests. Three in five consumers who perceive their AI interactions to be ethical place higher trust in the company, spread positive word of mouth, and are more loyal. More than half of consumers participating in a recent survey say they would purchase more from a company whose AI interactions are deemed ethical. Leaders finally sit up and take notice of AI ethics. As organizations progress to harness the benefits of AI, consumers, employees and citizens are watching closely and are ready to reward or punish behavior.

Smart board level questions to ask about AI. Artificial intelligence, or "AI," raises legal and ethical issues beyond those generally found in investments in technology. Due to the rapid growth in this area, the lack of standards for evaluation and oversight and the risks associated with AI use, AI projects would particularly benefit from board inquiry and oversight. Board members should ask the following questions as their company evaluates its use of AI. Will AI be replacing human judgment?

CA Technologies announced its participation in scientific research to discover how Internet of Things (IoT) applications can use a type of AI known as'deep learning' to imitate human decisions. The research will also explore how to prevent that AI-based decisions are not producing biased results. This three-year research project is named ALOHA (adaptive and secure deep learning on heterogeneous architectures). It is funded by the European Union as part of the Horizon 2020 research and innovation programme, and coordinated by the University of Cagliari in Italy. "The future of all technologies will include AI and deep learning in some way," said Otto Berkes, CTO, CA Technologies.

CA Technologies announced its participation in scientific research to discover how Internet of Things (IoT) applications can use a type of AI known as'deep learning' to imitate human decisions. The research will also explore how to prevent that AI-based decisions are not producing biased results. This three-year research project is named ALOHA (adaptive and secure deep learning on heterogeneous architectures). "The future of all technologies will include AI and deep learning in some way," said Otto Berkes, CTO, CA Technologies. "The expansion of complex, multi-layered IoT systems bring both security and software development challenges that AI and autonomous computing are uniquely positioned to address," he added.

The possibilities of artificial intelligence are endless. AI helps businesses create tremendous efficiencies through automation, while enhancing an organizations ability to make more effective business decisions. However, it's no surprise that companies are beginning to be held accountable for the outcomes of their AI-based decisions. From the proliferation of fake news to most recently, the deliberate creation of the AI psychopath Norman, we're beginning to understand and experience the potential negative outcomes of AI. While AI, machine learning, and deep learning have been deemed to be'black box' technologies, unable to provide any information or explanation of its actions, this inability to explain AI will no longer be acceptable to consumers, regulators, and other stakeholders.