With companies continuing to shrink or outsource their human resources departments, it is tempting to augment that traditional business function with artificial intelligence. Data science holds so much promise for other fields that it makes sense for algorithms to replace imperfect human decision-making for hiring, firing, scheduling and promoting. But new research from Wharton professors Peter Cappelli and Prasanna "Sonny" Tambe flashes a cautionary yellow light on using AI in human resources. In their paper, "Artificial Intelligence in Human Resources Management: Challenges and a Path Forward," the professors show how limited data, the complexity of HR tasks, fairness and accountability pose problems for digital HR. The study, which was co-authored by Valery Yakubovich, professor at ESSEC Business School and senior fellow at the Wharton Center for Human Resources, also looks at how to remedy those problems. Cappelli and Tambe spoke about their research with Knowledge@Wharton. An edited transcript of the conversation follows. Knowledge@Wharton: You make the point that while AI is invading many different industries and sectors, there are some special concerns when it comes to using AI in human resources. Can you talk about what some of those challenges are?

Compared to constituency parsing and semantic role labeling, dependency parsing provides more clear relationships between predicates and arguments (Johansson and Nugues, 2008). Constituency parsers provide information about noun phrases in a sentence, but provide only limited information about relationships within a noun phrase. For example, in the sentence "What do you think about Google's privacy policy being reviewed by journalists from CNN?," a constituency parser would place "Google's privacy policy being reviewed by journalists from CNN" under a single phrasal node. Similarly, a semantic role labeling system would tend to label the same phrase as an argument of the verb, but it would not disambiguate the relationships within the phrase. Finally, NER only provides information about named entities which may or may not be the key semantic content of the sentence. Dependency parsers, by contrast, can provide information about relationships when a sentence contains multiple entities, even when those entities are within the same phrase. Identifying relationships between entities in a user utterance can help a dialog system formulate a more appropriate response. For instance, in the sentence about "Google's privacy policy" mentioned above, there are multiple entities for the system to consider. The system must determine the most important entity in the utterance in order to model the topic and generate an appropriate response.

This week I'm joined by Jason "JJ" James who is the CIO for Optima Healthcare Solutions. JJ is also the author of the upcoming book "Make IT work: Practical IT guide to mergers and acquisitions." In our conversation, JJ outlines how the CIO is similar to an anthropologist and must be a study of human tools, culture and development. We also discuss JJ's perspective on how innovative technology is impacting the healthcare industry. Closing out our conversation, JJ talks about why hype is valuable and the role the CIO plays in the changing landscape. Tim Crawford: Hello, and welcome to the CIO In The Know podcast, where I take a provocative, but pragmatic look at the intersection between business and technology. Tim Crawford: This week I'm joined by Jason JJ James, who is the CIO for Optima Healthcare Solutions. JJ is also the author of the upcoming book, Make it Work: Practical IT Guide to Mergers and Acquisitions. In our conversation, JJ outlines how the CIO is similar to an anthropologist, and must be a study of human tools, culture, and development. We also discuss JJ's perspective, on how innovative technology is impacting the healthcare industry. Closing out the conversation, JJ talks about why hype is valuable, and the role the CIO plays in the changing landscape. So, when you think of healthcare in the space we operate in, which is post-acute care, which is everything outside of the hospital, there's so much happening in that space. I think about my own mother who is in assisted living.

John Chambers, founder and chief executive officer of JC2 Ventures LLC, stands for a photograph following a Bloomberg Technology Television interview in San Francisco, California, U.S. John Chambers, who just turned 70, prefers to take any stage at a run. In his tech trendy uniform--blue blazer, designer jeans, checked shirt, and blue boat shoes--he still exudes the physical energy of his successful tenure as CEO at Cisco Systems, which he grew from an annual revenue of $2.2 billion in 1995 to $49 billion in 2015 when he stepped down. Today Chambers runs his own boutique investment firm, JC2 Partners, in Palo Alto, California. He prefers to call himself a mentor to startup CEOs rather than a venture capitalist. When he's not in Silicon Valley he can often be found in India, where he advises Prime Minister Narendra Modi's government on digital transformation and the economy.

Artificial Intelligence and related fields like deep learning and machine learning have come to dominate discussions about where our society is headed, with debate often polarised between declaring AI to be the savior of society or its potential downfall. The truth lies somewhere in the middle: AI won't destroy our civilization, but we can't cede responsibility for saving it to AI, either. In any case, AI-related technologies are versatile and powerful and can provide the basis for change in our world. We caught up with some leading experts in the field to ask them for their view on the future of AI. Prof. Barry O'Sullivan is Director of the Insight Centre for Data Analytics, Computer Science, at University College Cork, Ireland.

There is currently a lot of talk about automated machine learning. There is also a high level of skepticism. I am here with data scientists Paolo Tamagnini, Simon Schmid and Christian Dietz, to ask a few questions on this topic from their point of view and I found this concept of guided automation quite interesting as well, since it is directly involved in the practice of automated machine learning. Rosaria Silipo: What is automated machine learning? Christian Dietz: Automated machine learning is about building a system, process or application able to automatically create, train and test machine learning models with as little human input as possible.

I was 13 years old when the movie Alien was released. It scared me into a month-long spell of anxiety. The hair on the back of my neck was perpetually up and I had the jittery demeanor of a combat veteran. While the full-grown xenomorph alien was chilling, the larval stage face-hugger was terrifying. Not only did it penetrate the human host's throat, planting the chest-burster in the gut, but it was intrinsically grotesque, an odious, zoological mash-up of scurrying spider and slithering snake. It's easy to interpret our fears of alien predators as nothing more than superficial horror ginned up by the Hollywood fright machine.

This week I'm joined by Yousuf Khan, CIO and VP of Customer Success at Moveworks. Yousuf has served as CIO for multiple companies and shares his perspective on the changing CIO role. CIOs are finding themselves moving beyond operational functions and taking on customer-facing responsibilities. In this episode, we discuss the importance customer experience plays for enterprises and the pillars that enable the CIO to play a significant role. Yousuf outlines three keys to building relationships and the importance that community plays in CIO decision making. Tim Crawford: Hello, and welcome to the CIO In The Know podcast, where I take a provocative but pragmatic look at the intersection between business and technology. Tim Crawford: This week I'm joined by Yousuf Khan, CIO and VP of Customer Success at Moveworks. Yousuf has served as CIO for multiple companies and shares his perspective on the changing CIO role. CIOs are finding themselves moving beyond operational functions and taking on customer-facing responsibilities. In this episode, we discuss the importance customer experience plays for enterprises and the pillars that enable the CIO to play a significant role.

I have a problem when I see the word healthcare next to industry. It seems like we're talking about a basic human right next to a word that means a sellable product. This industry relies heavily on human intervention and subjective opinions. It uses advanced technologies like genome decoding, MRIs, PET scans, and radiotherapy., But it also strongly depends on human interpretations, and humans make mistakes.

It was a rambling discussion in Shanghai last week that brought Artificial Intelligence into focus once again. In this case, it was the clearly jet-lagged Elon Musk, a technology legend and disruptor, and a very composed Jack Ma, the largest purveyor of goods over the internet. While the discussion rambled all over, from Pong to The Matrix to Mars, it was Musk shooting down Ma's optimism by saying that these could be "famous last words", that caught my attention. Musk has been consistently pessimistic about AI. In another famous joust, this time with Facebook's Mark Zuckerberg, he tweeted that "his (Zuckerberg's) understanding of the subject is pretty limited".