As machine learning algorithms are called upon to make more decisions for organizations, including talent decisions like recruiting and assessment, it’s becoming even more crucial to make sure that the performance of these algorithms is regularly monitored and reviewed just like the performance of an employee. While automation has been held up as a way to eliminate errors of human judgment from bias-prone processes like hiring, in reality, algorithms are only as good as the data from which they learn, and if that data contains biases, the algorithm will learn to emulate those biases.
The risk of algorithmic bias is a matter of pressing concern for organizations taking the leap into AI- and machine learning-enhanced HR processes. The most straightforward solution to algorithmic bias is to rigorously scrutinize the data you are feeding your algorithm and develop checks against biases that might arise based on past practices. Diversifying the teams that design and deploy these algorithms can help ensure that the organization is sensitive to the biases that might arise. As large technology companies make massive investments in these emerging technologies, they are also becoming aware of these challenges and looking for technological solutions to the problem as well. At Fast Company last week, Adele Peters took a look at Accenture’s new Fairness Tool, a program “designed to quickly identify and then help fix problems in algorithms”:
The tool uses statistical methods to identify when groups of people are treated unfairly by an algorithm–defining unfairness as predictive parity, meaning that the algorithm is equally likely to be correct or incorrect for each group. “In the past, we have found models that are highly accurate overall, but when you look at how that error breaks down over subgroups, you’ll see a huge difference between how correct the model is for, say, a white man versus a black woman,” [Rumman Chowdhury, Accenture’s global responsible AI lead,] says.
Google and the online learning platform Coursera are launching a five-course machine learning specialization to teach developers how to build machine learning models using the TensorFlow framework, Frederic Lardinois reports at TechCrunch:
The new specialization, called “Machine Learning with TensorFlow on Google Cloud Platform,” has students build real-world machine learning models. It takes them from setting up their environment to learning how to create and sanitize datasets to writing distributed models in TensorFlow, improving the accuracy of those models and tuning them to find the right parameters.
As Google’s Big Data and Machine Learning Tech Lead Lak Lakshmanan told me, his team heard that students and companies really liked the original machine learning course but wanted an option to dig deeper into the material. Students wanted to know not just how to build a basic model but also how to then use it in production in the cloud, for example, or how to build the data pipeline for it and figure out how to tune the parameters to get better results. …
It’s worth noting that these courses expect that you are already a somewhat competent programmer. While it has gotten much easier to start with machine learning thanks to new frameworks like TensorFlow, this is still an advanced skill.
The new series is a continuation of Google’s longstanding partnership with Coursera, through which the tech giant went public with its internal IT support training curriculum earlier this year.
Apple made a big move in the battle for top AI talent this week, hiring John Giannandrea away from Google, where he had until Monday been chief of search and artificial intelligence. Apple announced on Tuesday that Giannandrea would lead its machine learning and AI strategy, reporting directly to CEO Tim Cook, the New York Times reported:
Apple has made other high-profile hires in the field, including the Carnegie Mellon professor Russ Salakhutdinov. Mr. Salakhutdinov studied at the University of Toronto under Geoffrey Hinton, who helps oversee the Google Brain lab.
Apple has taken a strong stance on protecting the privacy of people who use its devices and online services, which could put it at a disadvantage when building services using neural networks. Researchers train these systems by pooling enormous amounts of digital data, sometimes from customer services. Apple, however, has said it is developing methods that would allow it to train these algorithms without compromising privacy.
Cook stressed Apple’s commitment to charting a privacy-conscious course on AI development in his statement on Tuesday, saying Giannandrea “shares our commitment to privacy and our thoughtful approach as we make computers even smarter and more personal.” While safeguarding users’ privacy may pose a significant technical challenge in AI and machine learning, that commitment could have an upside from a marketing perspective at a time when tech companies are facing heightened scrutiny and criticism of their data privacy practices.
Google Hire, the search giant’s recruiting and applicant tracking application, has been updated with a new feature called candidate discovery that is designed to help hiring managers more easily keep track of past candidates who might be good fits for newly open positions, Google announced on its blog last Wednesday. According to the company, the new feature enables managers to:
- Find qualified candidates immediately upon opening a job. The first step in filling a role should be checking who you already know that fits the job criteria. Candidate discovery creates a prioritized list of past candidates based on how their profile matches to the title, job description and location.
- Use a search capability that understands what they are looking for. Candidate discovery understands the intent of what recruiters and hiring managers are looking for. It takes a search phrase like “sales manager Bay Area” and immediately understands the skills and experiences relevant to that job title, as well as which cities are part of the Bay Area. That means the search results will include candidates with sales management skills even if their past job titles are not an exact keyword match.
- Easily search by previous interactions with candidates. Hire lets recruiters search and filter based on the previous interactions with the candidate, such as the type of interview feedback they received or whether you extended them an offer before. Candidates with positive feedback will rank higher in search results than those without, and candidates who received an offer in the past but declined it will rank higher than those who were previously rejected.
The feature is now available in beta to all Google Hire users, a pool currently limited to small and mid-sized US employers using its G Suite of enterprise software products. Matt Charney took a more detailed technical look at the product for Recruiting Daily, noting that “traditional search engines are notoriously bad at searching for individual people and profiles,” which may be why it’s taken Google so long to expand into this space. Now that it has, however, it’s a pretty big deal:
Last November, Amazon announced that it was bringing its voice-controlled assistant Alexa into the workplace, launching Alexa for Business at its its annual AWS re:Invent conference. This week, the company revealed how far the enterprise version of Alexa has come, who is using it, and how the product is being applied in business settings. Amazon Chief Technology Officer Werner Vogels expanded on these points in a post on his blog, All Things Distributed:
Voice interfaces are a paradigm shift, and we’ve worked to remove the heavy lifting associated with integrating Alexa voice capabilities into more devices. For example, Alexa Voice Service (AVS), a cloud-based service that provides APIs to interface with Alexa, enables products built using AVS to have access to Alexa capabilities and skills.
We’re also making it easy to build skills for the things you want to do. This is where the Alexa Skills Kit and the Alexa Skills Store can help both companies and developers. Some organizations may want to control who has access to the skills that they build. In those cases, Alexa for Business allows people to create a private skill that can only be accessed by employees in your organization. In just a few months, our customers have built hundreds of private skills that help voice-enabled employees do everything from getting internal news briefings to asking what time their help desk closes.
Alexa for Business is now capable of interfacing with common enterprise applications like Salesforce, Concur, and ServiceNow, Vogels added, while IT developers can use the Alexa Skills Kit to enable custom apps as well. WeWork, one early adopter of the service, has “built private skills for Alexa that employees can use to reserve conference rooms, file help tickets for their community management team, and get important information on the status of meeting rooms.”
Last year, the Montreal-based startup Element AI estimated that there were fewer than 10,000 people worldwide with the necessary skills to design artificial intelligence/machine learning systems, but the Chinese internet conglomerate Tencent Holdings later estimated the total number of AI researchers and practitioners at between 200,000 and 300,000 people.
Element AI came out with a new estimate on Wednesday, Jeremy Kahn reports at Bloomberg, putting the number of AI specialists with recently-earned PhDs at 22,000, of whom 3,000 are looking for work. With less restrictive parameters, however, the total number of AI experts could be four times greater:
Element AI said it scoured LinkedIn for people who earned PhDs since 2015 and whose profiles also mentioned technical terms such as deep learning, artificial neural networks, computer vision, natural language processing or robotics. In addition, to make the cut, people needed coding skills in programming languages such as Python, TensorFlow or Theano.
Gartner is projecting worldwide IT spending to reach $3.7 trillion this year, a 4.5 percent increase from 2017, with enterprise software expected to be the fastest-growing component of IT spend, growing by 9.5 percent from $355 billion last year to $389 billion in 2018. HR technologies are among the leading drivers of innovation in this space, with significant spending forecast on software-as-a-service solutions in financial management systems (FMS), human capital management (HCM), and analytic applications. Big data, algorithms, machine learning, and AI are among the technologies expected to drive growth in IT investments in the coming years.
(For readers who want to hear more about our IT spending forecast, Gartner analysts discuss these findings in detail in a complimentary webinar, available on demand here.)
For talent management leaders, this information carries significant implications. In the coming years, technology will inevitably be more embedded into the HR function: The only choice for leaders is whether they want to be on the front or back end of the adoption curve. Technology in the HR realm is advancing at a rapid rate, but the function seems consistently hesitant to take advantage of the opportunities and efficiencies it offers. A wide range of tools are newly available or in development that can help solve perennial HR challenges such as candidate vetting, employee wellness, space management, analytics strategy, recruiting and retaining diverse employees, understanding drivers of high performance, making learning more accessible, or offering digital assistants for all employees.