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When applying for a new job, candidates may well find that the use of artificial intelligence (AI) tools is involved at some point in the recruitment process. New recruitment businesses and technology are entering the market, setting up entirely automated initial conversations with candidates to help them find the right vacancy for their skill set, saving time for applicant and recruiter alike.

CV screening is also becoming more prevalent, with AI screening and tracking tools being used to quickly analyse CVs to ascertain whether the individual has the qualifications and experience necessary for the role for example, burger chain Five Guys is said to be utilising such technology.

Unilever recently hit the headlines when it announced that, instead of human recruiters, it uses an AI system to analyse video interviews. Candidates record interviews on their phone or laptop, and the system scans candidates language, tone and facial expressions from the videos, assessing their performance against traits that are considered to indicate job success at Unilever.

But it is not just the recruitment stage where AI and people analytics are being used by businesses performance management is another targeted area. Amazon is leading this charge the company was issued with two patents in the US for a wristband for tracking the performance of workers in their warehouse, which would mean that staff receive a little buzz if they place a product near or in the wrong inventory location.

It is also alleged that Amazon uses a computer system to automatically generate warnings or terminations to employees, when their productivity (or lack of) warrants it.

The benefits of such technology for employers are countless and clear, including costs savings, efficiency, and the purported removal of human unconscious bias and prejudice. However, the use of AI in the workplace has come under scrutiny and has posed serious ethical and legal questions, including whether AI itself could in fact be biased.

Another important aspect when implementing AI in the workplace is its relationship with data protection laws such as the EUs General Data Protection Regulation (GDPR). So, what data protection considerations should an employer make when considering the introduction of AI technology?

The use of AI for processing personal data will usually meet the legal requirement for completing a DPIA.

A DPIA enables the business to analyse how the AI plans will affect individuals privacy, and ensures the company can assess the necessity and proportionality of its technology.

As the UK Information Commissioners guidance confirms, the deployment of an AI system to process personal data needs to be driven by the proven ability of that system to fulfil a specific and legitimate purpose, not just by the availability of the technology.

The DPIA should demonstrate that the applicable purposes the AI is being used for could not be accomplished in another reasonable way. In doing so, organisations need to think about and document any detriment to data subjects that could follow from bias or inaccuracy in the algorithms and data sets being used.

A business cannot simply process personal data because it wishes to do so data can only be processed where one of the legitimate grounds or conditions of processing has been met. There are various bases, including performance of a contract, compliance with a legal obligation, consent and legitimate business interests. For the processing of sensitive personal data (such as health data), the bases are even more limited.

Before using AI or people analytics in the workplace, employers will first need to consider what data is being processed by such activity and second what legal basis can be relied upon in processing the data in that way. If they do not have a legal basis, the data cannot be processed.

One of the key principles of GDPR is transparency, requiring businesses to provide individuals with mandatory information about the processing of their personal data, including the reason why it is being processed, the legal basis, who it will be shared with and how long it will be retained. Employers will need to update their privacy notices to ensure anyone subject to the AI technology is made aware of its use.

The privacy notice needs to be concise and intelligible, using clear and plain language this will be particularly difficult when including a complex AI system, as businesses will need to provide a meaningful explanation of the technology to meet the transparency principle of GDPR. Opaque or complex descriptions of the tech may result in contention or pushback from the employees and candidates affected.

GDPR prohibits instances of computer says no and contains the right for data subjects not to be subjected to a decision based solely on automated processing, which has a legal or similarly significant impact on them. Its aim is to protect individuals against the risk that a potentially damaging decision is taken without human intervention, and will therefore likely capture a recruitment result made without any human input.

There are specific exceptions when automated decision-making is permitted, including where explicit consent was given, contractual necessity, or where authorised by law. Where such an exception is being relied upon, such as with the consent of a candidate, the business must still implement further safeguarding measures, including permitting the individual to request human intervention or to contest the decision.

Employers will need to ensure that their automated technology is being lawfully used, before relying on its output.

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Dont forget to consider GDPR when using artificial intelligence in the workplace - ComputerWeekly.com

CARY The ongoing spread of the coronavirus is sparking urgent searches for a vaccine to counteract it. Several Triangle firms such as Lenovo and Heat Biologics have joined the fight. Also bringing powerful and emerging technology to the battle is software and data analytics giantSAS, a heavy investor in artificial intelligence and a world leader of tools to extract information from so-called big data information gathered from many sources.

In an exclusive Q&A with WRAL TechWire,Theresa Do, Support Manager for Federal Healthcare at SAS, talks about the potential uses of AI, machine learning and analytics to combat the corornavirus and future health threats. Do also is a Professor ofEpidemiology & Biostatistics at George Washington Univ. in Washington, DC.

Developing new treatments and creating vaccines and antiviral medications for newly discovered viruses is a difficult and time-consuming process, traditionally involving lots of trial and error. AI and advanced analytics can help improve the application of current treatments and speed up the development of new ones.

Theresa Do

For example, AI specifically deep learning is currently being used to help radiologists make better treatment decisions based on medical imaging. Chest x-rays of patients infected with the new coronavirus may serve as input into AI models that can help physicians make faster diagnoses as the outbreak continues. AI can also help here by examining data from similar viral diseases and using that data to predict what types of vaccines and medicines might be more effective.

Data and analytics are the lifeblood for decision-making during infectious-disease outbreaks.Analytics can provide insights about the spread of a disease and the effectiveness of public health action, which can improve the response.The more information people have about case counts, mortality rates, how a disease spreads and how contagious it is, the better decisions they can make to limit, prevent and treat the disease. Public health and scientific data must be shared freely and rapidly with stakeholders and key decision makers so they can take action.

For decades, SAS has provided analytics software to public health and government agencies in the United States and around the world, helping them improve the health and well-being of their citizens. Governments hold much of the critical data needed to understand current conditions during an outbreak, but analytics companies like SAS offer an ability to synthesize this data with other non-government data and specialized tools to get the most insights from this unified data. These data-driven insights support better, faster government and public health decision-making. Events like the ongoing COVID-19 outbreak require public and private sectors to work closely together to limit disease spread and save lives.

Collaboration, integration and rapid information sharing are essentialto improve response and recovery for infectious disease outbreaks.Gone are the days when only governments and public health organizations had valuable data to fight epidemics. Disparate, non-traditional data setscan serve as sentinel sources everything from travel and census data, to demographic information and animal migration patternscan be applied to the public health threats. But the key is how to take advantage of all this data and emerging new data like genetic sequences.

Advanced analytics and AI (particularly machine learning) are essential tools to put data to work and save lives. With more and diverse data sets, the challenge is to synthesize everything to derive the insights needed to make decisions. Asolid data management ecosystem and platform where the data can be stored, cleaned, scaled and shared among key stakeholders and decision makers is essential. So, its not just about the data, but also how that data can be used effectively in global collaboration to fight the emergence and spread of disease.

Finally, having enough good data is a challenge when a new, or novel virus is causing a disease outbreak. Advanced analytics are only as good as the data they can explore, analyze and sift through. For COVID-19, collaboration will continue to improve as more data is shared.

Analytics has an important and growing role to play in the detection and monitoring of all viral-disease outbreaks. Critical insights about disease spread and the effectiveness of public health action can be derived from analytical approaches, which helps decision-makers adjust and adapt their strategies and responses.

AI and machine learning in particular are valuable tools for healthcare professionals and policymakers to reduce or better manage the impact of emerging infectious diseases like COVID-19. Machine learning is designed to consider large amounts of data, find patterns in that data and detect anomalies, and in many cases offer predictions.

AI can help health authorities better detect infectious disease outbreaks by analyzing sentinel data sources for early warning of potential threats.AI can be applied to models on common themes or topics to help identify common symptoms among new and evolving public health threats. Moreover, AI can help to automate data analysis, identify patterns and build models of risk factors to help in scenario analysis of transmission. And when it comes to identifying paths of transmission, AI can aid in the search for a host and/or index case, as well as tracking possible contacts.

When SARS emerged, there were fewer data sources that could be leveraged, such as social media, Internet of Things (IoT) devices and technologies to help with diagnostics. Phone apps for tracking of health data and diagnostics were not yet present. (The iPhone came out in 2007, four years after SARS broke out). With the advent of the iPhone and new types of apps and technologies, scientists can leverage a lot more data for analysis in addition to the available sentinel sources.

Today there are more ways that people can communicate to quickly distribute public health prevention efforts and quell misinformation regarding public health threats such as the some of the misinformation around COVID-19. Providers have more information at their fingertips via technology versus when SARS first broke out during the early stages, as well as the availability of the internet. However, with COVID-19, there is still a lot more to be uncovered and learned.

For any infectious disease outbreak, good responses need good data that can be shared readily and acted upon quickly. That was true then for SARS, and its true today for COVID-19.

Lenovo, Intel team up to accelerate analysis of coronavirus genome in vaccine search

Morrisville-based Heat Biologics joins global effort to discover coronavirus vaccine

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High tech vs. coronavirus: Using artificial intelligence, analytics are weapons SAS brings to the fight - WRAL Tech Wire

The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams.

The Joint Artificial Intelligence Center will take a Series A, B, approach to building tech for customers, with product managers and mission teams. By PatrickTucker

Military leaders who long to copy the way Silicon Valley funds projects should know: the Valley isnt the hit machine people think it is, says Nand Mulchandani, chief technical officer of the Pentagons Joint Artificial Intelligence Center. The key is to follow the right venture capitalmodel.

Mulchandani, a veteran of several successful startups, aims to ensure JAICs investments in AI software and tools actually work out. So he is bringing a very specific venture-capital approach to thePentagon.

Heres the plan: when a DoD agency or military branch asks JAIC for help with some mission or activity, the Center will assign a mission team of, essentially, customer representatives to figure out what agency data might be relevant to theproblem.

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Next, the JAIC will assign a product manager not DoDs customary program manager, but a role imported from the techindustry.

He or she handles the actual building of the product, not the administrative logistics of running a program. The product manager will gather customer needs, make those into product features, work with the program manager, ask, What does the product do? How is it priced? Mulchandani told Defense One in a phone conversation onThursday.

The mission team and product manager will take a small part of the agencys data to the software vendors or programs that they hire to solve the problem. These vendors will need to prove their solution works before scaling up to take on all availabledata.

Were going to have a Series A, a seed amount of money. You [the vendor] get a half a million bucks to curate the data, which tends to be the problem. Do the problem x in a very tiny way, taking sample data, seeing if an algorithm applies to it, and then scale it, Mulchandani saidon Wednesday at an event hosted by the Intelligence and National Security Alliance, orINSA.

In the venture capital industry, you take a large project, identify core risk factors, like team risk, customer risk, etc. you fund enough to take care of these risks and see if you can overcome the risks through a prototype or simulation, before you try to scale, he addedlater.

The customer must also plan to turn the product into a program of record or give it some other life outside of theJAIC.

Thats very different from the way the Defense Department pays for tech today, he said. The unit of currency in the DoD seems to be Well, this was a great idea; lets stick a couple million bucks on it, see what happens. Were not doing that way anymore he said onWednesday.

The JAIC is working with the General Services Administration Centers of Excellence to create product manager roles in DoD and to figure out how to scale small solutions up. Recently, some members of the JAIC and the Centers of Excellence participated in a series of human-centered design workshops to determine essential roles and responsibilities for managing data assets, across areas that the JAIC will be developing products, like cybersecurity, healthcare, predictive maintenance, and business automation, according to thestatement.

Mulchandani urges the Pentagon not to make a fetish of Silicon Valley. Without the right business and funding processes, many venture startups fail just as badly as poorly thought out government projects. You just dont hear aboutthem.

When you end up in a situation where theres too much capital chasing too few good ideas that are real, you end up in a situation where you are funding a lot of junk. What ends up happening [in Silicon Valley] is many of those companies just fail, he said Wednesday. The problem in DOD is similar. How do you apply discipline up front, on a venture model, to fund the good stuff as opposed to funding a lot of junk and then seeing two or three products that becomesuccessful?

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The Pentagon's AI Shop Takes A Venture Capital Approach to Funding Tech - Defense One