Jay Kiew is a management consultant and MBA 2018 from the Ivey Business School.
Canada, we have a problem.
Our companies are struggling to retain their employees. According to a 2017 study by LinkedIn, Canada has the fourth-highest employee-turnover rate globally at 16 per cent (compared with the global average turnover rate of 11 per cent).
As companies struggle to predict how likely their employees are to leave, organizations are turning to data at the source: employees themselves. While organizations are legally allowed to monitor employees’ internet, e-mail and instant-messaging content, the extent of monitoring is now elevated with sentiment analysis using artificial intelligence.
With details of the EU’s General Data Protection Regulation (GDPR) bombarding news feeds globally about privacy and data regulation, a multitude of questions arise. How much data should companies be able to capture about their employees? To what extent are employees aware of the monitoring?
Broadly speaking, how far should companies go to retain their employees and where do ethics in AI fit in?
Accessing employee insights through sentiment analysis
One of the predominant workplace communication tools is Slack, whose userbase has grown to 70,000 paying organizations and eight million daily active users, competing directly with Microsoft’s Skype for Business and Facebook’s Workplace.
Enter Vibe, a Slack plug-in that brands itself as a morale meter for teams. Vibe applies natural-language processing (NLP) algorithms to scan messages across individuals, teams and the company. It identifies five key emotions (happiness, irritation, disapproval, disappointment and stress) to generalize the mood in the workplace. By analyzing employee sentiment on public channels through keywords and emojis, Vibe can help companies identify employees who may be flight risks.
Vibe isn’t the only one though. Emotion-detection company Affectiva offers Emotion as a Service, which analyzes images, video and audio to provide facial and vocal emotion metrics. Veriato tracks workplace productivity and insider threats by monitoring internet and e-mail content.
With a wide range of sentiment analysis tools available, organizations can keep track of employee stress levels - and perhaps, by extension, their job satisfaction - through their use of positive or negative words.
If managers can track their employees a bit better, perhaps they can make a concerted effort to reach out and check in when necessary. After all, understanding employees better is the first step to improving employee retention.
It is important to note that sentiment analysis is not new. Personal journal reflection sites such as 750 Words, created in 2009, provide NLP insights for users who write daily as an introspection tool. The website’s software examines keywords and phrases to identify a writer’s mindset, feelings and concerns.
The insights from 750 Words are extremely valuable for users to take an outside view of their own thoughts. The application of sentiment analysis to user data is intentionally transparent; users seek to better understand themselves.
However, the difference between 750 Words, Vibe and Veriato lies in the privacy and intended use of the data they obtain. With applications such as Vibe and Veriato, users may not fully understand that their messages are being collected and analyzed – and employers recognize that there are risks to that.
In Deloitte’s Global Human Capital Trends for 2018, six out of ten respondents said “they were concerned with employee perceptions of how their data is being used.” If these new technologies are used recklessly, consequences such as backlash over data abuse or privacy concerns could take a PR spotlight.
Ethics in work force analytics
There is no denying that employers must better understand their employees in order to better support their work lives. However, when applying artificial intelligence to employee monitoring, work force analytics programmers should take three things into account: data collection and analysis; data use; and data security.
Data Collection and Analysis: AI programs are highly dependent on the type of data that is collected, how that data is cleaned and how algorithms are coded. That means the results and capabilities of any AI program are reliant on the programmers that code it. When using third-party software, companies should be conscious of possible biases that may have gone into a software’s development and place controls on the software’s scope of data collection and analysis. As standard practice, companies should ask for employee permission to collect and analyze data.
Data Use: Discussing IBM’s use of data last year, IBM chief executive Ginny Rommetti promised transparency on when, where and how AI insights would be used. Transparency for employees is a great start to broader corporate policies ensuring proper use of data. Employees should be aware of what information is being tracked and how data is specifically being used to engage or retain them.
Data Security: In light of the recent Facebook-Cambridge Analytica data scandal, companies need to own accountability for securing personal data. Although that scandal surrounded the leak of user data, storing employee data is equally important. If third-parties are analyzing company data, companies must take additional measures to ensure sensitive personal data is not leaked.
The use of AI in work force analytics should not be discounted completely. However, ethics should play a key role in the development and use of AI in work force analytics, and companies should be held accountable for its implementation.
In the conversation on employee retention, AI-enabled work force analytics may be more of a distraction than a benefit.
In fact, organizations may already have the answer: A 2015 LinkedIn survey notes that 45 per cent of employees globally left jobs because they were “concerned about the lack of opportunities for advancement.”
When it comes to retention, perhaps companies don’t need AI after all. They just need to pay closer attention to what their employees are already saying.
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