The technical implications of artificial intelligence have become a big part of workforce conversations in recent years. However, the ethical implications surrounding AI and data management have created a larger discussion.
Let’s take a look at what data ethics mean, how AI impacts data management and privacy, as well as how this impacts employee experience, engagement, and trust within the workplace.
What are data ethics and why do they matter?
According to McKinsey, data ethics are a set of principles that strike the balance between user convenience and privacy, ensuring data is used only for intended purposes. In an era where individuals and companies produce more data now than ever (a projected 463 exabytes each day, compared to 3 exabytes in 2015), digital privacy is crucial to maintain trust. Without this trust, companies are destined to burn bridges with both consumers and employees, risking business reputation and overall success.
Poor application of data ethics can also impact data-driven decision making. If your data sources are inaccurate or improperly handled, decisions based off this information can be rendered unfair or ineffective. Shifting towards a work culture that values data ethics and putting key principles into practice gives businesses an advantage in a competitive landscape.
The ethical implications of data use and management
From product purchases to customer service interactions, today, nearly every business interaction generates consumer data. Consumer data from these interactions must be properly handled, consensually provided, and transparently utilized.
Data ethics address a variety of issues, including but not limited to:
- Privacy and security: With vast amounts of data being collected, user privacy and security are top concerns. Data ethics ensure that risk is assessed and minimized, providing safe solutions for collection use.
- Transparency: Informed consent is a cornerstone of transparent data collection. Users should know when data is being collected, how it’s being processed, and when breaches occur.
- Fairness: Data sources aren’t faultless and require routine assessment to ensure data-driven decisions are based on fair, equal algorithms. Principles of ethical data management include having thorough oversight to address bias.
Ethical data management can shape the employee experience
Ethical data management should be part of business culture and framework, ensuring proper practices trickle down from leadership to employees. Data ethics practices aren’t reserved for data scientists or analysts in your organization, frontline employees should be versed in proper data management too. This helps frontline employes understand the layered risks in improper data management, shaping customer interactions and service quality.
Promoting comprehensive data ethics training from day one is an initial step in shaping employee experiences and relationships with company values. As employees collect data through methods like customer service calls and chats, user privacy and security should be prioritized. In addition to ongoing training, data ethics can be emphasized through workforce software that frontline employees utilize including quality management and performance management tools.
Utilizing “data for good” isn’t limited to employee interactions with customers. Ethical data collection should also be promoted to build trust between employees and their workplace, with leadership properly ensuring employee digital trust is developed. Data sources like workforce management software can provide managers with a comprehensive look at employee data, including performance metrics and activity monitoring, but also personally identifiable information (PII). The collection and use of this employee data should be transparent and utilized to inform choices that benefit employees. This can include:
- Tailored engagement strategies
- Skill building and career path planning
- Diversity and inclusion (DEI) initiatives
The role of ethical AI in the employee experience
In a recent report, Gartner® notes, “Improving productivity has always come with ethical considerations. AI is a technology that amplifies those considerations, giving the ethical discussion new urgency.”¹
As automation and AI start to play larger roles in workforces, businesses must consider ethical implications of utilizing these tools to manage the employee experience. Privacy concerns and ethical considerations should be taken during automation, especially when developing hiring processes and employee engagement plans.
Streamlining recruitment and hiring
AI can be a useful tool for recruiters and HR professionals to streamline a hiring process. Automated technology makes it quicker to sift through applications that fit specific guidelines, ideally giving the most qualified candidates advantages in a competitive hiring landscape.
The use of AI in hiring processes should not only be transparently stated to applicants but should also be reviewed internally for potential bias. Ethical data management includes reviewing AI algorithms for bias, which often stems from skewed data sources. If your system has been fed data with a uniform demographic of candidates, promoting diversity and inclusion in an automated hiring process will be difficult. This raises a major ethical concern of AI being used to discriminate in the hiring process.
Developing engagement plans for better performance
Employee data plays an important role in crafting customized employee engagement plans. Automated analysis of workforce performance data can help identify engagement trends that may not be manually seen, offering tailored plans to boost employee engagement through training programs and development initiatives.
This data-driven decision making can improve employee experiences by providing automatic feedback and solutions. However, the concern of employee privacy from automated monitoring and AI assessment comes into play. Teams should be given transparent notice of data collection and how it's being used to improve their work.
“Many organizations have stated that their goal of using AI is to augment people’s work, rather than replace them. Productivity improvements are often achieved by one or more forms of augmentation of the worker. The question is this: How do those augmentation efforts that lead to productivity increases actually redefine the job?”¹
Companies must continuously ask this question to ensure that AI is being used for good, as an augmentation and not a replacement. Employees should feel supported by automated solutions and humanized at work, rather than being pushed to strict performance goals by data-driven decisions.
Humans are still the key to ethical data management
“Humans must continuously review, refine, and optimize the outcomes of generative AI systems, so having a ‘human in the loop’ seems like an obvious answer.” - A Privacy Primer On Generative AI Governance, Forrester Research, Inc., July 7, 2023.
Despite the rise and benefits of AI in workforces, a human-centric approach is still necessary for employee satisfaction and business success. Ethically implementing AI into a workforce includes the need for human oversight. Human oversight is necessary to adjust performance goals, ensuring employees aren’t burned out by AI-driven productivity improvements. Human oversight is also needed to eliminate bias in data-driven decision making, to ensure your business is ethically utilizing automated systems. This includes routinely reviewing AI training systems and data sources to promote balanced, fair business decisions.
Embracing AI to enhance employee workdays
According to a Gallup study, only about 21% of frontline employees say they use AI for their job. Embracing AI to enhance frontline roles can improve workdays for busy teams and business outcomes across an organization.
Ethical AI can be used to empower employees through:
- Better work-life balance: AI can play a role in promoting better work-life balance for employees, finding options to balance schedules in frontline workforces.
- Routine task automation: By automating routine tasks, employees can save time and invest resources elsewhere.
- Personalized support: Training shouldn't be one size fits all. AI solutions can find skill gaps based on performance data to personalize support for employees.
Consider the way AI and data ethics impact your workforce
With evolving laws like General Data Protection Regulation (GDPR) in the EU and the California Consumer Privacy Act (CCPA), it's more important now than ever for businesses to implement ethical data management and assess how AI ties into these practices. Prioritizing ethical AI implementation and data management can improve workplace experiences for employees and relationships with customers. These practices should be part of a business’s foundation to build trust and foster a positive work culture.
How is your business ethically implementing AI and how is this impacting your employee experience? How can workforce tools support ethical data management in your workplace?
[¹] Gartner, Digital Ethics: Questions You Should Raise Around AI Productivity, By Frank Buytendijk, Philip Walsh, Bart Willemsen, Bettina Tratz-Ryan, Helen Poitevin, 22 October 2024. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
