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Abstract: This article explores key HR technology-related controversies that will continue shaping organizational culture and workforce strategies through 2025. The article examines four controversial issues prevalent in today's literature: 1) Finding balance between employee data access enabling effective people strategies while respecting individual privacy expectations, 2) Addressing algorithmic bias that can systematically disadvantage underrepresented groups if people data and models reflect inherent biases, 3) The impact of automation on required job skills and the nature of work itself, and how reskilling efforts can support workers, and 4) Measuring worker productivity and well-being amid rising use of digital monitoring tools, and ensuring these do not normalize an always-on work culture at the expense of work-life balance. For each issue, the article provides academic grounding, analyzes practical implications through industry case studies, and offers recommendations for responsible technology adoption centered on employee experience.
As an HR technology strategist and consultant with experience implementing people analytics and workforce optimization solutions for Fortune 500 organizations, I have had a front-row seat observing the rapid evolution of HR technology. While innovation in HR tech has enabled data-driven decision making and productivity gains for both employers and employees, it has also introduced controversies around privacy, bias, and the future of work that must be addressed to ensure ethical and equitable progress.
Today we will explore four key HR tech-related controversies that I believe will continue shaping organizational culture and workforce strategies in 2025: the tension between data access and privacy, the challenge of mitigating algorithmic bias, the impact of automation on job design and skills development, and the balance of employee well-being with productivity metrics.
Data Access Versus Privacy
The first controversy involves finding the right balance between data access enabling effective people strategies while also respecting individual privacy expectations that are higher than ever before. As organizations increasingly leverage big data and cloud-based systems to gain insights, employees feel their personal information is at greater risk despite regulations like GDPR and CCPA intended to give them more control (Rothstein & Irwin, 2019). However, some degree of data sharing is necessary considering people data is most valuable when linked and analyzed at an enterprise level (Briscoe & Kellogg, 2015).
This tension played out prominently in 2021 when Google employees went on strike to protest the company's collection of physical access card data, with workers feeling it represented an overreach into their private lives (Conger, 2021). While Google maintained the data access policy supported COVID safety, many viewed it as prioritizing productivity over privacy. The episode highlighted the complexity of balancing open data cultures with personal autonomy preferences.
To foster trust, organizations should implement careful data governance programs with transparency into what data is collected and how it will be used. They may also consider offering alternative roles not requiring certain data access as well as privacy-preserving techniques like synthetic data and federated learning models. Overall, adopting a human-centric approach centered on employee experience will be critical to navigating this controversy productively.
Mitigating Algorithmic Bias
A second major concern involves addressing bias in algorithms used for recruiting, performance evaluation, compensation, and other impactful people decisions (Briscoe & Kellogg, 2015; Dastin, 2018). If data and models reflect the inherent biases of their creators or the environments in which they were developed, they risk systematically disadvantaging underrepresented groups. While organizations have made progress on identifying biased metrics and variables, fully "de-biasing" complex systems remains challenging (Holstein et al., 2019).
Amazon abandoned development of an AI recruiter after finding it favorable to male candidates over female counterparts, a reflection of biases in the tech industry's historical hiring patterns (Dastin, 2018). Such cases show the need for techniques like bias auditing, representative training data, and governance around sensitive people processes involving algorithms.
Hiring diverse teams to build models, rigorous testing and auditing of results for fairness, and continued evaluation after deployment can help address much but not all bias. Pairing predictive analytics with human judgment prevents complete reliance on potentially biased systems. Organizations must also acknowledge socio-technical bias as an ongoing challenge requiring sustained effort rather than a problem with a simple technical fix.
The Impact of Automation on Skills and Jobs
A third major controversy around HR tech's role in shaping the future of work centers on how automation and AI will impact required skills and the nature of jobs themselves. While many predict substantial displacement of roles, researchers argue technology also enables novel job creation by augmenting human skills and improving productivity (Brynjolfsson & McAfee, 2014; World Economic Forum, 2020).
Amazon faced criticism for automation initiatives perceived to reduce some warehouse jobs, yet the company claims technology also allows for more innovative, higher-skilled roles interfacing with machines that have boosted overall employment (D'Onfro, 2019). The mixed impacts show the need to proactively support reskilling and new job design versus reactive downsizing.
Focusing L&D strategies on blending human and technical skills through programs like classroom + on-the-job training can position workers for emerging, Tech-Augmented roles. Leaders must clearly communicate rationales for automation to minimize uncertainty and actively involve employees in envisioning the future organization. Changing work through retraining versus layoffs preserves culture and institutional knowledge critical to innovation.
Employee Well-being Amid Productivity Monitoring
Finally, measuring worker productivity objectively through digital tools enables improvements but risks normalizing a 24/7 work culture out of step with well-being-centered visions of the future. Surveillance software, keystroke logging, and electronic monitoring can feel intrusive and counterproductive versus trust-based approaches (Cranor, 2016; Zarsky, 2016).
Microsoft faced backlash over a patent for a system comparing employee webcam video and biometrics to an "engagement heuristic" without informed consent (Newton, 2019). While intended to maximize attention, the invasive approach violated expectations of personal workspace boundaries.
Tracking voluntary, consent-based productivity metrics like task time, not involuntary actions, and focusing analytics on systemic optimization over individual evaluation maintains privacy and work-life balance. Pairing digital monitoring with manager coaching emphasizing goals versus activity can still yield efficiency gains within a culture of empowerment versus monitoring. Comprehensive well-being programs further foster intrinsic motivation over constant surveillance.
Conclusion
In summarizing these controversies around data, algorithms, jobs, and well-being in HR technology, what is clear is that while analytics create opportunities to understand talent and optimize organizations, an overly narrow focus on metrics risks negative outcomes if not balanced with human priorities of privacy, fairness, lifelong learning, and quality of life. As the challenges are socio-technical in nature, solutions require not just robust guardrails and oversight of emerging tools but investment in company culture and leadership emphasizing dignity, equity and employee experience. By recognizing both the human and technical sides of these issues, and committing resources accordingly, organizations can embrace the promise of intelligent systems while avoiding potential pitfalls to realize a more ethical and productive future of work by 2025.
References
Briscoe, G., & Kellogg, K. C. (2015). The genesis of reused abstraction: Finding and remobilizing prior knowledge in new work. In Academy of Management Proceedings (Vol. 2015, No. 1, p. 10785). Briarcliff Manor, NY 10510: Academy of Management. https://doi.org/10.5465/ambpp.2015.10785abstract
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. WW Norton & Company.
Conger, K. (2021, May 28). Google employees protest company collection of physical access card data. The New York Times. https://www.nytimes.com/2021/05/28/technology/google-data-collection-employees.html
Cranor, L. F. (2016). Can technological solutions to workplace privacy problems keep pace with threats?. Notre Dame JL Ethics & Pub. Pol'y, 30, 489. https://heinonline.org/HOL/LandingPage?handle=hein.journals/ndjlepp30&div=16&id=&page=
D'Onfro, J. (2019, September 30). Amazon defends itself against claims of 'militarizing' its warehouses with controversial metric tracking technology. Business Insider. https://www.businessinsider.com/amazon-warehouse-metric-tracking-reduces-jobs-increases-innovation-company-says-2019-9
Dastin, J. (2018, October 9). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G
Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudík, M., & Wallach, H. (2019). Improving fairness in machine learning systems: What do industry practitioners need?. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-16). https://arxiv.org/pdf/1812.05239.pdf
Newton, C. (2019, November 21). Microsoft workers protest contract that raised ethics concerns about tech developed for military use. The Verge. https://www.theverge.com/2019/11/21/20975559/microsoft-employees-protest-arklay-lvmpd-facial-recognition-contract
Rothstein, M. A., & Irwin, A. (2019). Privacy, big data, and the public good: Frameworks for engagement. Cambridge University Press.
World Economic Forum. (2020, January 16). 8 top skills for 2025 that aren't currently common. https://www.weforum.org/agenda/2020/01/8-skills-2025-future-jobs-skills-career/
Zarsky, T. (2016). The trouble with algorithmic decisions: An analytic road map to examine efficiency and fairness in automated and opaque decision making. Science, Technology and Human Values, 41(1), 118-132. https://journals.sagepub.com/doi/10.1177/0162243915605575
Additional Reading
Westover, J. H. (2024). Optimizing Organizations: Reinvention through People, Adapted Mindsets, and the Dynamics of Change. HCI Academic Press. doi.org/10.70175/hclpress.2024.3
Westover, J. H. (2024). Reinventing Leadership: People-Centered Strategies for Empowering Organizational Change. HCI Academic Press. doi.org/10.70175/hclpress.2024.4
Westover, J. H. (2024). Cultivating Engagement: Mastering Inclusive Leadership, Culture Change, and Data-Informed Decision Making. HCI Academic Press. doi.org/10.70175/hclpress.2024.5
Westover, J. H. (2024). Energizing Innovation: Inspiring Peak Performance through Talent, Culture, and Growth. HCI Academic Press. doi.org/10.70175/hclpress.2024.6
Westover, J. H. (2024). Championing Performance: Aligning Organizational and Employee Trust, Purpose, and Well-Being. HCI Academic Press. doi.org/10.70175/hclpress.2024.7
Citation: Westover, J. H. (2024). Workforce Evolution: Strategies for Adapting to Changing Human Capital Needs. HCI Academic Press. doi.org/10.70175/hclpress.2024.8
Westover, J. H. (2024). Navigating Change: Keys to Organizational Agility, Innovation, and Impact. HCI Academic Press. doi.org/10.70175/hclpress.2024.11
Westover, J. H. (2024). Inspiring Purpose: Leading People and Unlocking Human Capacity in the Workplace. HCI Academic Press. doi.org/10.70175/hclpress.2024.12
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
Suggested Citation: Westover, J. H. (2024). The Rise of Data and Ethics: HR Tech Controversies that Will Shape the Future of Work in 2025. Human Capital Leadership Review, 14(3). doi.org/10.70175/hclreview.2020.14.3.12
