ORG 726 Week 6 Ethical and Workforce Effects of Technology Example

Monitored, Scored and Retrained: Ethical and Workforce Effects of Technology at a Mutual Insurer

[Student Name]

University of Phoenix

ORG/726: The Impact of Technology on Organizations

Week 6 Assignment

[Instructor Name]

[Date]

Prairie Shield Mutual, its people, practices and figures are composites written for a model paper.

Earlier weeks examined how Prairie Shield Mutual's technology changed its structure, work design, adoption and decisions. This paper asks a different question: what are the ethical and workforce effects, and who bears them? Four issues stand out: monitoring of adjusters, the fairness of the fraud model, the privacy of members' driving data and changes in claims jobs.

The Ethics of Algorithms

Mittelstadt et al. (2016) mapped the ethical debate about algorithms and identified six types of concern. Three are epistemic: conclusions based on inconclusive evidence, on inscrutable evidence that cannot be examined and on misguided evidence from flawed data. Two are normative: unfair outcomes and transformative effects, such as changes to how people understand their autonomy and privacy. The sixth is traceability: when harm occurs, it can be hard to find who is responsible. Martin (2019) argued that algorithms carry the choices of the people who build and use them, so the firms behind them answer for what they do, rather than treating algorithms as neutral tools.

Issue 1: Monitoring Adjusters

The claims platform shows managers each adjuster's claims closed per hour, time on each claim and idle time, updated in real time. In Omaha, the analyst ranks adjusters weekly and shares the rankings. Ball (2010) reviewed research on workplace surveillance and noted that monitoring has expanded with technology into performance, behavior and personal characteristics, and that its effects on employees, including stress and perceptions of privacy and fairness, depend on how it is implemented and whether employees see it as legitimate. Omaha adjusters report feeling watched and rushed, and several said they avoid complex claims that would lower their numbers, a perverse effect on members. Des Moines uses the same data for workload balancing without rankings, and adjusters there describe it as useful.

The same dashboard balances workload in one office and ranks people in the other; the ethics lie in the use, not the data.

Issue 2: Fairness of the Fraud Model

A review found that the fraud model flags claims from 14 rural counties at about twice the average rate. Investigation showed that the model weights claim filing delays and certain contractor patterns, both more common where contractors are scarce and claims are filed later. Confirmed fraud in those counties is not higher than average. Members in those counties face longer settlements and investigations they did not earn. In Mittelstadt's terms, the model draws misguided conclusions from data shaped by rural conditions and produces unfair outcomes. Under Martin's argument, Prairie Shield, not the vendor, is responsible for the effect.

Issue 3: Members' Driving Data

The planned usage-based auto program would collect location, speed, braking and phone use data from members' phones. Benefits include lower premiums for safe drivers and feedback that might reduce accidents. Risks include collection beyond what pricing needs, uses members do not expect, such as claims investigations, and data breaches. As owners of a mutual, members have a stronger claim to transparency and consent than customers of a stock insurer.

Issue 4: Claims Jobs

Over two years, automation and routing eliminated 74 claims positions, mostly through attrition and early retirement, while 19 new roles were created in model review, data quality and special investigations. Acemoglu and Restrepo (2019) argued that automation displaces labor from tasks it takes over, while new technologies also create new tasks in which labor has an advantage, and that effects on employment and wages depend on the balance between displacement and new tasks. At Prairie Shield, displacement has outpaced new tasks. Of the 74 eliminated roles, only six people moved into new roles; the new roles were filled mostly by outside hires with data skills.

Benefits That Should Not Be Lost

The analysis should not obscure what technology has done well. Simple claims now settle in four days instead of eleven, which matters most to members facing a damaged car or a leaking roof. Fraud detection has improved, and fraud costs are borne by all members through premiums. Workload dashboards, used as Des Moines uses them, have helped adjusters avoid being buried in claims while colleagues sat idle. New roles in model review offer higher pay and a career path some claims staff want. The aim is not to reverse these changes but to share their benefits and costs more fairly.

The Rural Members' Case in Detail

The fraud model issue shows how unfairness can arise without anyone intending it. No one chose to target rural members. The vendor trained the model on national claims data in which late filing and repeat contractors were associated with fraud. In rural Iowa and Nebraska, late filing often reflects distance and harvest schedules, and repeat contractors reflect the fact that one roofer may serve an entire county. The model learned a pattern that holds in cities and misfires in the country. Since Prairie Shield's membership is more rural than the national data, the harm falls on a core group of the members who own the company. The fix is technical, adding regional context to the model, but finding the problem required someone to ask the question, which is why regular fairness audits matter.

Weighing Interests

Members gain faster settlements and, potentially, lower premiums; members in rural counties bear unfair scrutiny. The company gains efficiency. Adjusters gain tools but lose autonomy where monitoring is punitive. Displaced employees bear the largest costs, while new roles go largely to outsiders. A mutual's mission of serving members and its long-standing commitment to its local workforce both argue for addressing these imbalances.

Ethical Commitments

Monitoring: productivity data used for workload balancing and coaching, not public rankings; adjusters see their own data and its uses.

Fairness: annual fairness audits of each model across regions and member groups by the model risk committee proposed in Week 5, with published summaries.

Responsibility: a named executive accountable for each model's effects, with a member appeal process for fraud referrals.

Privacy: driving data collected only with opt-in consent, used only for pricing and safety feedback, never for claims investigations without separate consent, and deleted after three years.

Workforce Plan

Reskilling: a paid 16-week program training current claims staff for model review and data quality roles, with a target of filling half of new roles internally.

Transparency: two years' notice of planned automation affecting roles.

Transition support: placement help and extended severance for those who cannot move.

Measures

Adjuster engagement and stress survey results by office; model flag rates and confirmed fraud by region; member consent and complaint rates for driving data; and the share of new technical roles filled internally. The board will receive an annual report.

Conclusion

Technology at Prairie Shield has brought gains and costs unevenly distributed. Research on algorithmic ethics, workplace surveillance and automation explains the concerns: monitoring used to rank, a model unfair to rural members, privacy risks in driving data and displacement outpacing new opportunities for existing staff. Commitments with named owners, fairness audits, consent and reskilling would align the company's technology with its mutual purpose.

References

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. https://doi.org/10.1257/jep.33.2.3

Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87-106. https://doi.org/10.1080/00236561003654776

Martin, K. (2019). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835-850. https://doi.org/10.1007/s10551-018-3921-3

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1-21. https://doi.org/10.1177/2053951716679679