How AI Reinforces Inequality in Service Experiences
Written By Muyao Zhang
The Hidden Impact of Algorithmic Bias
Artificial intelligence has revolutionized service delivery across all industries, with the potential for efficiency, customization, and improved customer experiences. But behind this technology is a chilling reality: AI systems end up reproducing and amplifying existing social inequalities. They learn from historical data that usually contains inherent biases regarding race, gender, socioeconomic background, and other aspects. According to Varsha (2023), "Biases can be incorporated into AI systems either consciously or unconsciously... through the underlying data used to train the system and the methods used to process that data." Such bias perpetuation occurs even without discrimination being explicitly programmed into the system, leading to so-called "black box problems" where decision-making is opaque and unjustified.
Source: em360tech
Disproportionate Impacts on Vulnerable Populations
The burden of algorithmic bias falls disproportionately on society, with some groups experiencing far more negative effects. Poor communities and economically disadvantaged groups typically experience the greatest damage from discriminatory AI in money markets. Outcomes in lending demonstrate bias even when there is no explicitly discriminatory programming, constraining economic mobility and reinforcing current wealth disparities. Similarly, racial and ethnic minorities suffer systematic bias in various uses of AI. Ferrara (2024) writes that "small biases or skewed data inputs at various stages of algorithm development [can] result in significant and unexpected unfair outcomes," too often manifesting as racial discrimination in environments from criminal justice risk assessments to medical resource allocation.
Source: Salestech Series
Gender disparities emerge as another critical area of interest. Gender bias in AI-driven career advertisements, particularly in STEM careers, perpetuating occupational segregation and pay inequalities as AI systems limit what options are presented to certain groups. Further, those whose digital literacy or technology access is limited face compounded disadvantage. Farahani and Ghasemi (2024) acknowledge "digital inequality" as a central part of AI-mediated disparities, noting that "disparities in access to high-speed internet, digital literacy, and AI resources can create digital divides, limiting individuals' ability to benefit from AI-driven innovations."
Mechanisms of Reinforcing Inequality
There are several mechanisms to buttress each other to perpetuate inequality through service experiences driven by AI. The most nefarious issue is self-reinforcing feedback loops, where initial biases establish discriminatory results that generate new biased data used to train subsequent algorithms. As Oguntibeju (2024) has pointed out, "AI-driven decision-making has shown a tendency to produce uneven results" that can lead to "dissatisfaction, reduced customer loyalty, and lower profitability." This is a self-reinforcing cycle in which poor service experiences for marginalized groups become normalized and reproduced.
Their secrecy is then complemented by the opacity of AI systems. AI systems are basically "black boxes" where decision-making is unknown even to creators. Srivastava and Sinha (2023) observe that "AI systems frequently function as 'black boxes,' making it challenging to comprehend how they make judgements." Being transparent makes it so much harder to locate and correct bias, particularly for those who are nontechnical or lack the ability to challenge algorithmic decisions. The allocation of resources is another way in which AI entrenches inequality. AI systems more and more decide who gets priority service, preferential terms, and tailor-made services. Where allocation programs are discriminatory, they systematically lead to a better service experience for privileged groups and a worse one for others and, in practice, build a two-tiered world of services that perpetuates and reinforces existing social stratification.
Creating Pathways to Algorithmic Fairness
AI bias mitigation requires drastic measures with different stakeholders from technological, commercial, and policy backgrounds. Multidisciplinary development teams can uncover potential biases, which homogeneous teams could easily overlook, and regular algorithmic audits during development and roll-out prevent problems at bay before users even get to them. Trained datasets for all populations and environments are required, along with making AI decision-making transparent and accountable through the imposition of transparency conditions.
Source: Search Engine Journal
If you're interested in learning more about "The Ethics of Replacing Human Interaction"— check out my colleagues research blogs recapping Empathy & Emotional Intelligence, Consumer Rights & Transparency and Job Displacement.
Author: Muyao Zhang
Phone Number: (918)804-4315
Email: zzzzmy28@gmail.com
References:
Farahani, M., & Ghasemi, G. (2024). Artificial intelligence and inequality: Challenges and opportunities. Int. J. Innov. Educ, 9, 78-99. https://doi.org/10.32388/7HWUZ2
Ferrara, E. (2024). The butterfly effect in artificial intelligence systems: Implications for AI bias and fairness. Machine Learning with Applications, 15, 100525. https://doi.org/10.1016/j.mlwa.2024.100525
Oguntibeju, O. O. (2024). Mitigating artificial intelligence bias in financial systems: A comparative analysis of debiasing techniques. Asian Journal of Research in Computer Science, 17(12), 165-178. https://doi.org/10.1016/j.mlwa.2024.100525
Srivastava, S., & Sinha, K. (2023). From bias to fairness: a review of ethical considerations and mitigation strategies in artificial intelligence. Int J Res Appl Sci Eng Technol, 11, 2247-51. https://www.ijraset.com/best-journal/from-bias-to-fairness-a-review-of-ethical-considerations-and-mitigation-strategies-in-artificial-intelligence
Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems–A systematic literature review. International Journal of Information Management Data Insights, 3(1), 100165. https://doi.org/10.1016/j.jjimei.2023.100165
