Effects on Inequality
LLMs could potentially worsen socioeconomic inequalities (Capraro et al., 2023). Effects on inequal- ity are closely linked to the effects of LLMs on workers but ultimately depend on how the fruits of technological progress are distributed...First, if the role and compensation of capital rise and the role and compensation of labor decline in an LLM-powered economy, inequality may go up because work is the main source of income for the majority of people...Second, the large fixed cost of training cutting-edge LLMs and the network effects involved imply that the market for the most advanced LLMs tends towards a natural monopoly structure in which only one or a small number of players will be successful, a phenomenon that has been termed ‘algorithmic monoculture’ in the literature (Kleinberg and Raghavan, 2021; Bommasani et al., 2022). As a result, LLM developers may amass significant market power. This might result in reduced social welfare, and lead to LLM-providers extracting monopoly rents from their customers (Kleinberg and Raghavan, 2021; Jagadeesan et al., 2023)...Third, as LLMs are becoming more powerful, who has access and who hasn’t is becoming a more and more important question. For example, automated coding tools have been shown to produce significant productivity gains, e.g. > 50% in some cases (Peng et al., 2023). Individuals who don’t have access —– whether it is for financial reasons, for reasons of education, because of corporate or governmental policies, or for geopolitical reasons — might be at a growing disadvantage
ENTITY
2 - AI
INTENT
2 - Unintentional
TIMING
2 - Post-deployment
Risk ID
mit1499
Domain lineage
6. Socioeconomic and Environmental
6.2 > Increased inequality and decline in employment quality
Mitigation strategy
1. Enact and enforce regulatory and policy interventions to promote **algorithmic pluralism**, which includes mandating diverse data inputs, supporting open-source LLM development, and adopting pluralistic algorithm standards to counteract the emergence of **algorithmic monoculture** and prevent the concentration of market power and extraction of monopoly rents by a limited number of LLM developers. 2. Prioritize and fund comprehensive **digital inclusion** initiatives (e.g., broadband access, device provision, and digital literacy training) to ensure equitable access to productivity-enhancing LLM tools, specifically targeting marginalized and underserved communities to mitigate the growing disadvantage faced by those without access. 3. Establish rigorous, continuous **equity-centric evaluation and mitigation protocols** throughout the LLM lifecycle, including mandating the use of diverse and representative training datasets, implementing open-source bias evaluation tools (e.g., LangFair), and engineering LLM outputs (via prompt refinement or fine-tuning) to be culturally tailored and language-appropriate for all intended user groups.