Objectivityhave long stood as a grand ideal and claim within the sphere of contemporary human thought and existence. Proponents of objectivity, by calling upon scholars and obliging them to eliminate subjective characteristics in their engagement with natural and social phenomena, have promised the attainment of "true" theories that correspond and align with "reality". Numerous philosophers and scholars of science have critiqued this claim and rejected the possibility of its realization.With the emergence of artificial intelligence systems, and owing to their remarkable and unprecedented capabilities, the question of the feasibility of achieving objectivity is once again raised. This article examines the concept of objectivity by reflecting on prominent conceptions of artificial intelligence, particularly its applications in the field of urban planning—systems which, according to Stuart Russell and Peter Norvig's classification, fall under the category of those that "act rationally”. It appears that in supervised forms of AI systems, the realization of objectivity is impossible for several reasons, including the problem of aligning values. In unsupervised forms, the influence of subjective or intersubjective factors will be deeper and more complex. Technological efforts to approach this goal in more advanced AI systems are not only improbable but would also approximate a dangerous condition.
Keywords:
artificial intelligence, objectivity, subjectivity, urban planning,value alignment problem

Introduction
There is no scholarly consensus on a unified definition of artificial intelligence. Specialists from diverse fields operate with divergent assumptions about what AI is and how it should be evaluated. This conceptual plurality has not diminished AI's profound impact on both theory and practice. While some emphasize AI's transformative potential, others warn of algorithmic bias, opacity, and loss of human agency. Amidst these polarized views, a recurring question concerns the future role of human agency alongside AI. This article focuses on a specific epistemological dimension: the re-emergence of objectivity as a central aspiration in AI-assisted practices. Objectivism holds that knowledge of reality can and should be independent of subjective biases. Historically, this ideal has been challenged by epistemologies emphasizing the situatedness of all knowledge. However, big data and advanced machine learning have reinvigorated objectivist aspirations, appearing to offer a pathway toward genuinely data-driven knowledge. The article pursues two inquiries: a conceptual investigation into how AI systems operationalize objectivity, and an empirical exploration of AI applications in urban planning. The main research question is: Can rationally acting AI systems achieve scientific objectivity and eliminate subjective elements from specialized practices?

Materials and Methods
This study employs a qualitative, analytical approach combining theoretical examination and case-based analysis. The theoretical framework draws on Daston and Galison's historical-epistemological account of objectivity, distinguishing between methodological objectivity and procedural fairness. It also utilizes Russell and Norvig's fourfold classification of AI systems, focusing specifically on rationally acting systems—those that claim mathematically grounded, objective decision-making through utility functions and optimization. The empirical component comprises two case studies. The first is Sidewalk Labs' Quayside project in Toronto (2015-2020), a large-scale AI-driven urban development initiative by Google. The second examines two concurrent AI projects in Los Angeles (2020) using aerial photographs—one commissioned by the city council for green space expansion, another by the police for crime prediction—which produced contradictory recommendations from similar data. Analysis focused on identifying points of human intervention at each AI pipeline stage: problem definition, utility function design, data curation, model selection, result interpretation, and implementation.

Discussion and Results
The analysis reveals that rationally acting AI systems cannot achieve objectivity in the strong epistemological sense. First, the utility function—central to rational action—is always designed by human agents. Four current methods for defining utility functions all depend on human value judgments. AI systems are "value calculators," not "value creators." Second, human intervention persists at multiple critical stages. In problem definition, humans determine what questions to ask—witnessing how the Los Angeles projects produced contradictory recommendations due to different commission mandates. In data preparation, all data are collected with specific purposes, never neutral—exemplified by the absence of residents' historical memories of green spaces from the AI's dataset. In model design, choices of algorithms embed subjective judgments. In result interpretation, AI cannot distinguish correlation from causation—as in Los Angeles where it mistakenly equated tree density with crime rates. Third, even unsupervised or self-correcting systems merely shift human intervention to deeper levels—similarity criteria, cluster interpretation, and error definitions all require human judgment. Fourth, the claim that AI offers "satisfactory" outcomes is relativistic; satisfaction is a phenomenological category AI does not experience. Fifth, the Toronto case reveals that AI's promise of enhanced participation failed because public engagement resisted computational modeling. Citizens' concerns over privacy could not be algorithmically optimized. The Los Angeles cases show that even when AI processes data "objectively," human institutions appropriate results according to their own goals—police cut trees without further study.

Conclusion
The article concludes that AI systems—even those designed to act rationally—cannot achieve scientific objectivity in the sense of producing knowledge entirely independent of human subjects. While they demonstrate remarkable capacities for processing data and optimizing decisions, they remain epistemologically dependent on human agency at multiple critical stages. For supervised systems, both technical and theoretical reasons prevent objectivity. For unsupervised or self-correcting systems, the issue reappears through the Value Alignment Problem—which becomes more urgent, not less, as systems advance. The persistent failure of AI to produce unified theories about any single phenomenon mirrors the historical failure of objectivist philosophers; multiple interpretations persist because knowledge production inevitably involves human judgment and situated perspectives. Urban planning serves as an exemplary arena for observing this condition. The Toronto project's failure demonstrated that cities remain strongholds of democracy and privacy. The Los Angeles projects showed that human institutions appropriate AI results according to their own values. Ultimately, the future of objectivity in AI-assisted practices depends less on technological advancement and more on institutional and ethical infrastructures. The article recommends caution against techno-solutionist narratives and advocates for reflexive, democratically accountable governance of AI. The objectivist ideal is neither wholly illusory nor automatically realized; it is an ongoing practical achievement requiring human judgment and continuous ethical deliberation.