Autonomous Vehicles for All?

The paper poses the question of whether autonomous vehicles will be socially responsible and serve all segments of society. It situates AVs within longstanding transportation inequities stemming from auto-centric systems, regressive funding mechanisms, and land-use patterns that disadvantage those who cannot or do not drive. While AVs promise safety, congestion relief, and enhanced mobility, the authors caution that without explicit attention to social responsibility, AVs may exacerbate existing inequalities due to higher costs, slow trickle-down to used-vehicle markets, potential data misuse, differential service provision in Mobility-as-a-Service models, and job displacement in sectors requiring less formal education. The purpose is to outline why social responsibility—fairness, equity, transparency—must be integral to AV development and deployment and to propose steps and frameworks for achieving it.

The paper synthesizes literature on fairness in AI and its applicability to AV systems. Group fairness notions (e.g., statistical parity, equalized odds, risk difference/ratio, odds ratio) and individual fairness (similar individuals receive similar outcomes) are reviewed, along with causal and counterfactual fairness to distinguish disparity from true bias. Methods such as situation testing and fair representation learning are noted. The authors discuss the rise of generative AI (GANs, GPT) in AV tasks (driver behavior imitation, sensor modeling, trajectory prediction) and the susceptibility of data-driven models to bias and frequency artifacts, underscoring the need for fairness-aware development. Policy and governance literature is covered, including GDPR’s fairness and transparency principles and critiques of its technology-neutral language leading to information asymmetries. AV policy reviews show emphasis on safety, privacy, cybersecurity, and liability with scant attention to social responsibility. U.S. federal efforts on equitable data (Equitable Data Working Group recommendations, OSTP progress) are cited as exemplars for advancing disaggregated, shareable, and collaboratively governed data that could support AV fairness assessments. Economic and equity-oriented transport research is referenced, including evidence that autonomous electric microtransit can significantly reduce total cost of ownership and that transit riders are disproportionately low-income, implying a role for subsidized/shared AV services.

This is a conceptual and policy-oriented paper rather than an empirical study. The authors: (1) articulate a social responsibility problem framing for AVs; (2) synthesize interdisciplinary literature from AI fairness, data governance (e.g., GDPR), transportation equity, and AV policy; (3) propose a stepwise process for developing socially responsible AVs: convening multidisciplinary experts to define a social responsibility checklist (fairness, equity, transparency), collecting diverse and representative data via field tests and pilots, critically analyzing outcomes across stakeholders to update requirements, and institutionalizing these steps within regulatory frameworks; and (4) discuss dimensions of fairness (algorithmic challenges and metrics), equity (service provision, affordability, public subsidies), and transparency (data practices, explainable AI) as components of a broader AV social responsibility framework. No experiments or statistical analyses are reported; examples and policy cases are used illustratively.

- AV benefits are significant but not guaranteed to be socially responsible without explicit design and policy interventions. - Fairness: Data-driven AV algorithms can propagate biases even when sensitive attributes are removed due to proxy variables; fairness requires careful selection and application of metrics (group fairness such as statistical parity/equalized odds; individual fairness via situation testing; causal/counterfactual fairness to separate disparity from bias). Generative AI used in AVs (GANs/GPT) is promising but prone to bias and frequency artifacts, necessitating fairness-aware development. - Data equity: Measuring fairness is constrained by limited, non-representative, or non-disaggregated datasets; U.S. federal equitable data initiatives highlight best practices (disaggregation, inter-agency sharing, multi-level collaboration) relevant to AVs. - Equity: High AV costs and delayed trickle-down (typical fleet turnover 10–15 years) risk excluding low-income users. Shared and/or subsidized AV fleets and autonomous public transit can enhance equitable access; regulatory requirements may be needed to ensure adequate service in rural/low-income/minority areas. Evidence from Singapore suggests autonomous electric microtransit can reduce total cost of ownership by about 70% compared to other microtransit options. - Transparency: GDPR links fairness with transparency but suffers from ambiguous, technology-neutral language enabling ongoing data capture by third parties; similar risks exist for AV-generated mobility data. Transparency and opt-out controls for personal data are essential; explainable AI remains immature and must address diverse user expectations and contexts. - Governance gap: Existing AV regulations in the U.S., Europe, and Asia largely emphasize safety, privacy, cybersecurity, and liability, with minimal treatment of social responsibility, leading to risks of inequitable outcomes. - Labor impacts: AVs will displace certain driving jobs; lessons from agriculture indicate technology-driven productivity gains can have adverse social consequences unless accompanied by community-focused adaptation. Near-term human-in-the-loop operations and long-term reskilling/redeployment strategies are recommended.

The analysis demonstrates that AV deployment, if guided solely by technical performance (safety, efficiency) and market incentives, may perpetuate or exacerbate inequities. Incorporating fairness metrics into AV perception and decision-making addresses algorithmic bias risks, while equitable data practices enable valid measurement and mitigation across demographic groups. Equity-focused service design and public policy—such as subsidized/shared AVs and obligations for coverage in underserved areas—translate social goals into access outcomes. Transparency in data governance, combined with progress toward explainable AI, builds user trust and guards against discriminatory uses of mobility data. The proposed framework embeds these dimensions into AV development cycles and regulatory oversight, aligning technological progress with societal values. Addressing labor transitions through proactive reskilling and human-in-the-loop strategies further mitigates negative externalities. Collectively, these measures directly respond to the research question by charting a path for AVs that reduce, rather than widen, social divides.

The paper argues for making social responsibility—fairness, equity, transparency—an integral part of AV design, testing, deployment, and regulation. It proposes a multidisciplinary, iterative framework that establishes a social responsibility checklist, gathers representative pilot data, evaluates outcomes with diverse stakeholders, and codifies requirements into policy and standards. Key contributions include translating AI fairness concepts to AV contexts, highlighting equitable data needs, emphasizing transparency and explainability, and addressing labor market transitions. Future research should: (1) operationalize and validate fairness metrics for specific AV subsystems (e.g., perception, prediction, planning) with representative datasets; (2) develop AV-specific transparency and data governance standards, including user-centric consent mechanisms; (3) evaluate the real-world equity impacts of shared/subsidized AV deployments via pilots; (4) assess labor reskilling pathways and policy interventions; and (5) create regulatory sandboxes that incorporate social responsibility criteria alongside safety and cybersecurity.

The work is conceptual and policy-oriented, without empirical experiments or quantitative evaluations of proposed frameworks. It relies on illustrative examples and secondary sources, which may not capture all operational contexts or global regulatory diversity. The generalizability of findings to specific AV technologies, vendors, and jurisdictions is untested, and some statistics (e.g., cost savings from microtransit) are context-specific case results rather than universally applicable outcomes.