AI Regulation in Waste Management and Recycling

This page is a translation of the original Portuguese text, provided for convenience. It deals with Brazilian law. If there is any discrepancy, the Portuguese version prevails. Read the original in Portuguese

Artificial intelligence is already transforming waste management in Brazil, but the absence of specific regulation creates concrete legal risks for companies and municipalities that adopt these technologies.

The Advance of Artificial Intelligence in Solid Waste Management

In recent years, we have witnessed a significant transformation in how solid waste is managed in Brazilian urban and industrial centers. Systems based on artificial intelligence now perform functions ranging from the automated sorting of recyclable materials to the optimization of collection routes, including forecasting disposal volumes and monitoring environmental conditions at sanitary landfills. This technological revolution, though promising from an operational standpoint, raises complex legal questions that Brazilian law has yet to address adequately.

The National Solid Waste Policy (Law 12.305/2010) set out fundamental guidelines for waste management in the country, including shared responsibility for the life cycle of products and reverse logistics. However, that legislation was conceived in a technological context completely different from today’s. When the provisions of the PNRS (National Solid Waste Policy) are examined in light of the AI technologies now available, regulatory gaps emerge that both lawmakers and legal practitioners need to address.

Computer vision systems, for example, are used in recycling cooperatives to identify and separate materials with greater precision than manual sorting. Machine learning algorithms calculate collection routes that reduce carbon emissions and operating costs. Smart sensors in containers monitor fill levels in real time, allowing on-demand pickups. Each of these applications generates data, makes automated decisions, and produces environmental, economic, and social impacts that the law needs to govern.

Current Legal Framework and Its Limitations

Brazil’s Legal Framework for Artificial Intelligence (Bill 2338/2023, still pending) introduced general principles for the development and use of AI systems, including transparency, safety, non-discrimination, and accountability. Yet when we try to apply those principles to the specific field of waste management, significant practical difficulties arise. The bill establishes risk categories but does not detail how to classify AI systems used in environmental processes, which creates legal uncertainty for companies in the sector.

The LGPD (Brazil’s General Data Protection Law, Law 13.709/2018) also applies to these operations, though not always in obvious ways. Smart collection systems that monitor disposal patterns by household or neighborhood can indirectly generate behavioral profiles of residents. Identifying consumption habits from the analysis of discarded waste amounts to the processing of personal data, which requires an adequate legal basis, consent or legitimate interest, and effective anonymization measures.

In environmental licensing, CONAMA (Brazil’s National Environmental Council) Resolution 237/1997 does not provide for assessing the impacts of using autonomous systems in waste treatment facilities. When a recycling plant operates with AI-guided sorting robots, algorithmic failures can result in cross-contamination of materials, compromising entire recycling chains. Responsibility for such damage remains in a gray area among the system manufacturer, the algorithm developer, and the facility operator.

Civil Liability for Algorithmic Decisions

One of the most sensitive issues concerns assigning responsibility when an AI system makes mistaken decisions in waste management. Consider a situation in which an algorithm incorrectly classifies hazardous waste as ordinary waste and sends it to unsuitable landfills. The resulting environmental harm can be severe, but the chain of liability involves multiple parties: the contracting municipality, the operating company, the technology supplier, and the developer of the AI model.

Integrating artificial intelligence into waste management requires a regulatory framework that balances technological innovation, environmental protection, and legal certainty for everyone involved in the chain.

The Brazilian Civil Code, in Articles 927 and following, provides tools for imposing liability, including strict liability for risk-creating activities. Environmental case law has settled on the view that liability for environmental damage is strict and joint and several. But introducing autonomous systems into this equation complicates the analysis of causation, especially when the algorithmic decision results from machine learning processes whose inner workings are opaque even to their own developers (the so-called “black box” problem).

Sector-Specific Regulatory Challenges

When you examine the particulars of the waste and recycling sector, you find regulatory challenges with no parallel in other areas where AI is applied. The first involves standardizing the environmental data used to train artificial intelligence models. The quality of an algorithmic decision depends directly on the quality of the input data, and in Brazil, data collection on solid waste is fragmented among thousands of municipalities with extremely uneven technical capacity.

The SINIR (National Information System on Solid Waste Management), provided for in the PNRS, still has significant gaps in coverage. AI models trained on incomplete or biased data can perpetuate regional inequalities, steering investments and resources toward areas that are already well served while neglecting regions with greater infrastructure needs. This algorithmic bias bears directly on the constitutional principle of reducing regional inequalities (Art. 3, III, of the Federal Constitution).

Impact on Waste Pickers and Cooperatives

Automating waste sorting with AI systems raises labor and social questions of great importance. Brazil has roughly 800,000 pickers of recyclable materials (catadores), according to estimates from the National Movement of Waste Pickers. The PNRS expressly recognizes the role of these workers in the recycling chain and requires their inclusion in waste management plans. Introducing robots and automated systems in recycling cooperatives without adequate transition planning can deepen the social vulnerability of these workers.

From a legal standpoint, concession contracts and public-private partnerships for waste management need to include clauses that ensure a just transition for workers affected by automation. This includes training programs, job reassignment, and, when necessary, financial compensation. Failing to provide these safeguards may amount to noncompliance with the PNRS’s social objectives and could support lawsuits brought by associations and unions representing the profession.

Certification and Auditing of Environmental Algorithms

Another challenge that deserves regulatory attention is the need for certification and periodic auditing of AI systems used in critical environmental processes. Unlike industrial equipment, which undergoes regular inspections and is covered by well-established technical standards (such as those of the ABNT, Brazil’s technical standards association), AI algorithms are not subject to standardized procedures for verifying environmental compliance.

We argue that environmental agencies, such as IBAMA (Brazil’s federal environmental agency) and the state environment departments, should develop specific protocols for assessing the reliability of AI systems applied to waste management. These protocols should cover verifying the accuracy of material classification, analyzing biases in training data, evaluating failure scenarios and their potential environmental impacts, and ensuring transparency in the algorithm’s decision criteria.

Regulatory Outlook and Practical Recommendations

The international landscape offers important reference points for building a Brazilian regulatory framework. The European Union, through the AI Act (Regulation 2024/1689), classified AI systems used in critical infrastructure as high-risk, subjecting them to strict compliance requirements. Although waste management is not expressly listed among the high-risk sectors in the European regulation, a broad reading of the “critical infrastructure” category could cover AI systems that manage hazardous waste or operate at the municipal scale.

At the domestic level, sector-specific regulation can move forward along different paths. CONAMA has the authority to issue resolutions setting technical requirements for the use of AI in environmental processes. ANVISA (Brazil’s National Health Surveillance Agency) can regulate matters relating to waste from health care services managed by automated systems. State sanitation regulatory agencies, for their part, can include algorithmic governance requirements in concession contracts.

Companies and municipalities that already use, or plan to adopt, AI systems in waste management should consider several preventive legal practices. First, conduct algorithmic impact assessments before deployment, documenting the environmental, social, and legal risks identified. Second, include contract clauses that clearly allocate responsibilities between technology suppliers and operators. Third, put in place human oversight mechanisms for critical algorithmic decisions, especially those involving the classification of hazardous waste.

Taking out specific insurance for risks arising from algorithmic failures also deserves consideration, although the Brazilian insurance market is still at an early stage of developing products for AI risks. Finally, keeping detailed records (logs) of algorithmic decisions is an essential measure, both for environmental audits and for any defense in judicial or administrative proceedings.

The Role of Municipalities in Algorithmic Waste Governance

Brazilian municipalities hold a central position in this discussion, since they have constitutional authority to organize and provide urban cleaning and solid waste management services (Art. 30, V, of the Federal Constitution). When they incorporate AI systems into their municipal integrated solid waste management plans, local governments must observe principles of transparency and public participation that go beyond mere technical compliance.

The Access to Information Law (Law 12.527/2011) makes publicity the rule for acts of public administration. When a municipality uses algorithms to define collection routes, prioritize neighborhoods, or allocate recycling resources, those automated decisions must be open to explanation and challenge by the citizens they affect. Algorithmic opacity cannot serve as a shield against public oversight of government.

It is also worth noting that the PMGIRS (Municipal Integrated Solid Waste Management Plans), required by the PNRS, need to be updated to address the use of AI technologies. That update should include algorithmic performance targets, criteria for replacing systems that fail to meet the required environmental standards, and mechanisms for public participation in governing these technologies. Municipal environmental councils can play an important role in overseeing and monitoring these deployments.

As informações deste artigo são de caráter informativo e não substituem consulta jurídica individualizada. Cada caso possui particularidades que exigem análise específica.