Artificial Intelligence-Driven Biodiversity Conservation Framework: A Literature Review

1. Introduction

Biodiversity is foundational to ecosystem stability and human well-being. However, accelerating anthropogenic pressures have resulted in unprecedented rates of species extinction [20]. Traditional conservation strategies have struggled to keep pace with the scale and complexity of these threats. In response, artificial intelligence (AI) has emerged as a transformative tool [6,24], offering the potential to revolutionize ecological data collection, analysis, and decision-making processes [12,21].

AI-driven frameworks integrate machine learning (ML), computer vision, and natural language processing to enhance monitoring and management. Yet, deployment is not without challenges. Concerns regarding fairness, transparency, and interdisciplinary integration mirror those observed in healthcare and education [1,14], raising critical questions about the ethical implications of AI-powered conservation tools [2,12].

This literature review synthesizes insights from recent scholarship on AI applications, with a particular focus on the design, implementation, and governance of AI-driven frameworks. Drawing on parallel advances and ethical discussions in AI for education, healthcare, and creative problem-solving, this review elucidates the opportunities and challenges facing AI-driven biodiversity conservation. It concludes by proposing principles and research directions for the responsible and effective integration of AI in the service of global biodiversity.

2. Methodology: Systematic Literature Search

To ensure a comprehensive and unbiased synthesis of the current state of AI in biodiversity conservation, a systematic literature search was conducted. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines where applicable to the narrative synthesis.

2.1. Strategy and Databases

The search was conducted across five primary academic databases: Web of Science, Scopus, IEEE Xplore, Google Scholar, and PubMed (specifically for cross-domain ethical comparisons). The search period was restricted to papers published between 2014 and 2025 to capture the most recent advancements in deep learning and transformer architectures.

2.2. Keywords and String Construction

The search utilized combinations of the following primary keywords:

• Domain: “biodiversity conservation,” “ecology,” “wildlife management,” “species monitoring,” “habitat restoration.”
• Technology: “artificial intelligence,” “machine learning,” “deep learning,” “computer vision,” “convolutional neural networks (CNN),” “bioacoustics,” “remote sensing.”
• Governance/Ethics: “algorithmic bias,” “explainable AI (XAI),” “transparency,” “socio-ecological systems,” “fairness.”

Example search string: (“AI” OR “Machine Learning”) AND (“Biodiversity” OR “Species Identification”) AND (“Ethics” OR “Bias”).

2.3. Inclusion and Exclusion Criteria

Papers were included if they met the following criteria: (1) Peer-reviewed journal articles or high-impact conference proceedings; (2) Studies presenting original AI frameworks for ecological data or meta-analyses of existing tools; (3) Research addressing the intersection of technology and conservation ethics. Exclusion criteria involved: (1) Studies lacking a control group or rigorous validation metrics in field experiments; (2) Non-peer-reviewed blog posts or white papers; (3) Studies where AI was used only for basic statistical analysis (e.g., simple linear regression) without a “learning” or “optimization” component. A total of 40 core references were selected for final synthesis.

3. AI in Biodiversity Conservation: Opportunities and Core Components

3.1. The Promise of AI for Conservation

AI-powered computer vision systems automate species identification from camera traps [7,29], drones [11], and satellite imagery [40]. Recent advances in machine learning for image-based identification have transitioned the field from manual labeling to high-throughput automated workflows [19]. Deep learning architectures, such as Convolutional Neural Networks (CNNs) [25], have achieved human-level accuracy in identifying savanna megafauna [33] and detecting illegal logging via acoustic sensors [15,31]. These systems follow a multi-stage pipeline: problem formulation, knowledge representation, manipulation, and evaluation [4,16].

Machine learning models facilitate the detection of patterns in species distributions, habitat changes, and population trends, enabling more timely and targeted interventions. Furthermore, AI-driven simulations and optimization algorithms support scenario analysis and resource allocation, enhancing the capacity to anticipate and mitigate emerging threats.

3.2. Data Collection and Knowledge Representation

Modern data sources include passive acoustic monitoring [10], environmental DNA (eDNA) [37], and citizen science platforms like iNaturalist [27]. Digital traces from social media are increasingly used to map human-nature interactions [38], while digital data mining helps identify "cryptic extinctions" that might otherwise go undetected in traditional surveys [39]. Knowledge representation, such as the Essential Biodiversity Variables (EBV) framework, supports the integration of these disparate datasets into a unified global capture of species populations [17]. However, data representativeness remains a challenge; over-reliance on data from high-income regions can perpetuate "geographic blind spots" [2,34]. Knowledge representation (e.g., ontologies) supports interoperability between these diverse datasets [23,36].

Knowledge representation in AI systems encompasses the encoding of species traits, ecological relationships, environmental variables, and management objectives. Structured representations (e.g., graphs, ontologies) support effective reasoning and interoperability, while unstructured representations (e.g., images, text) demand advanced processing techniques such as deep learning and natural language processing. The choice of representation influences the system’s ability to generalize, adapt, and explain its recommendations, underscoring the need for transparent and interpretable models [3].

3.3. Methods of Knowledge Manipulation: Learning and Reasoning

Machine learning (ML) models are used for classification and habitat suitability modeling. Benchmark studies have shown that the predictive performance of presence-only distribution models can be significantly enhanced through ensemble techniques and updated modeling practices [32]. Large-scale identification of plant species in the wild has also been revolutionized by deep learning, allowing for broader taxonomic coverage [26]. When integrated with creative problem-solving (CPS) paradigms, AI agents can adaptively expand their conceptual space to address novel challenges in uncertain environments [4,23,36].

3.4. Evaluation and Feedback

The evaluation of AI-driven conservation interventions involves both quantitative and qualitative metrics. Performance measures such as accuracy, precision, recall, and area under the curve (AUC) are used to assess model predictions, while scenario-based simulations and field experiments validate the real-world impact of AI recommendations. Continuous feedback loops, drawing on monitoring data and stakeholder input, are essential for adaptive management and system improvement. As in educational measurement, the incorporation of human-in-the-loop frameworks ensures that AI outputs are scrutinized by domain experts, fostering accountability and trust [1].

4. Ethical, Social, and Technical Challenges

4.1. Fairness, Bias, and Representation

AI systems are susceptible to biases. In conservation, biased data collection can result in the neglect of underrepresented species or regions [2,18]. Analogous challenges are documented in AI for education, where demographic biases perpetuate inequality [1,9]. Mitigating bias requires deliberate strategies such as demographic stratification and participatory design [34,35].

Mitigating bias requires deliberate strategies at multiple stages of the AI pipeline. Data collection protocols must strive for representativeness, transparency, and inclusiveness, with mechanisms for continuous fairness assessment and auditing. As highlighted in educational AI research, fairness cannot be achieved solely through large-scale data aggregation; local context and stakeholder engagement are critical for identifying and addressing subtle or systemic forms of unfairness [2]. Approaches such as demographic stratification, participatory design, and open data initiatives enhance the legitimacy and effectiveness of AI-driven conservation efforts.

4.2. Explainability and Transparency

The complexity and opacity of many AI models, especially deep learning systems, pose significant challenges for explainability and transparency. As observed in healthcare and education, the inability to interpret AI decisions undermines trust, accountability, and the ability to detect errors or biases [1,3]. In conservation, explainability is crucial for justifying management actions, securing stakeholder buy-in, and facilitating regulatory oversight.

The opacity of "black box" models undermines trust [22]. Explainable AI (XAI) methodologies, such as feature importance and counterfactual explanations, are essential for justifying management actions to stakeholders [3,14]. Distinguishing between explainability ("why") and interpretability ("how") is critical for building trustworthy systems [3,22].

4.3. Human-in-the-Loop and Interdisciplinary Collaboration

The integration of human expertise throughout the AI pipeline is essential for ensuring the validity, reliability, and ethical acceptability of AI-driven conservation tools. Human-in-the-loop frameworks, as advocated in educational measurement and creative problem-solving literature, enable domain experts to supervise, validate, and refine AI outputs [1,4]. Multidisciplinary teams—comprising ecologists, data scientists, ethicists, and local stakeholders—bridge the gap between technical innovation and practical application, fostering solutions that are context-sensitive and socially robust.

Participatory design processes, continuous stakeholder engagement, and transparent communication channels are vital for aligning AI interventions with community values and needs [2,5]. In educational AI, transdisciplinary curricula and project-based learning approaches have been shown to enhance understanding, creativity, and ethical awareness, lessons that are directly relevant to conservation education and capacity-building [5].

4.4. Environmental and Societal Impacts

While AI offers substantial benefits for conservation, its deployment entails environmental and societal costs. The computational resources required for training large models contribute to energy consumption and carbon emissions, raising questions about the net ecological footprint of AI interventions [1]. Moreover, the automation of conservation tasks can disrupt traditional livelihoods and governance structures, necessitating careful consideration of social impacts and equitable benefit-sharing.

Balancing technological innovation with environmental stewardship and social justice is a central challenge for AI-driven conservation frameworks. Ethical guidelines, regulatory standards, and impact assessments must be developed in consultation with affected communities and stakeholders, drawing on experiences from healthcare and education sectors [1,3].

5. Lessons from Parallel Domains: AI in Education, Healthcare, and Creative Problem-Solving

5.1. Ethical Governance and Standards

The rapid proliferation of AI in high-stakes domains has prompted the development of ethical guidelines and governance structures aimed at promoting responsible innovation. Educational and healthcare sectors have established ethical guidelines emphasizing inclusiveness and human oversight [1,14]. These frameworks provide templates for conservation, highlighting the need for multidisciplinary oversight bodies [5,12].

5.2. Frameworks for Fairness and Continuous Assessment

As advocated in creative problem-solving literature, domain experts must supervise and refine AI outputs [4,23]. Human oversight at critical junctures ensures that AI interventions are context-sensitive and socially robust [12,35]. AI systems must not only perform well on aggregate metrics but also demonstrate equitable outcomes across diverse contexts and populations. The adoption of fairness-aware pipelines, as advocated in educational technology research, supports the emergence of more just and inclusive conservation strategies.

5.3. Explainable AI and Trustworthiness

The literature on explainable AI (XAI) in healthcare offers insights into the challenges and solutions associated with model transparency and trust. The distinction between explainability and interpretability, as well as the need for high-fidelity, human-comprehensible explanations, is critical for fostering trust among practitioners and stakeholders [3]. XAI methodologies, including attention mechanisms, feature selection, and surrogate modeling, enable the demystification of complex AI systems, facilitating their integration into decision-making processes.

Trustworthy AI systems are those that balance predictive performance with transparency, accountability, and user agency. In conservation, as in healthcare, the ability to explain and justify AI recommendations is essential for stakeholder acceptance and effective policy implementation.

5.3. Creative Problem Solving and Adaptation

AI systems deployed in dynamic, uncertain, or novel environments must exhibit creative problem-solving capabilities. The CPS framework, as articulated in artificial intelligence literature, emphasizes the need for systems that can expand their conceptual space, discover new knowledge, and adapt to unforeseen challenges [4]. This flexibility is particularly relevant in biodiversity conservation, where emergent threats, complex interactions, and incomplete knowledge demand adaptive, innovative solutions.

The development of CPS-enabled AI agents involves the integration of learning, reasoning, and knowledge manipulation techniques, supported by robust evaluation and feedback mechanisms. Human creativity, supported by AI augmentation, remains indispensable for addressing the multifaceted challenges of biodiversity conservation.

5.4. Transdisciplinary Education and Capacity Building

Transdisciplinary education, as exemplified by AI curricula that integrate multiple disciplines and perspectives, is crucial for preparing conservation practitioners to engage with AI technologies effectively [5]. Project-based and problem-based learning approaches foster critical thinking, creativity, and ethical awareness, equipping learners to navigate the technical, social, and ethical complexities of AI-driven conservation.

The co-design of curricula with educators, communities, and domain experts ensures relevance and inclusivity, while experiential learning opportunities (e.g., internships, field projects) bridge the gap between theory and practice. The lessons from educational AI underscore the importance of capacity building and community engagement in the successful deployment of AI for biodiversity conservation.

6. Toward a Responsible AI-Driven Biodiversity Conservation Framework

6.1. Principles for Framework Design

Drawing on the literature from conservation, education, healthcare, and AI research, several key principles emerge for the design of responsible AI-driven biodiversity conservation frameworks:

• Fairness and Inclusivity: Ensure equitable outcomes across taxa and regions [1,2].
• Transparency and Explainability: Utilize XAI to promote stakeholder trust [3,22].
• Human-in-the-Loop: Maintain expert oversight for validation [4,12].
• Interdisciplinary Collaboration: Engage ecologists, ethicists, and local communities [34,35].
• Continuous Assessment and Adaptation: Implement feedback loops to detect unintended impacts [1,30].
• Environmental Sustainability: Evaluate and minimize the carbon footprint of AI models [28].

7. Directions and Future Challenges

The responsible integration of AI into biodiversity conservation is an ongoing endeavor, demanding sustained research and innovation. Key areas for future work include:

• Development of context-sensitive fairness metrics and bias mitigation techniques tailored to conservation data and objectives [2].
• Advancement of XAI methods that balance interpretability with predictive performance in ecological applications [3].
• Design of adaptive, creative AI agents capable of addressing novel conservation challenges through CPS frameworks [4].
• Creation of transdisciplinary curricula and capacity-building programs to empower practitioners and communities [5].
• Establishment of governance structures and ethical guidelines informed by cross-sectoral experiences in education and healthcare [1,3].

8. Conclusions

Artificial intelligence holds immense promise for advancing biodiversity conservation, offering new capabilities for data analysis, prediction, and decision support. However, the deployment of AI-driven frameworks must be guided by principles of fairness, transparency, inclusivity, and sustainability. Lessons from AI applications in education, healthcare, and creative problem-solving illuminate both the opportunities and the ethical, social, and technical challenges that must be navigated.

A responsible AI-driven biodiversity conservation framework integrates multidisciplinary expertise, participatory processes, explainable models, and continuous assessment. By centering human values and ecological integrity, such frameworks can harness the power of AI to safeguard the diversity of life on Earth, ensuring that technological innovation serves both nature and society.