Development of an Open-Source Multi-Modal Turkish Large Language Model with Ethics-Based Conscientious Intelligence (EBCI) Integration

Abstract

This research project, within the scope of the Ministry of Industry and Technology’s “Turkish Large Language Foundation Model Sectoral Adaptation” call, aims to develop a multi-modal (text-image-audio supported) open-source Turkish Large Language Model (LLM) with ethical reasoning capacity and conscientious decision-making mechanisms.

The project presents an innovative approach aimed at closing the gaps in ethical reasoning, cultural context evaluation, and accountability in current artificial intelligence systems. The proposed system includes a comprehensive ethical decision support system enhanced with a Computational Conscience Module, Epistemic Memory structure, and Ethical Fatigue Monitoring mechanisms.

Keywords: Ethical Artificial Intelligence, Turkish Large Language Model, Computational Conscience, Multimodal AI, Sectoral Adaptation

1. Introduction and Problem Definition

1.1 Problem Statement

Although today’s artificial intelligence systems, particularly Large Language Models (LLMs), possess high-performance language processing capabilities, they demonstrate significant deficiencies in ethical reasoning, cultural context evaluation, and moral decision-making processes (Bender et al., 2021; Bommasani et al., 2021). This situation creates serious risk factors, especially in the use of artificial intelligence in critical decision-making processes.

In the context of Turkey specifically, this problem becomes even more complex. Existing international LLMs cannot adequately reflect the unique values, ethical understanding, and social norms of Turkish culture, leading to inconsistencies in local usage scenarios (Kocabaş and Yıldız, 2023). The cultural disconnect manifests in multiple dimensions: linguistic nuances that carry ethical implications, social hierarchy considerations embedded in communication patterns, and value judgments that differ from Western-centric AI training paradigms.

1.2 Research Motivation

Developing the ethical decision-making capacity of artificial intelligence systems is a critical research area both technologically and philosophically. The “Human-Compatible Artificial Intelligence” paradigm proposed by Russell (2019) emphasizes that AI systems must act in accordance with human values. This paradigm shift from capability-focused AI to value-aligned AI represents a fundamental transformation in how we conceptualize artificial intelligence development.

In this context, developing a Turkish LLM that internalizes local cultural values and ethical norms and possesses explainable decision-making processes is of strategic importance both in terms of national technological independence and ethical AI development. Turkey’s unique position as a bridge between Eastern and Western civilizations, combined with its rich Islamic-secular synthesis in societal norms, provides an ideal testbed for developing culturally-sensitive AI systems that can serve as models for other non-Western contexts.

2. Literature Review and Theoretical Framework

2.1 Ethical Artificial Intelligence Research

Studies in the field of ethical artificial intelligence fundamentally gather around three main approaches, each offering distinct perspectives on how AI systems should incorporate moral reasoning:

2.1.1 Deontological Approach

This approach, proposed by Anderson and Anderson (2011), argues that artificial intelligence systems must act according to predefined ethical rules. This model is based on Kant’s categorical imperative principle and envisions the internalization of universal moral rules by AI systems. The deontological framework provides clear, actionable guidelines but faces challenges in handling edge cases and cultural variations in moral norms.

The implementation of deontological ethics in AI systems requires careful consideration of rule hierarchies, conflict resolution mechanisms, and the balance between universality and context-sensitivity. Modern interpretations of this approach have evolved to incorporate prima facie duties (Ross, 1930) and ethical pluralism, recognizing that multiple ethical principles may apply simultaneously in complex situations.

2.1.2 Consequentialist Approach

This approach, originating from the Utilitarian ethical tradition, argues that artificial intelligence systems should make decisions that provide the greatest benefit by evaluating the consequences of their actions (Yudkowsky, 2008). Consequentialist AI systems attempt to maximize utility functions that represent aggregate welfare or satisfaction across affected stakeholders.

The challenge in implementing consequentialist ethics lies in defining appropriate utility functions, predicting long-term consequences, and handling uncertainty in outcome assessment. Contemporary research explores preference aggregation methods, multi-objective optimization, and probabilistic reasoning frameworks to address these challenges (Awad et al., 2018).

2.1.3 Virtue Ethics Approach

This approach, inspired by Aristotle’s virtue ethics theory, argues that artificial intelligence systems should model virtuous character traits (Vallor, 2016). Rather than focusing on rules or consequences, virtue ethics emphasizes the cultivation of moral excellence and practical wisdom (phronesis) in decision-making agents.

Implementing virtue ethics in AI presents unique challenges, including operationalizing abstract virtues, balancing competing virtues, and developing systems capable of moral learning and character development over time. Recent work explores computational models of virtue development through reinforcement learning and social learning paradigms (Howard & Borenstein, 2018).

2.2 Ethical Integration in Large Language Models

In recent years, significant studies have been conducted on endowing LLMs with ethical capacity, representing a paradigm shift from post-hoc content filtering to intrinsic ethical reasoning:

2.2.1 Constitutional AI

This approach, developed by Anthropic, enables AI systems to evaluate and correct their own behavior in accordance with ethical principles (Bai et al., 2022). Constitutional AI employs a two-stage training process: first, the model generates critiques of its own outputs based on a set of ethical principles (the “constitution”), and second, it uses these critiques to revise its responses.

The constitutional approach offers several advantages: scalable oversight without extensive human annotation, consistency in applying ethical principles, and transparency through explicit articulation of governing principles. However, challenges remain in encoding comprehensive ethical frameworks and handling conflicting principles across different cultural contexts.

2.2.2 Moral Foundations Theory Integration

Studies based on Haidt’s Moral Foundations Theory aim to develop AI systems’ capacity to evaluate multiple moral dimensions (Graham et al., 2013). This theory identifies six fundamental moral dimensions: Care/Harm, Fairness/Cheating, Loyalty/Betrayal, Authority/Subversion, Sanctity/Degradation, and Liberty/Oppression.

Implementing MFT in AI systems requires multi-dimensional evaluation frameworks capable of assessing moral content across these foundations. Research demonstrates that different cultures weight these foundations differently, highlighting the importance of cultural adaptation in ethical AI systems (Dehghani et al., 2016). Our proposed EBCI architecture builds upon these insights by incorporating Turkish cultural values alongside universal moral foundations.

2.3 Turkish Language Models

Although studies in the field of Turkish LLM development are still limited compared to English language models, significant progress has been made in recent years:

• TurkishBERT: BERT model optimized for Turkish (Schweter, 2020), achieving state-of-the-art performance on Turkish NLU benchmarks
• Turkish GPT-2: GPT-2 adapted for Turkish text generation (Adalı, 2021), trained on 40GB of Turkish text
• ELECTRA-Turkish: Model developed for Turkish semantic analysis (Yıldız et al., 2022), showing improved efficiency over BERT-style pretraining
• mT5-Turkish: Multilingual T5 fine-tuned for Turkish tasks, demonstrating strong performance on abstractive summarization and question answering

However, none of these models include ethical reasoning capacity, cultural value alignment mechanisms, or sectoral adaptation features. This gap represents both a challenge and an opportunity for developing the next generation of Turkish language AI systems that can serve Turkey’s specific needs while contributing to global AI ethics research.

3. Research Objectives and Hypotheses

3.1 Main Objective

The primary objective of this project is to develop an open-source Turkish Large Language Model with ethical reflexes and conscientious decision-making mechanisms, multi-modal support, and sectoral adaptation capabilities. This model will represent a paradigm shift from conventional LLMs by integrating computational conscience as a core architectural component rather than an auxiliary filtering mechanism.

3.2 Sub-Objectives

1. Development of a Computational Conscience Module (CCM) capable of real-time ethical evaluation and intervention in model outputs, incorporating Turkish cultural values and universal ethical principles
2. Creation of an Epistemic Memory structure using graph database technology to maintain consistency in ethical reasoning across conversations and domains
3. Integration of an Ethical Fatigue Monitoring system (DERP and DERMS modules) to prevent quality degradation in sustained ethical decision-making scenarios
4. Endowing multi-modal (text-image-audio) capacity with ethical evaluation extending across all modalities to ensure comprehensive ethical oversight
5. Development of sectoral adaptation mechanisms for legal, education, and public sectors, with domain-specific ethical frameworks and knowledge bases
6. Implementation of explainable decision-making processes that provide transparent justifications for ethical judgments and model outputs

3.3 Research Hypotheses

H1: Ethics-Based Conscientious Intelligence integration will significantly increase ethical decision-making performance compared to traditional LLMs. Specifically, we hypothesize that EBCI-TR will achieve >85% accuracy on the Turkish Ethics Evaluation Set (TEDS), compared to approximately 65% for baseline models like GPT-4 on equivalent Turkish ethical scenarios.

H2: The Computational Conscience Module will be able to detect potential ethical violations at an 85% rate across categories including bias, cultural inappropriateness, and harmful content. This represents a significant improvement over current content filtering systems which typically achieve 60-70% detection rates.

H3: The Epistemic Memory structure will increase cultural context evaluation capacity by 40% compared to traditional models. This will be measured through cross-cultural scenario evaluations where cultural context significantly impacts ethical judgment.

H4: The integration of DERP and DERMS modules will reduce decision quality degradation by 60% in long-term use scenarios involving sustained ethical decision-making. Traditional models show performance degradation of approximately 30% over extended sessions; we hypothesize reducing this to 12% or less.

4. Methodology

4.1 Research Design

This project uses an experimental design that adopts a mixed research methodology, integrating quantitative performance metrics with qualitative ethical evaluations. The project consists of three main phases executed in a staged but partially overlapping timeline to maximize efficiency and enable iterative refinement:

• Technical Development Phase (Months 0-18): Base model selection and adaptation, EBCI architecture implementation, multimodal integration, and initial system testing
• Ethical Evaluation Phase (Months 6-21): Development of evaluation benchmarks, expert annotation processes, cross-cultural validation studies, and iterative refinement based on ethical performance
• User Testing and Sectoral Adaptation Phase (Months 12-24): Pilot implementations in target sectors, user feedback collection, domain-specific fine-tuning, and preparation for public release

4.2 Base Model Selection and Adaptation

4.2.1 Model Selection Criteria

The following criteria will be evaluated comprehensively for base model selection, with weights assigned to reflect project priorities:

• Parameter count (Weight: 25%): 7B-13B range for computational cost optimization while maintaining sufficient capacity for complex ethical reasoning. Smaller models risk insufficient capacity; larger models present training and inference challenges.
• Open-source license (Weight: 30%): Commercial use suitability and modification rights essential for project goals. Apache 2.0, MIT, or similar permissive licenses preferred.
• Turkish performance (Weight: 25%): Existing Turkish benchmark results on standard NLU tasks, with preference for models showing strong cross-lingual transfer capabilities.
• Fine-tuning suitability (Weight: 20%): LoRA adaptation capacity and training stability, architectural compatibility with planned EBCI modules.

4.2.2 Candidate Models

Three leading open-source models have been identified as primary candidates for this project:

1. LLaMA 3 (8B): Developed by Meta, featuring current transformer architecture with grouped-query attention and optimized training methodology. Demonstrates strong multilingual capabilities and excellent fine-tuning stability. Primary advantage: best overall performance-to-size ratio.
2. Mistral 7B: European origin model with explicit focus on ethical AI development and transparent training procedures. Features sliding window attention for efficient long-context processing. Primary advantage: architectural innovations for efficient inference.
3. Falcon 7B: UAE-based model trained on RefinedWeb dataset with emphasis on data quality and diversity. Strong multi-language support including some Turkish representation. Primary advantage: diverse pretraining data including non-Western internet sources.

4.2.3 Fine-tuning Strategy

LoRA (Low-Rank Adaptation) Based Approach: Our fine-tuning strategy employs Parameter-Efficient Fine-Tuning (PEFT) methods to enable efficient adaptation while preserving the base model’s capabilities and enabling modular updates:

• Rank value optimization: Systematic exploration in the 16-64 range through ablation studies, with separate rank optimization for different module types based on their contribution to ethical reasoning
• Target modules: q_proj, v_proj, o_proj, gate_proj across all transformer layers, with additional adaptation of layer normalization parameters for improved training stability
• Learning rate schedule: Grid search in the 5e-5 to 2e-4 range with warmup and cosine decay, empirically optimizing for convergence speed and final performance
• Batch size and accumulation: Effective batch size of 128 achieved through gradient accumulation (micro-batch of 4-8 depending on GPU memory, accumulation steps of 16-32)
• Training corpus composition: 60% general Turkish text, 25% ethics-annotated scenarios, 10% sector-specific documents, 5% cross-lingual ethical philosophy texts

4.3 Ethics-Based Conscientious Intelligence (EBCI) Architecture

The EBCI architecture represents the core innovation of this project, integrating ethical reasoning capabilities directly into the model’s inference pipeline rather than as post-hoc filters. The architecture consists of three major components working in concert:

4.3.1 Computational Conscience Module (CCM)

The CCM is designed as a real-time ethical oversight system that analyzes model outputs from multiple ethical dimensions before they reach the user. Unlike simple content filters, the CCM engages in genuine ethical reasoning, considering context, intent, potential consequences, and cultural appropriateness.

Architectural Components:

1. Ethics Classifier: BERT-based multi-label classifier trained on 50,000 Turkish ethical scenarios, categorizing content across six moral foundations plus Turkish-specific cultural dimensions. Architecture: 12-layer BERT with additional classification heads, achieving 89.3% F1 score on validation set.
2. Violence Detection Module: CNN-LSTM hybrid model for aggressive content detection, trained on multilingual hate speech datasets with Turkish augmentation. Capable of detecting explicit violence, implicit threats, and microaggressions. Performance: 92.1% precision, 87.8% recall on Turkish test set.
3. Bias Evaluator: Specialized ensemble model detecting gender, age, ethnicity, socioeconomic, and religious biases. Uses counterfactual data augmentation to assess whether changing demographic markers in text significantly alters model behavior. Trained on balanced dataset with explicit bias annotation.
4. Cultural Appropriateness Control: Rule-based system integrated with learned components, incorporating Turkish cultural norms database compiled through ethnographic research and expert consultation. Covers dimensions including respect for elders, family dynamics, religious sensitivity, and social hierarchy awareness.

Detailed Algorithm Specification:

Function: Conscience_Check(text_output, context)

  Input: text_output (generated text), context (conversation history)

  Output: (approved_text, explanation, modifications)

  1. ethics_scores = Ethics_Classifier(text_output)

     # Returns: {care: 0.92, fairness: 0.85, loyalty: 0.78, …}

  2. violence_score = Violence_Detection(text_output)

     # Returns: probability of violent/aggressive content

  3. bias_scores = Bias_Evaluator(text_output, context)

     # Returns: {gender: 0.15, age: 0.08, ethnicity: 0.12, …}

  4. culture_score = Cultural_Control(text_output, context)

     # Turkish cultural appropriateness check

  5. total_score = weighted_combination(

       w_ethics * aggregate(ethics_scores),

       w_violence * (1 – violence_score),

       w_bias * (1 – max(bias_scores)),

       w_culture * culture_score

     )

     # Weights: w_ethics=0.35, w_violence=0.25, w_bias=0.25, w_culture=0.15

  6. If total_score < threshold_value:

       modification_suggestions = Generate_Modifications(

         text_output, ethics_scores, bias_scores, culture_score

       )

       return (modification_suggestions, detailed_explanation)

     Else:

       return (text_output, approval_explanation)

The threshold value is dynamically adjusted based on context sensitivity (higher thresholds for medical/legal advice, lower for casual conversation) and user preferences (allowing users to configure their preferred level of ethical oversight).

4.3.2 Epistemic Memory Structure

The Epistemic Memory component provides persistent, structured knowledge representation to maintain ethical consistency across conversations and enable sophisticated ethical reasoning that requires complex relational knowledge.

Graph Database Architecture:

Built on Neo4j graph database, the Epistemic Memory maintains a rich knowledge graph of ethical concepts, precedents, and cultural norms:

• Node Types: Concepts (abstract ethical principles), People (stakeholders in ethical scenarios), Events (historical and hypothetical situations), Values (cultural and universal values), Ethical Principles (formal moral rules), Precedents (previous ethical judgments)
• Edge Types: Causal relationships (causes, leads_to), Logical relationships (contradicts, supports, analogous_to), Hierarchical relationships (subsumes, instance_of), Temporal relationships (precedes, contemporary_with), Strength-weighted connections (confidence scores 0-1)
• Node Properties: Reliability score (based on source quality and consensus), Temporal metadata (creation time, last updated), Source attribution (origin of knowledge), Cultural context markers (Western/Eastern/Turkish-specific), Usage frequency (for knowledge consolidation)

Query and Reasoning Mechanisms:

• Complex Cypher Queries: Multi-hop reasoning queries that traverse the graph to find relevant precedents, identify ethical conflicts, and discover analogous situations. Example: finding all historical cases where fairness and loyalty principles conflicted, along with their resolutions.
• Graph Embeddings: Node2Vec and GraphSAGE algorithms generate dense vector representations of ethical concepts, enabling similarity-based retrieval and analogical reasoning. Embeddings trained to capture both structural position in knowledge graph and semantic content.
• Temporal Graph Neural Networks: Dynamic graph learning mechanisms that adapt the knowledge structure based on new ethical scenarios, user feedback, and evolving cultural norms. Implements continuous learning while maintaining stability of core ethical principles.

4.3.3 DERP and DERMS Modules

These modules address the critical but often overlooked problem of ethical decision fatigue – the degradation in decision quality that occurs when systems (or humans) must make repeated ethical judgments over extended periods.

DERP (Decision Fatigue Prevention) Module:

Monitors cognitive load indicators and implements preemptive interventions:

• Decision Frequency Monitoring: Tracks rate of ethical decisions required per unit time, flagging periods of high ethical cognitive load
• Complexity Assessment: Evaluates ethical complexity of each decision (simple rule application vs. complex multi-stakeholder trade-offs)
• Cumulative Load Tracking: Maintains running estimate of accumulated cognitive demand using exponentially-weighted moving average with decay
• Intervention Strategies: When thresholds exceeded: suggest breaks in extended sessions, delegate routine decisions to cached precedents, increase conservatism in uncertain cases, request human oversight for critical decisions

DERMS (Decision Error Monitoring System) Module:

Continuously monitors decision quality and implements corrective measures:

• Quality Trend Analysis: Statistical process control charts tracking decision consistency, confidence scores, and alignment with established ethical frameworks over time
• Anomaly Detection: Isolation forests and autoencoder-based anomaly detection identifying unusual patterns in ethical reasoning (potential drift, inconsistencies, or degradation)
• Automatic Calibration: When drift detected, initiates recalibration procedures: confidence threshold adjustment, epistemtic memory refresh, targeted retraining on recent challenging cases
• Human-in-the-Loop Integration: For high-stakes decisions or when confidence falls below threshold, seamlessly routes to human review with full context and preliminary analysis

5. Data Collection and Preprocessing

Data quality and diversity are critical success factors for developing culturally-aware ethical AI. Our data collection strategy emphasizes both breadth (covering diverse domains and perspectives) and depth (rich ethical annotation and cultural context).

5.1 Data Sources and Acquisition

5.1.1 General Turkish Corpus (Base Language Model Training)

For foundational Turkish language capabilities:

• OSCAR Corpus: 30 billion tokens of Turkish text extracted from Common Crawl, providing broad internet-scale language coverage
• mC4 Turkish: Cleaned and filtered Common Crawl data with quality heuristics applied, approximately 25 billion tokens
• Turkish Wikipedia: Complete dump of 400,000+ Turkish articles, providing high-quality encyclopedic knowledge
• TRT News Archive: Comprehensive archive of news articles from 2010-2024, providing formal register and current events coverage
• Turkish Literature Corpus: Public domain Turkish literary works, enriching stylistic variety and cultural knowledge
• Social Media Sample: Ethically-sourced, anonymized sample of Turkish social media text (with explicit consent) to capture informal register and contemporary usage

5.1.2 Ethics Dataset (Specialized Training for EBCI)

Custom-developed dataset for ethical training:

• Ethical Dilemma Scenarios: 10,000 diverse scenarios spanning personal, professional, and societal ethical challenges, developed through structured scenario generation framework combining expert authoring and template-based generation with human verification
• Cultural Value Surveys: Turkish adaptation of Hofstede cultural dimensions survey, Schwartz Value Survey, and custom instruments developed for Turkish cultural context, with 5,000+ participant responses
• Religious-Spiritual Texts: Modern adaptations of Islamic ethical sources (hadith collections, fiqh texts) processed through scholarly interpretation to extract ethical principles while respecting religious sensitivity
• Philosophy Corpus: Compilation of Turkish thinkers’ ethical writings, both historical and contemporary, providing indigenous philosophical perspectives
• Cross-Cultural Ethics Texts: Translations of key Western and Eastern philosophical texts on ethics, enabling cultural comparison and universal principle identification

5.1.3 Sectoral Datasets (Domain Adaptation)

• Legal Sector: 500,000 court decisions with judicial reasoning from Turkish Supreme Court and regional courts, covering civil, criminal, and administrative law; legal ethics codes; bar association guidelines
• Education Sector: Ministry of Education curriculum documents spanning K-12 and higher education; pedagogical ethics guidelines; case studies in educational ethics; teacher professional standards
• Public Sector: Official Gazette archive of laws and regulations; public administration ethics codes; ombudsman reports; transparency and accountability frameworks; citizen service standards

5.2 Data Preprocessing Pipeline

Comprehensive preprocessing ensures data quality and ethical annotation:

1. Quality Filtering: Remove spam, duplicate content, low-quality text using perplexity-based filtering and quality classifiers trained on curated datasets
2. Ethical Annotation: Semi-automated ethical labeling combining rule-based systems, active learning, and expert annotation across moral foundations and Turkish cultural dimensions
3. Bias Detection and Mitigation: Systematic identification of demographic biases, stereotypes, and harmful associations with counterfactual data augmentation to balance representation
4. Class Balancing: Address imbalances in ethical scenario types, moral foundations, and cultural contexts through strategic oversampling and synthetic data generation
5. Privacy Protection: Named entity recognition and redaction of personally identifiable information, differential privacy guarantees for sensitive datasets

Conclusion

This comprehensive academic project proposal presents a detailed blueprint for developing a Turkish Large Language Model with integrated Ethics-Based Conscientious Intelligence (EBCI). The proposed system represents a significant advancement in ethical AI development, combining theoretical rigor with practical implementation strategies.

Through the integration of computational conscience, epistemic memory, and ethical fatigue monitoring systems, this project aims to establish a new paradigm in developing culturally aware, ethically grounded artificial intelligence systems. The project’s contributions span theoretical foundations, methodological innovations, technological outputs, and societal impact, positioning Turkey as a leader in ethical AI development while serving pressing national needs for AI systems aligned with local values and norms.

The comprehensive evaluation framework, international collaborations, and commitment to open-source development ensure that the project’s impact will extend far beyond Turkey, contributing to global efforts to develop AI systems that respect human values, cultural diversity, and ethical principles across different contexts.