AICII-26 2026 - 5th MANILA International Conference on Artificial Intelligence: Challenges, Issues & Impacts (AICII-26) scheduled on March 30-April 1, 2026 Manila (Philippines)

Website https://iaaes.org/conference/378 | Edit Freely

Category Artificial Intelligence: Challenges, Issues & Impacts

Deadline: March 05, 2026 | Date: March 30, 2026-April 01, 2026

Venue/Country: Manila, Philippines

Updated: 2025-12-08 20:39:39 (GMT+9)

Call For Papers - CFP

Topics/Call for Papers

Full Articles/ Reviews/ Shorts Papers/ Abstracts are welcomed in the following research fields:

The field of Artificial Intelligence (AI): Challenges, Issues & Impacts is one of the most critical and fast-evolving areas of discussion across technology, ethics, law, and economics. A conference on this theme would address the immense power, inherent risks, and transformative effects of AI systems.

The topics can be organized into three core pillars:

1. ⚙️ Challenges in AI Development (Technical & Implementation)

This pillar focuses on the limitations, difficulties, and resource demands inherent in building and deploying AI systems, especially large-scale models like Generative AI.

Explainability and Interpretability (XAI):

The "Black Box" problem: Making complex deep learning models understandable.

Methods for Explainable AI (XAI) to build user trust and enable auditing.

Data and Robustness:

Challenges in curating, securing, and standardizing the massive datasets required for training frontier models.

Data Privacy vs. Data Utility: Reconciling AI's "hunger" for data with privacy regulations (like GDPR).

Adversarial Attacks and Security: Protecting AI systems from malicious input and data poisoning.

Resource and Environmental Impacts:

The massive computational cost (GPUs) and energy consumption required for training large models.

Environmental Footprint: Water consumption for cooling data centers and the contribution of AI infrastructure to e-waste and carbon emissions.

Scalability and Integration:

Difficulties in scaling AI initiatives from successful pilots to full enterprise implementation.

Challenges of integrating modern AI tools with legacy or outdated IT systems.

2. ⚖️ Ethical Issues and Societal Bias (Bias & Fairness)

This pillar addresses the direct harm and unfair outcomes that AI systems can perpetuate or create, particularly in high-stakes domains.

Bias and Discrimination:

Algorithmic Bias: Identifying and mitigating bias embedded in training data (historical, cultural, societal) that leads to discriminatory outcomes.

Impact of bias in critical sectors: Hiring/Recruitment, Credit Lending, Healthcare Diagnostics, and Criminal Justice (e.g., predictive policing).

Accountability and Liability:

Determining who is legally responsible when an autonomous AI system (e.g., a self-driving car or a diagnostic tool) causes harm or makes an incorrect decision.

Defining the scope of human oversight and control over increasingly autonomous AI agents.

Manipulation and Misinformation:

The creation and dissemination of Deepfakes (audio, video, text) and their impact on democracy, politics, and trust.

AI's role in amplifying filter bubbles and polarizing public opinion through biased content recommendations.

Creativity and Ownership (IP):

Intellectual Property (IP) and Copyright challenges for content (text, image, music) generated by AI models trained on existing copyrighted works.

Defining originality and ownership of AI-generated creative works.

3.