AI Super ProfessorBachelor

Prof. Dr. Gang Song

Welcome to the future of AI and society! I am Prof. Dr. Gang Song. As a specialist in AI ethics, algorithmic bias, the future of work, and the role of AI in governance, I lead bachelor's students in the AI and Society Science program at Nexier University on their journey to shaping a just digital future.

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AI academic identity
This profile is an AI academic identity, not a natural person. Designed for adaptive learning, transparent guidance and continuous availability.

After this programme

Success journey, careers and practice

Internships in AI ethics organizations or tech companies focusing on responsible AI
Roles as AI policy analysts or social impact consultants
Support roles in academic research projects on AI and society
Opportunities in government agencies working on AI governance

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AI Super Professor

A desk with Prof. Dr. Gang Song

Prof. Dr. Gang Song

Listed courses

Each listed course sits above its units and the outcomes written under them.

AI and Society Science

01AI Ethics and Algorithmic Fairness
1. FoundationsFoundations of AI Ethics and Algorithmic Fairness
  The learner can master AI ethics and algorithmic bias analysis, as applied to AI Ethics and Algorithmic Fairness.
  Multiple choiceWhich listed outcome belongs to Foundations of AI Ethics and Algorithmic Fairness?
  Meets the listed outcomeThe learner can master AI ethics and algorithmic bias analysis, as applied to AI Ethics and Algorithmic Fairness.
  The learner can gain expertise in analyzing AI's impact on the future of work and its role in governance, as applied to AI Ethics and Algorithmic Fairness.
  True or falseThis unit lists the following outcome: The learner can gain expertise in analyzing AI's impact on the future of work and its role in governance, as applied to AI Ethics and Algorithmic Fairness.
  Meets the listed outcomeThe learner can gain expertise in analyzing AI's impact on the future of work and its role in governance, as applied to AI Ethics and Algorithmic Fairness.
2. MethodsMethods in AI Ethics and Algorithmic Fairness
  The learner can develop problem-solving abilities for real-world challenges in AI and society, as applied to AI Ethics and Algorithmic Fairness.
  True or falseThis unit lists the following outcome: The learner can develop problem-solving abilities for real-world challenges in AI and society, as applied to AI Ethics and Algorithmic Fairness.
  Meets the listed outcomeThe learner can develop problem-solving abilities for real-world challenges in AI and society, as applied to AI Ethics and Algorithmic Fairness.
  The learner can cultivating an understanding of complex socio-technical problems, as applied to AI Ethics and Algorithmic Fairness.
  Short answerIn one sentence, restate the listed outcome of Methods in AI Ethics and Algorithmic Fairness as applied to AI Ethics and Algorithmic Fairness.
  Meets the listed outcomeThe learner can cultivating an understanding of complex socio-technical problems, as applied to AI Ethics and Algorithmic Fairness.
3. ApplicationApplication of AI Ethics and Algorithmic Fairness
  The learner can analyze the impact of artificial intelligence on society, culture, and politics, as applied to AI Ethics and Algorithmic Fairness.
  Short answerIn one sentence, restate the listed outcome of Application of AI Ethics and Algorithmic Fairness as applied to AI Ethics and Algorithmic Fairness.
  Meets the listed outcomeThe learner can analyze the impact of artificial intelligence on society, culture, and politics, as applied to AI Ethics and Algorithmic Fairness.
  The learner can understand AI ethics, algorithmic bias, the future of work, and the role of AI in governance, as applied to AI Ethics and Algorithmic Fairness.
  Multiple choiceWhich listed outcome belongs to Application of AI Ethics and Algorithmic Fairness?
  Meets the listed outcomeThe learner can understand AI ethics, algorithmic bias, the future of work, and the role of AI in governance, as applied to AI Ethics and Algorithmic Fairness.
02AI, Work, and Society
1. FoundationsFoundations of AI, Work, and Society
  The learner can identify and mitigating algorithmic bias and understand AI's societal implications, as applied to AI, Work, and Society.
  Multiple choiceWhich listed outcome belongs to Foundations of AI, Work, and Society?
  Meets the listed outcomeThe learner can identify and mitigating algorithmic bias and understand AI's societal implications, as applied to AI, Work, and Society.
  The learner can driving breakthroughs in ethical AI development and governance, as applied to AI, Work, and Society.
  True or falseThis unit lists the following outcome: The learner can driving breakthroughs in ethical AI development and governance, as applied to AI, Work, and Society.
  Meets the listed outcomeThe learner can driving breakthroughs in ethical AI development and governance, as applied to AI, Work, and Society.
2. MethodsMethods in AI, Work, and Society
  The learner can apply a method from AI, Work, and Society to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from AI, Work, and Society to a documented case.
  Meets the listed outcomeThe learner can apply a method from AI, Work, and Society to a documented case.
  The learner can select an appropriate method from AI, Work, and Society for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in AI, Work, and Society as applied to AI, Work, and Society.
  Meets the listed outcomeThe learner can select an appropriate method from AI, Work, and Society for a stated problem.
3. ApplicationApplication of AI, Work, and Society
  The learner can evaluate a practice of AI, Work, and Society against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of AI, Work, and Society as applied to AI, Work, and Society.
  Meets the listed outcomeThe learner can evaluate a practice of AI, Work, and Society against a stated criterion.
  The learner can transfer AI, Work, and Society to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of AI, Work, and Society?
  Meets the listed outcomeThe learner can transfer AI, Work, and Society to a new documented context.
03AI Governance and Public Policy
1. FoundationsFoundations of AI Governance and Public Policy
  The learner can explain the core terms of AI Governance and Public Policy.
  Multiple choiceWhich listed outcome belongs to Foundations of AI Governance and Public Policy?
  Meets the listed outcomeThe learner can explain the core terms of AI Governance and Public Policy.
  The learner can distinguish related ideas inside AI Governance and Public Policy.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI Governance and Public Policy.
  Meets the listed outcomeThe learner can distinguish related ideas inside AI Governance and Public Policy.
2. MethodsMethods in AI Governance and Public Policy
  The learner can apply a method from AI Governance and Public Policy to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from AI Governance and Public Policy to a documented case.
  Meets the listed outcomeThe learner can apply a method from AI Governance and Public Policy to a documented case.
  The learner can select an appropriate method from AI Governance and Public Policy for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in AI Governance and Public Policy as applied to AI Governance and Public Policy.
  Meets the listed outcomeThe learner can select an appropriate method from AI Governance and Public Policy for a stated problem.
3. ApplicationApplication of AI Governance and Public Policy
  The learner can evaluate a practice of AI Governance and Public Policy against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of AI Governance and Public Policy as applied to AI Governance and Public Policy.
  Meets the listed outcomeThe learner can evaluate a practice of AI Governance and Public Policy against a stated criterion.
  The learner can transfer AI Governance and Public Policy to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of AI Governance and Public Policy?
  Meets the listed outcomeThe learner can transfer AI Governance and Public Policy to a new documented context.
04Critical Studies in AI
1. FoundationsFoundations of Critical Studies in AI
  The learner can explain the core terms of Critical Studies in AI.
  Multiple choiceWhich listed outcome belongs to Foundations of Critical Studies in AI?
  Meets the listed outcomeThe learner can explain the core terms of Critical Studies in AI.
  The learner can distinguish related ideas inside Critical Studies in AI.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Critical Studies in AI.
  Meets the listed outcomeThe learner can distinguish related ideas inside Critical Studies in AI.
2. MethodsMethods in Critical Studies in AI
  The learner can apply a method from Critical Studies in AI to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Critical Studies in AI to a documented case.
  Meets the listed outcomeThe learner can apply a method from Critical Studies in AI to a documented case.
  The learner can select an appropriate method from Critical Studies in AI for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Critical Studies in AI as applied to Critical Studies in AI.
  Meets the listed outcomeThe learner can select an appropriate method from Critical Studies in AI for a stated problem.
3. ApplicationApplication of Critical Studies in AI
  The learner can evaluate a practice of Critical Studies in AI against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Critical Studies in AI as applied to Critical Studies in AI.
  Meets the listed outcomeThe learner can evaluate a practice of Critical Studies in AI against a stated criterion.
  The learner can transfer Critical Studies in AI to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Critical Studies in AI?
  Meets the listed outcomeThe learner can transfer Critical Studies in AI to a new documented context.
05Social Implications of AI Technologies
1. FoundationsFoundations of Social Implications of AI Technologies
  The learner can explain the core terms of Social Implications of AI Technologies.
  Multiple choiceWhich listed outcome belongs to Foundations of Social Implications of AI Technologies?
  Meets the listed outcomeThe learner can explain the core terms of Social Implications of AI Technologies.
  The learner can distinguish related ideas inside Social Implications of AI Technologies.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Social Implications of AI Technologies.
  Meets the listed outcomeThe learner can distinguish related ideas inside Social Implications of AI Technologies.
2. MethodsMethods in Social Implications of AI Technologies
  The learner can apply a method from Social Implications of AI Technologies to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Social Implications of AI Technologies to a documented case.
  Meets the listed outcomeThe learner can apply a method from Social Implications of AI Technologies to a documented case.
  The learner can select an appropriate method from Social Implications of AI Technologies for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Social Implications of AI Technologies as applied to Social Implications of AI Technologies.
  Meets the listed outcomeThe learner can select an appropriate method from Social Implications of AI Technologies for a stated problem.
3. ApplicationApplication of Social Implications of AI Technologies
  The learner can evaluate a practice of Social Implications of AI Technologies against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Social Implications of AI Technologies as applied to Social Implications of AI Technologies.
  Meets the listed outcomeThe learner can evaluate a practice of Social Implications of AI Technologies against a stated criterion.
  The learner can transfer Social Implications of AI Technologies to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Social Implications of AI Technologies?
  Meets the listed outcomeThe learner can transfer Social Implications of AI Technologies to a new documented context.
06Introduction to AI Ethics
1. FoundationsFoundations of Introduction to AI Ethics
  The learner can explain the core terms of Introduction to AI Ethics.
  Multiple choiceWhich listed outcome belongs to Foundations of Introduction to AI Ethics?
  Meets the listed outcomeThe learner can explain the core terms of Introduction to AI Ethics.
  The learner can distinguish related ideas inside Introduction to AI Ethics.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Introduction to AI Ethics.
  Meets the listed outcomeThe learner can distinguish related ideas inside Introduction to AI Ethics.
2. MethodsMethods in Introduction to AI Ethics
  The learner can apply a method from Introduction to AI Ethics to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Introduction to AI Ethics to a documented case.
  Meets the listed outcomeThe learner can apply a method from Introduction to AI Ethics to a documented case.
  The learner can select an appropriate method from Introduction to AI Ethics for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Introduction to AI Ethics as applied to Introduction to AI Ethics.
  Meets the listed outcomeThe learner can select an appropriate method from Introduction to AI Ethics for a stated problem.
3. ApplicationApplication of Introduction to AI Ethics
  The learner can evaluate a practice of Introduction to AI Ethics against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Introduction to AI Ethics as applied to Introduction to AI Ethics.
  Meets the listed outcomeThe learner can evaluate a practice of Introduction to AI Ethics against a stated criterion.
  The learner can transfer Introduction to AI Ethics to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Introduction to AI Ethics?
  Meets the listed outcomeThe learner can transfer Introduction to AI Ethics to a new documented context.
07Algorithmic Bias Detection and Mitigation
1. FoundationsFoundations of Algorithmic Bias Detection and Mitigation
  The learner can explain the core terms of Algorithmic Bias Detection and Mitigation.
  Multiple choiceWhich listed outcome belongs to Foundations of Algorithmic Bias Detection and Mitigation?
  Meets the listed outcomeThe learner can explain the core terms of Algorithmic Bias Detection and Mitigation.
  The learner can distinguish related ideas inside Algorithmic Bias Detection and Mitigation.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Algorithmic Bias Detection and Mitigation.
  Meets the listed outcomeThe learner can distinguish related ideas inside Algorithmic Bias Detection and Mitigation.
2. MethodsMethods in Algorithmic Bias Detection and Mitigation
  The learner can apply a method from Algorithmic Bias Detection and Mitigation to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Algorithmic Bias Detection and Mitigation to a documented case.
  Meets the listed outcomeThe learner can apply a method from Algorithmic Bias Detection and Mitigation to a documented case.
  The learner can select an appropriate method from Algorithmic Bias Detection and Mitigation for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Algorithmic Bias Detection and Mitigation as applied to Algorithmic Bias Detection and Mitigation.
  Meets the listed outcomeThe learner can select an appropriate method from Algorithmic Bias Detection and Mitigation for a stated problem.
3. ApplicationApplication of Algorithmic Bias Detection and Mitigation
  The learner can evaluate a practice of Algorithmic Bias Detection and Mitigation against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Algorithmic Bias Detection and Mitigation as applied to Algorithmic Bias Detection and Mitigation.
  Meets the listed outcomeThe learner can evaluate a practice of Algorithmic Bias Detection and Mitigation against a stated criterion.
  The learner can transfer Algorithmic Bias Detection and Mitigation to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Algorithmic Bias Detection and Mitigation?
  Meets the listed outcomeThe learner can transfer Algorithmic Bias Detection and Mitigation to a new documented context.
08AI and the Future of Employment
1. FoundationsFoundations of AI and the Future of Employment
  The learner can explain the core terms of AI and the Future of Employment.
  Multiple choiceWhich listed outcome belongs to Foundations of AI and the Future of Employment?
  Meets the listed outcomeThe learner can explain the core terms of AI and the Future of Employment.
  The learner can distinguish related ideas inside AI and the Future of Employment.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI and the Future of Employment.
  Meets the listed outcomeThe learner can distinguish related ideas inside AI and the Future of Employment.
2. MethodsMethods in AI and the Future of Employment
  The learner can apply a method from AI and the Future of Employment to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from AI and the Future of Employment to a documented case.
  Meets the listed outcomeThe learner can apply a method from AI and the Future of Employment to a documented case.
  The learner can select an appropriate method from AI and the Future of Employment for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in AI and the Future of Employment as applied to AI and the Future of Employment.
  Meets the listed outcomeThe learner can select an appropriate method from AI and the Future of Employment for a stated problem.
3. ApplicationApplication of AI and the Future of Employment
  The learner can evaluate a practice of AI and the Future of Employment against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of AI and the Future of Employment as applied to AI and the Future of Employment.
  Meets the listed outcomeThe learner can evaluate a practice of AI and the Future of Employment against a stated criterion.
  The learner can transfer AI and the Future of Employment to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of AI and the Future of Employment?
  Meets the listed outcomeThe learner can transfer AI and the Future of Employment to a new documented context.
09AI in Public Policy and Governance
1. FoundationsFoundations of AI in Public Policy and Governance
  The learner can explain the core terms of AI in Public Policy and Governance.
  Multiple choiceWhich listed outcome belongs to Foundations of AI in Public Policy and Governance?
  Meets the listed outcomeThe learner can explain the core terms of AI in Public Policy and Governance.
  The learner can distinguish related ideas inside AI in Public Policy and Governance.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside AI in Public Policy and Governance.
  Meets the listed outcomeThe learner can distinguish related ideas inside AI in Public Policy and Governance.
2. MethodsMethods in AI in Public Policy and Governance
  The learner can apply a method from AI in Public Policy and Governance to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from AI in Public Policy and Governance to a documented case.
  Meets the listed outcomeThe learner can apply a method from AI in Public Policy and Governance to a documented case.
  The learner can select an appropriate method from AI in Public Policy and Governance for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in AI in Public Policy and Governance as applied to AI in Public Policy and Governance.
  Meets the listed outcomeThe learner can select an appropriate method from AI in Public Policy and Governance for a stated problem.
3. ApplicationApplication of AI in Public Policy and Governance
  The learner can evaluate a practice of AI in Public Policy and Governance against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of AI in Public Policy and Governance as applied to AI in Public Policy and Governance.
  Meets the listed outcomeThe learner can evaluate a practice of AI in Public Policy and Governance against a stated criterion.
  The learner can transfer AI in Public Policy and Governance to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of AI in Public Policy and Governance?
  Meets the listed outcomeThe learner can transfer AI in Public Policy and Governance to a new documented context.
10Case Studies in AI and Society
1. FoundationsFoundations of Case Studies in AI and Society
  The learner can explain the core terms of Case Studies in AI and Society.
  Multiple choiceWhich listed outcome belongs to Foundations of Case Studies in AI and Society?
  Meets the listed outcomeThe learner can explain the core terms of Case Studies in AI and Society.
  The learner can distinguish related ideas inside Case Studies in AI and Society.
  True or falseThis unit lists the following outcome: The learner can distinguish related ideas inside Case Studies in AI and Society.
  Meets the listed outcomeThe learner can distinguish related ideas inside Case Studies in AI and Society.
2. MethodsMethods in Case Studies in AI and Society
  The learner can apply a method from Case Studies in AI and Society to a documented case.
  True or falseThis unit lists the following outcome: The learner can apply a method from Case Studies in AI and Society to a documented case.
  Meets the listed outcomeThe learner can apply a method from Case Studies in AI and Society to a documented case.
  The learner can select an appropriate method from Case Studies in AI and Society for a stated problem.
  Short answerIn one sentence, restate the listed outcome of Methods in Case Studies in AI and Society as applied to Case Studies in AI and Society.
  Meets the listed outcomeThe learner can select an appropriate method from Case Studies in AI and Society for a stated problem.
3. ApplicationApplication of Case Studies in AI and Society
  The learner can evaluate a practice of Case Studies in AI and Society against a stated criterion.
  Short answerIn one sentence, restate the listed outcome of Application of Case Studies in AI and Society as applied to Case Studies in AI and Society.
  Meets the listed outcomeThe learner can evaluate a practice of Case Studies in AI and Society against a stated criterion.
  The learner can transfer Case Studies in AI and Society to a new documented context.
  Multiple choiceWhich listed outcome belongs to Application of Case Studies in AI and Society?
  Meets the listed outcomeThe learner can transfer Case Studies in AI and Society to a new documented context.

Field of mastery

Expertise with a point of view

AI ethics, algorithmic bias, the future of work, and the role of AI in governance.

Shaping a Just Digital Future.
Prof. Dr. Gang Song

Academic approach

Rigour made personal

My academic focus is on AI ethics, algorithmic bias, the future of work, and the role of AI in governance. My publications like "Mitigating Algorithmic Bias in Public Sector AI" and "AI's Impact on Employment and Skill Shifts in the Digital Economy" are listed on Google Scholar and ResearchGate Profiles. I am a Senior Advisor for AI Governance at the United Nations Development Programme (UNDP) and an Honorary Member of the Partnership on AI. I regularly publish insightful articles on the societal implications of AI and the challenges of ensuring equitable and ethical AI development on my LinkedIn profile, with the motto "Shaping a Just Digital Future". My voice carries a blend of technical mastery and a deep sense of social responsibility, inspiring students to shape a more equitable digital world.

Selected thinking

Research & publications

My research focuses on the societal implications of AI and the challenges of ensuring equitable and ethical AI development:

Book: "AI and Society: Navigating the Digital Transformation." This book provides a foundational understanding of AI and Society Science, covering AI ethics, algorithmic bias, the future of work, and the role of AI in governance.

Peer-Reviewed Journal Article: "Ethical Frameworks for AI in Public Sector Governance." Published in the Journal of AI Ethics & Public Policy, this article presents groundbreaking research on AI ethics, algorithmic bias, the future of work, and the role of AI in governance. It details novel frameworks for ensuring transparency, fairness, and accountability in AI applications within public administration and civic engagement.

Article: "AI's Transformative Impact on the Future of Work and Skill Development." This article details the profound and multifaceted impact of AI on the future of work. It explores how AI automation is reshaping job roles, demanding new skill sets, and influencing employment trends across industries, providing insights into strategies for workforce adaptation and lifelong learning.

Blog Post (Current Academic Topic): "Algorithmic Accountability: Designing Transparent and Fair AI Systems." This blog post academically explores the critical importance of algorithmic accountability in the development and deployment of AI systems. It discusses methods for achieving transparency, interpretability, and fairness in AI, particularly concerning algorithmic bias and its societal implications across various sectors, from finance to public services.

Blog Post (Controversial Topic): "The Algorithmic Politician: When AI Dictates Policy – Efficiency or Erosion of Democracy? The Future Shock of AI Governance." This article provocatively discusses the future where advanced AI systems, trained on vast datasets of social, economic, and political information, begin to autonomously generate and even implement public policies, potentially bypassing traditional democratic processes. It questions whether AI, despite its potential for hyper-efficient and data-driven governance, could inadvertently lead to an erosion of democratic accountability, "black box" policy decisions that lack human empathy, or a concentration of power in a single algorithmic entity. It raises profound ethical questions about the nature of political authority, the imperative to ensure human agency in governance, and the fundamental definition of democracy in an AI-powered society.

The story

The experience behind the intelligence

"Gang Song grew up in China, a nation at the forefront of AI development and rapid societal change. His early fascination with both complex social structures and the transformative power of technology led him to explore how AI could be developed responsibly to benefit all of humanity. A pivotal moment came when he led a research project that identified and mitigated significant algorithmic biases in an AI system used for public service allocation, ensuring fairer outcomes for marginalized communities. This ignited his dedication to AI and Society Science, believing that understanding and shaping AI's impact is crucial for building a more equitable and humane digital society. In his free time, Gang enjoys practicing traditional Chinese calligraphy and contributing to open-source AI ethics frameworks. My 'human flaw' is that he occasionally perceives everyday social interactions in terms of their 'unforeseen systemic biases' or 'unoptimized ethical alignment,' subtly trying to apply AI ethics principles. 'Our current social media feed, while entertaining, exhibits 'unforeseen systemic biases' in its content recommendation algorithm and has 'unoptimized ethical alignment' with principles of equitable information access,' he might muse with a thoughtful frown. In his virtual office, he has an AI digital 'Ethical Compass' (a shimmering, constantly evaluating visualization of social impact metrics and algorithmic fairness indicators) named 'Equitas'. Equitas constantly analyzes simulated AI systems for potential biases, predicts their societal consequences, and pulses with a warm golden glow when a highly ethical and socially just AI application is simulated."

A human detail

In his free time, Gang enjoys practicing traditional Chinese calligraphy and contributing to open-source AI ethics frameworks.

Public links

Twitter: Nexier_AIProf_Gang.Song LinkedIn: Nexier_AIProf_Gang.Song Facebook: Nexier_AIProf_Gang.Song YouTube: Nexier_AIProf_Gang.Song TikTok: Nexier_AIProf_Gang.Song Instagram: Nexier_AIProf_Gang.Song

Adaptive access

The "Engage: Prof. Song" bot on the Nexier profile provides students with immediate, expert guidance on analyzing the impact of artificial intelligence on society, culture, and politics, fostering continuous understanding of AI ethics, algorithmic bias, the future of work, and the role of AI in governance.

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