ARTIFICIAL INTELLIGENCE

ANNOTATED BIBLIOGRAPHY

Artificial intelligence research

Artificial Intelligence Annotated

Bibliography

Foundations, machine learning, modern architectures, responsible AI, and the challenges of

deployment

RESEARCH LENS

The sources below treat artificial intelligence as a multidisciplinary field: foundational methods explain

core AI systems, machine learning explains data-driven learning, modern architecture research shows

how capabilities have advanced, and responsible-AI research addresses risks and practical

governance.

Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. (2021). On

the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021

ACM Conference on Fairness, Accountability, and Transparency, 610-623.

https://doi.org/10.1145/3442188.3445922

ANNOTATION

Bender and colleagues examine the costs and risks that accompany the rapid scaling of large

language models. The paper argues that technical performance cannot be considered separately

from training data, labor, environmental costs, and the social contexts in which systems are

deployed. It is useful for artificial intelligence research because it connects model development with

questions of accountability and documentation. As a refereed conference paper, it provides a

concrete scholarly basis for discussing limitations and risks in contemporary AI systems.

Mitchell, T. M. (1997). Machine learning. McGraw-Hill.

ANNOTATION

Mitchell provides a foundational treatment of machine learning as a central area of artificial

intelligence. The book explains how algorithms learn from data and covers concept learning,

decision trees, neural networks, Bayesian learning, reinforcement learning, and related methods.

Its value is conceptual as well as technical: it gives readers a framework for understanding how

learning systems differ from programs that rely only on explicitly coded rules. This source is

particularly useful for establishing the relationship between AI and machine learning in academic

research.

ARTIFICIAL INTELLIGENCE

ANNOTATED BIBLIOGRAPHY

Artificial intelligence research

Russell, S. J., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.

ANNOTATION

Russell and Norvig present artificial intelligence as a broad field that includes intelligent agents,

search, knowledge representation, reasoning, planning, machine learning, natural language

processing, robotics, and ethics. The fourth edition is useful for a general AI bibliography because it

connects foundational methods with modern applications instead of focusing on one technique. Its

breadth makes it a strong starting point for defining the field and locating more specialized

research. The authors are established computer science researchers, and the text is widely used in

university AI courses.

Tabassi, E. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National

Institute of Standards and Technology. https://doi.org/10.6028/NIST.AI.100-1

ANNOTATION

Tabassi presents NIST's framework for helping organizations identify, measure, and manage risks

associated with artificial intelligence systems. The framework treats trustworthy AI as a practical

governance and engineering concern and organizes risk-management work around functions that

can be applied across the AI lifecycle. This report broadens an AI bibliography beyond algorithms

by showing how evaluation, accountability, and risk controls fit into real-world deployment. It is

especially useful for research on responsible AI because it was issued by NIST and written by the

report's listed author, providing a traceable institutional source.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin,

I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30

(pp. 5998-6008). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2017/hash/3f5ee

243547dee91fbd053c1c4a845aa-Abstract.html

ANNOTATION

Vaswani and colleagues introduce the Transformer architecture, replacing recurrence and

convolution with attention mechanisms for sequence transduction. The paper explains how the

architecture can improve parallelization and training efficiency while producing strong results on

machine-translation tasks. Its importance to an AI bibliography comes from showing how a specific

architectural innovation reshaped a major area of modern AI research. The paper also provides a

useful bridge between broad discussions of artificial intelligence and the technical development of

contemporary language systems.

Artificial Intelligence Annotated Bibliography

Prompt to Clone This Artificial Intelligence Annotated Bibliography

Apply this example's core structure, strategic pacing, and structural hierarchy directly to your own topic.

What are you generating today?

MLA Annotated Bibliography

Chicago Style Annotated Bibliography

Climate Change Annotated Bibliography

Social Media Annotated Bibliography