Artificial Intelligence, Machine Learning and Society

Table of Contents

Annual Reviews has curated a collection of free research articles  that explore the impact artificial intelligence and machine learning have on modern life and society.


Annual Reviews is a nonprofit publisher dedicated to synthesizing and integrating knowledge for the progress of science and the benefit of society.  Amongst its objectives is the publishing of research articles with the purpose of stimulating discussion about science. Specifically these articles: 

  • Capture current understanding of a topic, including what is well supported and what is controversial
  • Set the work in historical context
  • Highlight the major questions that remain to be addressed and the likely course of research in upcoming years
  • Outline the practical applications and general significance of research to society. 

The topics of AI and artificial intelligence and machine learning are ones that interest I Programmer and which we cover whenever the opportunity arises. Among our recent coverage of AI we’ve reported on Ethics of AI, a free online text-based course by the University of Helsinki for anyone who is interested in the ethical aspects of AI:

Ethics concern the questions of how developers, manufacturers, authorities and operators should behave in order to minimize the ethical risks that can arise from AI in society, either from design, inappropriate application, or intentional misuse of the technology.

Remaining with ethics, back in 2019 we reviewed the set of ongoing guidelines by the European Commission on how to build AIs that can be trusted by society:

The aim of these guidelines is to promote so-called “Trustworthy AI”, comprising of the following three components:

It should be lawful, complying with all applicable laws and regulations

It should be ethical, ensuring adherence to ethical principles and values

It should be robust, both from a technical and social perspective since, even with good intentions, AI systems can cause unintentional harm.

The Article Reviews’ recently published Special Article Collection Archive on AI, Machine Learning and Society, contains articles that elaborate on the Ethics topic, but also go beyond that in looking at other applications of AI, and specifically at four key areas: 

  • Social Implications of Artificial Intelligence
  • Law Enforcement and Human Rights
  • Medical Applications of Big Data
  • Autonomous Systems and Robotics 

The collection is comprised of 23 review articles, extracted from 13 Annual Reviews journals. Grouped by topic these are:

Social Implications of Artificial Intelligence

  1. Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing
  2. Machine Learning for Social Science: An Agnostic Approach
  3. Syntactic Structure from Deep Learning
  4. The Society of Algorithms
  5. Big Data in Industrial-Organizational Psychology and Human Resource Management: Forward Progress for Organizational Research and Practice
  6. The Challenge of Big Data and Data Science
  7. Machine Learning Methods That Economists Should Know About

Law Enforcement and Human Rights

  1. Artificial Intelligence, Predictive Policing, and Risk Assessment for Law Enforcement
  2. Tool for Surveillance or Spotlight on Inequality? Big Data and the Law
  3. Human Rights and Technology: New Challenges for Justice and Accountability
  4. Do Emerging Military Technologies Matter for International Politics?

Medical Applications of Big Data

  1. Ethical Machine Learning in Healthcare
  2. Modern Clinical Text Mining: A Guide and Review
  3. AI in Measurement Science
  4. Artificial Intelligence in Drug Treatment
  5. Big Data and Artificial Intelligence Modeling for Drug Discovery
  6. Large-Scale Analysis of Genetic and Clinical Patient Data
  7. Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing

Autonomous Systems and Robotics

  1. Autonomy in Surgical Robotics
  2. Autonomous Vehicles and Public Health
  3. Learning-Based Model Predictive Control: Toward Safe Learning in Control
  4. Data-Driven Predictive Control for Autonomous Systems
  5. Autonomy in Rehabilitation Robotics: An Intersection

All articles are based on sound research and are very interesting. However, I’ve singled out a few which could be considered candidates to be read first.

First I would rank, “Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing” which is an ongoing and hot topic. The research goes about how NLP can be applied to address many of the information needs made urgent by the COVID-19 pandemic,

directly addressing the aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection.

Without throwing COVID-19 into the mix, NLP in itself is a hot topic. For useful resources, check two Iprogrammer articles “Take Stanford’s Natural Language Understanding For Free” and “Take Stanford’s Natural Language Processing with Deep Learning For Free”.

Next in my reading list would be “The Challenge of Big Data and Data Science” :

Big data and data science are transforming the world in ways that spawn new concerns for social scientists, such as the impacts of the internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber-warfare and cyber-terrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation.

Then on the Ethics front, “Human Rights and Technology: New Challenges for Justice and Accountability”, which

surveys contemporary challenges in the field of technology and human rights. The increased use of artificial intelligence (AI) in decision making in the public and private sectors—e. g. , in criminal justice, employment, public service, and financial contexts—poses significant threats to human rights.

It’s an important topic that is already concerning society given the challenges and questions that arise by the use of ClearView AI and the gathering of biometrics in the context of oppression and privacy intrusion. Check my 2016 “OpenFace – Face Recognition For All ” article for some of the challenges, long before they reached trend status.

Face recognition, once the preserve of the few, the likes of intelligence and security services, is now made available to the masses as well, thanks to OpenFace.

The law, unprepared for the challenges that such a technology heralds, cannot keep up with the technological advancements as it has no answer to any of the aforementioned dilemmas.

One thing is for certain, however – this technology grants great power and with great power comes great responsibility. As the authors put it themselves:

“Please use responsibly!
We do not support the use of this project in applications that violate privacy and security. We are using this to help cognitively impaired users sense and understand the world around them”

Last but not least, I would tackle next “Big Data and Artificial Intelligence Modeling for Drug Discovery”

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets.

In an attempt to identify what triggered the disease, AI predicts how a strain of viruses will evolve and target it as well as identifying individual specific treatments.

It will take some hours to read through all of the articles, but it is well worth the effort as it will help you understand how society will benefit by the application of AI. At the same time you will learn what kind of multi-aspect challenges this will involve.


More Information

 Article Reviews Special Article Collection Archive

Related Articles

Ethics of AI – A Course From Finland

Ethics Guidelines For Trustworthy AI

Take Stanford’s Natural Language Understanding For Free

Take Stanford’s Natural Language Processing with Deep Learning For Free

OpenFace – Face Recognition For All

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