The 2nd edition of the Montreal AI Ethics Institute's The State of AI Ethics captures the most relevant developments in the field of AI Ethics since July 2020. This report aims to help anyone, from machine learning experts to human rights activists and policymakers, quickly digest and understand the ever-changing developments in the field. Through research and article summaries, as well as expert commentary, this report distills the research and reporting surrounding various domains related to the ethics of AI, including: AI and society, bias and algorithmic justice, disinformation, humans and AI, labor impacts, privacy, risk, and future of AI ethics. In addition, The State of AI Ethics includes exclusive content written by world-class AI Ethics experts from universities, research institutes, consulting firms, and governments. These experts include: Danit Gal (Tech Advisor, United Nations), Amba Kak (Director of Global Policy and Programs, NYU's AI Now Institute), Rumman Chowdhury (Global Lead for Responsible AI, Accenture), Brent Barron (Director of Strategic Projects and Knowledge Management, CIFAR), Adam Murray (U.S. Diplomat working on tech policy, Chair of the OECD Network on AI), Thomas Kochan (Professor, MIT Sloan School of Management), and Katya Klinova (AI and Economy Program Lead, Partnership on AI). This report should be used not only as a point of reference and insight on the latest thinking in the field of AI Ethics, but should also be used as a tool for introspection as we aim to foster a more nuanced conversation regarding the impacts of AI on the world.

New technologies have always thrown up new questions about Intellectual Property. Whether that's the printing press revolution, the invention of recorded music, or the advent of the internet. Artificial Intelligence is no different. Over the past ten years AI technologies have accelerated. I've seen for myself the incredible impact they're having across a huge range of sectors – from medicine to manufacturing.

In recent years, some astonishing technological breakthroughs in the field artificial intelligence (AI) and its sub-field deep learning have begun to train machines to behave like humans. As machines are increasingly emulating complex cognitive functions such as deductive reasoning, inferences, and informed decision-making, robots functioning as humans are a reality in many industry practices today. However, machines are still behind in articulating the reasons behind their choices or actions. In other words, a machine witness still cannot be used in a court of law to solve a case as it cannot "justify" past actions. The noteworthy achievements in AI applications include the inclusion of neural networks and deep learning (DL), which combine unique training opportunities for machines to learn from layers of knowledge, and then to apply that knowledge to achieve particular goals.

I use one of those credit monitoring services that regularly emails me about my credit score: "Congratulations, your score has gone up!" "Uh oh, your score has gone down!" I shrug and delete the emails. Credit scores are just one example of the many automated decisions made about us as individuals on the basis of complex models. I don't know exactly what causes those little changes in my score. Some machine learning models are "black boxes," a term often used to describe models whose inner workings -- the ways different variables ended up related to one another by an algorithm -- may be impossible for even their designers to completely interpret and explain.

In response to a growing demand for air quality monitoring and carbon control solutions, the aim is to develop an intelligent air quality monitoring, analytic, and carbon trading platform. It will allow the measuring and forecasting of air quality status, pollution levels, and providing timely scientific evidence that can be used for carbon trading. The system is optimized for speedy deployment with minimal additional infrastructure investment. Compared to a traditional monitoring system, the system has the characteristics of low-cost, installation portability, and easy information access with comprehensive air quality measuring sensors for PM2.5, CO2, NO2, PM10, VOCs, etc., it also explores the feasibility of linking carbon dioxide emission directly to a carbon trading platform for carbon control and greenhouse gas emission reduction. The system will be built on the combined advantages provided by three networks.

We provide a comprehensive analysis of the computational complexity of the outcome determination problem for the most important aggregation rules proposed in the literature on logic-based judgment aggregation. Judgment aggregation is a powerful and flexible framework for studying problems of collective decision making that has attracted interest in a range of disciplines, including Legal Theory, Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing the outcome for a given list of individual judgments to be aggregated into a single collective judgment is the most fundamental algorithmic challenge arising in this context. Our analysis applies to several different variants of the basic framework of judgment aggregation that have been discussed in the literature, as well as to a new framework that encompasses all existing such frameworks in terms of expressive power and representational succinctness.

To the layman, the word-set of ethical AI is a misnomer. AI oftentimes still conjures visions of a dystopian future in which artificial intelligence runs rampant, dominating humankind. Thanks to modern-era entertainment in films such as 2001:A Space Odyssey (HAL 3000) or The Terminator, public perception of AI has been limited to these fictional depictions. So it should come as no surprise that when we talk about ethical AI people would assume its inverse involves robots, lasers, and a war to end humanity. In truth, the conversation around ethical AI typically boils down to the societal issues such as data collection, cyberattacks on critical infrastructure, and inherent bias in code.

When Black Lives Matter protests were raging following the death of George Floyd, the publishers of the tabletop role-playing game Dungeons & Dragons, pledged to take concrete steps to make their games more diverse. Wizards of the Coast promised to "share what we've been doing, and what we plan to do in the future to address legacy D&D content that does not reflect who we are today". In addition, it also pulled several racist cards from the card game Magic: The Gathering, such as Invoke Prejudice, Jihad and Pradesh Gypsies. Is it a coincidence that D&D's dishonourable, dark-skinned elves come from a matriarchal society, or that its savage orcs bear uncanny resemblance to a traditionally white, western conceptualisation of barbaric peoples from the "uncivilised" world? Although fantasy affords us every freedom to imagine new worlds and cultures, for the last 200-odd years, humans have mostly managed derivative facsimiles of our own.

How could software designed to take the bias out of decision making, to be as objective as possible, produce these kinds of outcomes? After all, the purpose of artificial intelligence is to take millions of pieces of data and from them make predictions that are as error-free as possible. But as AI has become more pervasive--as companies and government agencies use AI to decide who gets loans, who needs more health care and how to deploy police officers, and more--investigators have discovered that focusing just on making the final predictions as error free as possible can mean that its errors aren't always distributed equally. Instead, its predictions can often reflect and exaggerate the effects of past discrimination and prejudice. In other words, the more AI focused on getting only the big picture right, the more it was prone to being less accurate when it came to certain segments of the population--in particular women and minorities.

It's been three months since the world was shaken by the brutal murder of George Floyd. The image of a white police officer kneeling on a black citizen for 8 minutes and 46 seconds are still fresh in America's collective memory. And unfortunately, it won't be the last one either. Racism in this country has deep roots. It is a festering wound that's either left ignored or treated with an infective medicine.