These past few months have been especially challenging, and the deployment of technology in ways hitherto untested at an unrivalled pace has left the internet and technology watchers aghast. Artificial intelligence has become the byword for technological progress and is being used in everything from helping us combat the COVID-19 pandemic to nudging our attention in different directions as we all spend increasingly larger amounts of time online. It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions. This pulse-check for the state of discourse, research, and development is geared towards researchers and practitioners alike who are making decisions on behalf of their organizations in considering the societal impacts of AI-enabled solutions. We cover a wide set of areas in this report spanning Agency and Responsibility, Security and Risk, Disinformation, Jobs and Labor, the Future of AI Ethics, and more. Our staff has worked tirelessly over the past quarter surfacing signal from the noise so that you are equipped with the right tools and knowledge to confidently tread this complex yet consequential domain.

As the industry grapples with how to reopen for production safely, one movie is proceeding with a lead actress who is immune to COVID-19 -- because she's a robot named Erica. Bondit Capital Media, which financed titles such as To the Bone and the Oscar nominated Loving Vincent, Belgium-based Happy Moon Productions and New York's Ten Ten Global Media have committed to back b, a $70 million science fiction film which producers say will be the first to rely on an artificially intelligent actor. Based on a story by visual effects supervisor Eric Pham, Tarek Zohdy, and Sam Khoze, who also produces through Life Entertainment, b follows a scientist who discovers dangers associated with a program he created to perfect human DNA and helps the artificially intelligent woman he designed (Erica) escape. Japanese scientists Hiroshi Ishiguro and Kohei Ogawa, who created Erica in real life as part of their study of robotics, also taught her to act, applying the principles of method acting to artificial intelligence, according to Khoze. "In other methods of acting, actors involve their own life experiences in the role," Khoze says.

In 2020, people benefit from artificial intelligence every day: music recommender systems, Google maps, Uber, and many more applications are powered with AI. One of popular Google search requests goes as follows: "are artificial intelligence and machine learning the same thing?". Let's clear things up: artificial intelligence (AI), machine learning (ML), and deep learning (DL) are three different things. The term artificial intelligence was first used in 1956, at a computer science conference in Dartmouth. AI described an attempt to model how the human brain works and, based on this knowledge, create more advanced computers. The scientists expected that to understand how the human mind works and digitalize it shouldn't take too long.

But with machine learning, FPGAs are showing benefits beyond those of more conventional solutions.

In October 2017, Facebook altered the Instagram API to make it harder for users to search its giant database of photos. The change was a small element of the company's response to the Cambridge Analytica scandal, but it was a significant problem for parts of the digital marketing industry. Not long before, New York-based influencer marketing agency Amra & Elma had developed a platform that ingested data from Instagram, and allowed its client to use AI image classifiers to find very specific influencers. For instance, they could find an influencer with, say, between 10,000 and 50,000 followers who had posted photos of themselves in a Jeep. Facebook's move killed this capability in a keystroke.

Facebook groups are ripe targets for bad actors, for people who want to spread misleading, wrong or dangerous information. And in a recent opinion column in WIRED magazine, she and a co-author write that the company's so-called pivot to privacy, Facebook's promise to protect sensitive user information, did little to combat the spread of misinformation. Instead, Facebook encouraged users to join its groups, which are private pages for users with similar interests. We want to note here that Facebook is among NPR's recent financial supporters. Nina Jankowicz is here with us now.

Apple's latest Watch update encourage users to wash their hands properly by showing them a 20-second timer on their wrist while they are doing it. The firm announced the new feature at their annual Worldwide Developer Conference on Monday alongside a spate of other updates. Apple said washing hands properly for at least 20 seconds can help prevent the spread of illnesses, such as the deadly coronavirus that put the world in lockdown. It uses the motion sensors, microphones and machine learning to detect when someone starts washing their hands then initiates a 20-second countdown timer. Other new features announced for the wearable device include the ability to swap Watch faces, dance tracking in the fitness app and sleep monitoring.

Online influence maximization has attracted much attention as a way to maximize influence spread through a social network while learning the values of unknown network parameters. Most previous works focus on single-item diffusion. In this paper, we introduce a new Online Competitive Influence Maximization (OCIM) problem, where two competing items (e.g., products, news stories) propagate in the same network and influence probabilities on edges are unknown. We adapt the combinatorial multi-armed bandit (CMAB) framework for the OCIM problem, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem. We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds, and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases. We propose an OCIM-OIFU algorithm with such an oracle that achieves logarithmic regret. We also design an OCIM-ETC algorithm that has worse regret bound but requires less feedback and easier offline computation. Our experimental evaluations demonstrate the effectiveness of our algorithms.