Are your AI Algorithms locked down and safe? In my prior Forbes blogs, the business imperative for Board Directors and CEOs to advance their governance practices to lead forward with AI was framed. This blog shared the insights from a recent interview with Cathy Cobey, the EY global trusted AI leader, where we explore: how practicing responsible AI is stacking up, the impact of data bias and key board director questions to ensure CEO's are managing the new risks that AI presents. One of the key insights Cathy shared is that from all of her global client interactions to date, she has yet to find any organization, large or small, which has a robust inventory management process to easily identify or source their AI models. This also mirrors my global research that Board Directors and CEO's don't know where their AI algorithms are.

Our good friend and host of Digital Detectives and the Digital Edge, Sharon Nelson from Sensei Enterprises joins the show to talk about some AI-enabled crimes such as deepfakes, fake news, driverless vehicles, and more. Thank you to our sponsor NBI.

Artificial intelligence (AI) has become prevalent in our everyday technologies and impacts both individuals and communities. The explainable AI (XAI) scholarship has explored the philosophical nature of explanation and technical explanations, which are usually driven by experts in lab settings and can be challenging for laypersons to understand. In addition, existing XAI research tends to focus on the individual level. Little is known about how people understand and explain AI-led decisions in the community context. Drawing from XAI and activity theory, a foundational HCI theory, we theorize how explanation is situated in a community's shared values, norms, knowledge, and practices, and how situated explanation mediates community-AI interaction. We then present a case study of AI-led moderation, where community members collectively develop explanations of AI-led decisions, most of which are automated punishments. Lastly, we discuss the implications of this framework at the intersection of CSCW, HCI, and XAI.

This primer explores the exciting subject of intelligence. Intelligence is a fundamental component of all living things, as well as Artificial Intelligence(AI). Artificial Intelligence has the potential to affect all of our lives and a new era for modern humans. This paper is an attempt to explore the ideas associated with intelligence, and by doing so understand the implications, constraints, and potentially the capabilities of future Artificial Intelligence. As an exploration, we journey into different parts of intelligence that appear essential. We hope that people find this useful in determining where Artificial Intelligence may be headed. Also, during the exploration, we hope to create new thought-provoking questions. Intelligence is not a single weighable quantity but a subject that spans Biology, Physics, Philosophy, Cognitive Science, Neuroscience, Psychology, and Computer Science. Historian Yuval Noah Harari pointed out that engineers and scientists in the future will have to broaden their understandings to include disciplines such as Psychology, Philosophy, and Ethics. Fiction writers have long portrayed engineers and scientists as deficient in these areas. Today, modern society, the emergence of Artificial Intelligence, and legal requirements all act as forcing functions to push these broader subjects into the foreground. We start with an introduction to intelligence and move quickly onto more profound thoughts and ideas. We call this a Life, the Universe and Everything primer, after the famous science fiction book by Douglas Adams. Forty-two may very well be the right answer, but what are the questions?

Most recommender systems (RS) research assumes that a user's utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In realistic settings, this is often not true---the dynamics of an RS ecosystem couple the long-term utility of all agents. In this work, we explore settings in which content providers cannot remain viable unless they receive a certain level of user engagement. We formulate the recommendation problem in this setting as one of equilibrium selection in the induced dynamical system, and show that it can be solved as an optimal constrained matching problem. Our model ensures the system reaches an equilibrium with maximal social welfare supported by a sufficiently diverse set of viable providers. We demonstrate that even in a simple, stylized dynamical RS model, the standard myopic approach to recommendation---always matching a user to the best provider---performs poorly. We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.

A major question that has generally been unaddressed involves how we will know when AILR has achieved Artificial Intelligence (AI) is increasingly being autonomous capacities. So far, AI as applied to the applied to law and a myriad of legal tasks amid legal profession has primarily consisted of aiding or attempts to bolster AI Legal Reasoning (AILR) supporting the legal work of human lawyers but has autonomous capabilities. A major question that has not reached the capability of being able to generally been unaddressed involves how we will autonomously perform legal tasks. A base assumption know when AILR has achieved autonomous is that inexorably there will be advances made in AI capacities. The field of AI has grappled with similar that will boost AILR systems and ultimately transcend quandaries over how to assess the attainment of them into having autonomous capacities, but there Artificial General Intelligence (AGI), a persistently does not yet exist any bona fide and nor rigorous discussed issue among scholars since the inception of means to viably attest to whether such AILR AI, with the Turing Test communally being considered autonomy has been achieved [44].

At Zetane (Trademark) we're looking for passionate contributors who want to commit to a startup and make a contribution to our mission, "to drive the democratization, access and explainability of AI in industry and facilitate collaboration between subject matter experts and AI specialists." Zetane Systems is a Montreal-region software technology company specializing in commercial applications of artificial intelligence (AI). Zetane's AI development engine is proprietary software that provides a visual, intuitive and collaborative environment for AI teams in businesses to build AI solutions and products based on machine learning models. We aim to make the development of AI solutions more accessible to the various stakeholders in businesses and promote abilities to explain the inner workings of complex neural networks to address "black box" concerns, increase stakeholder buy-in, reduce time to market and mitigate risk prior to deployment. In addition to selling powerful software, the company also provides a full suite of consulting services to facilitate the adoption of AI solutions in diverse industries.

Civil rights campaigners in the UK have won a legal challenge to South Wales Police's (SWP) use of facial recognition technology. The win on appeal is being hailed as a "world-first" victory in the fight against the use of an "oppressive surveillance tool", as human rights group Liberty puts it. However the police force does not intend to appeal the ruling -- and has said it remains committed to "careful" use of the tech. The back story here is SWP has been trialing automated facial recognition (AFR) technology since 2017, deploying a system known as AFR Locate on around 50 occasions between May 2017 and April 2019 at a variety of public events in Wales. The force has used the technology in conjunction with watchlists of between 400-800 people -- which included persons wanted on warrants; persons who had escaped from custody; persons suspected of having committed crimes; persons who may be in need of protection; vulnerable persons; persons of possible interest to it for intelligence purposes; and persons whose presence at a particular event causes particular concern, per a press summary issued by the appeals court.

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IN HER first semester as a graduate student at the MIT Media Lab, Joy Buolamwini encountered a peculiar problem. Commercial face-recognition software, which detected her light-skinned classmates just fine, couldn't "see" her face. Until, that is, she donned a white plastic mask in frustration. Coded Bias is a timely, thought-provoking documentary from director Shalini Kantayya. It follows Buolamwini's journey to uncover racial and sexist bias in face-recognition software and other artificial intelligence systems.