A 2019 survey found that 73 of 84 prominent AI strategy documents referenced transparency or explainability [81]. Influential intergovernmental bodies such as United Nations agencies and the Organization for Economic Cooperation and Development (OECD) have put forth transparency and explainability as key mechanisms for ensuring that algorithmic systems produce beneficial outcomes and uphold "democratic values" [121, 143]. Algorithmic transparency and explainability can serve many purposes, but some of the most important are legal in nature: allowing lawmakers to understand and craft effective rules for algorithmic systems, enabling a broader set of stakeholders to be aware of (and obtain redress from) algorithmic harms, and assisting regulators in exercising meaningful oversight over the use of algorithms [81, 109]. To serve these objectives, transparency measures and explanation techniques must be developed with an understanding of the specific goals, constraints, and incentives of policymakers. This paper aims to help bridge the gap between policymakers and the explanation research community, helping researchers to better understand and respond to the needs of policymakers. To this end, it provides case studies illustrating five uses for algorithmic transparency and explanation in policy settings. These case studies (Table 1) were selected to span four axes: the spectrum from explanation to transparency (including both requirements for specific explanation techniques, like those developed by the machine learning research community, and broader forms of transparency requirements); different jurisdictions (including U.S. federal regulators, U.S. states, and the EU); policy actors with differing technical and financial capacities; and a diverse array of policy approaches (including prescriptive technical rules, process-oriented rules, nonbinding guidelines, and modifications to legal procedures). Building on these case studies, this paper argues that explanation techniques developed by the research community can be too complex, too uncertain, or too restricted to satisfy the constraints that policymakers and the law operate under in practice. As a result, explanation is often limited in its ability to enable meaningful public policy solutions to algorithmic harms.

Artificial intelligence (AI) is rapidly changing the way we live, work, and interact with each other. It has the potential to improve healthcare, education, transportation, and other critical areas of society. However, the development and use of AI raise ethical concerns about privacy, bias, accountability, and transparency. How do we ensure good ethics in building AI platforms? In this blog, we will explore some strategies for building AI platforms with ethical principles.

Abstract: Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the standard approach is a comparison of models using the model evidence or the Bayes factor. However, in all but the simplest of cases, direct computation of these quantities is impossible. Markov Chain Monte Carlo approaches are widely used, such as sequential Monte Carlo, but it is not always clear how well such techniques perform in practice.

Did you miss a session at the Data Summit? If data is the new oil of the digital economy, artificial intelligence (AI) is the steam engine. Companies that take advantage of the power of data and AI hold the key to innovation -- just as oil and steam engines fueled transportation and, ultimately, the Industrial Revolution. In 2022, data and AI have set the stage for the next chapter of the digital revolution, increasingly powering companies across the globe. How can companies ensure that responsibility and ethics are at the core of these revolutionary technologies?

Black box "magic" isn't acceptable now that companies are moving from the "fun" phase of playing with AI's capabilities to implementing the technology in essential business workflows. "Every company in the world is looking for the opportunity to take advantage of AI to improve their business processes, whether it's to improve their revenue, lower their cost profile, or lower their risk," said Nenshad Bardoliwalla (pictured), chief product officer at DataRobot Inc. They discussed DataRobot's AI Cloud and self-service AI. (* Disclosure below.) The danger of AI is that algorithms aren't infallible. Trust is provided through transparency and explainability, which together allow data scientists to see into the workings of the AI's "mind" to understand how it arrived at insights.

Maura R. Grossman, JD, Ph.D., is a Research Professor in the Cheriton School of Computer Science, an Adjunct Professor at Osgoode Hall Law School, and an affiliate faculty member of the Vector Institute for Artificial Intelligence. She is also Principal at Maura Grossman Law, an eDiscovery law and consulting firm in Buffalo, New York. Maura is best known for her work on technology-assisted review, a supervised machine learning approach that she and her colleague, Computer Science Professor Gordon V. Cormack, developed to expedite review of documents in high-stakes litigation. She teaches Artificial Intelligence: Law, Ethics, and Policy, a course for graduate computer science students at Waterloo and upper-class law students at Osgoode, as well as the ethics workshop required of all students in the master's programs in artificial intelligence and data science at Waterloo. Artificial intelligence is an umbrella term first used at a conference in Dartmouth in 1956.

Chief Marketing Officer at Interactions, a conversational AI company, where he oversees all aspects of communications, sales and marketing. Let's face it: When a company develops artificial intelligence (AI) that can offer us a medical diagnosis, care for our elderly grandparents or autonomously drive a vehicle, ethics aren't the flashiest elements to focus on. It's tempting for companies to get caught up in the excitement of creating the latest cutting-edge technology and vow to sort out ethical considerations after the fact. That works just as well, right? Late last year, I had a conversation with Thomas Arnold, a research associate at Tufts' Human-Robot Interaction Lab, for my company's podcast.

Positive, reliable and human-centric artificial intelligence (AI) relies on the willingness of Europe as a whole to design a balanced and inclusive governance framework that would allow it to become a leader in the development of trustworthy AI technologies worldwide. That was the main conclusion reached in the frame of the high-level workshop organised by the Panel for the Future of Science and Technology (STOA) on 29 January 2020 at the European Parliament in Brussels. The first STOA event for this parliamentary term (2019-2024) drew a full house with Members of the European Parliament, European Commission leaders, academic experts and representatives of international organisations debating how to strike the right balance on AI. Harnessing the numerous benefits that the transformative power of AI can bring needs to also take account of the necessity to mitigate a number of potential risks – from hampering people's fundamental rights, such as privacy or non-discrimination – to undermining European values such as democracy, human dignity and the right to assemble. The event proved to be a timely occasion to discuss how Europe could maximise the benefits and address the challenges of AI in a human-centric way, coming only a few days before the publication of the European Commission's legislative plans on AI in the form of a White Paper on 19 February 2020.

When we talk about artificial intelligence (AI) and ethics, we are not referring primarily to dystopian applications in which an autonomous robot stubbornly makes decisions about life and death without human control in Terminator fashion. Of course, this does not mean that a critical discourse, for example, on autonomous weapons, is not urgently needed, which the Slaughterbots video by the Future of Life Institute and the renowned Berkeley professor Stuart Russell impressively demonstrated. But we don't have to go that far. Today's AI applications can have a significant impact on consumers, raising governance and ethical issues for businesses. The use of AI in business is aimed, among other things, at automating decisions that were previously made by a human being, for example, a clerk or expert.

The ready availability of large volumes of internal, external and social media data, along with advances in analytics and the advent of machine learning (ML), appear to have created the perfect opportunity for improving consumer lending decisions. Can it make the processes that financial institutions use to ensure they are meeting both the stringent regulatory requirements and the ever-changing consumer demands more efficient, while reducing credit risk? These questions were among the topics discussed during a recent webinar conducted by Protiviti's advanced analytics practice leaders and attended by more than 200 people. During the webinar session, we asked the attendees to weigh in on whether machine learning is currently used in their consumer lending business. Just 10.9 percent said yes, while half indicated they did not know whether their organization is using machine learning.