Today, AI is increasingly being used in many high-stakes decision-making applications in which fairness is an important concern. Already, there are many examples of AI being biased and making questionable and unfair decisions. The AI research community has proposed many methods to measure and mitigate unwanted biases, but few of them involve inputs from human policy makers. We argue that because different fairness criteria sometimes cannot be simultaneously satisfied, and because achieving fairness often requires sacrificing other objectives such as model accuracy, it is key to acquire and adhere to human policy makers' preferences on how to make the tradeoff among these objectives. In this paper, we propose a framework and some exemplar methods for eliciting such preferences and for optimizing an AI model according to these preferences.

The moral authority of ethics codes stems from an assumption that they serve a unified society, yet this ignores the political aspects of any shared resource. The sociologist Howard S. Becker challenged researchers to clarify their power and responsibility in the classic essay: Whose Side Are We On. Building on Becker's hierarchy of credibility, we report on a critical discourse analysis of data ethics codes and emerging conceptualizations of beneficence, or the "social good", of data technology. The analysis revealed that ethics codes from corporations and professional associations conflated consumers with society and were largely silent on agency. Interviews with community organizers about social change in the digital era supplement the analysis, surfacing the limits of technical solutions to concerns of marginalized communities. Given evidence that highlights the gulf between the documents and lived experiences, we argue that ethics codes that elevate consumers may simultaneously subordinate the needs of vulnerable populations. Understanding contested digital resources is central to the emerging field of public interest technology. We introduce the concept of digital differential vulnerability to explain disproportionate exposures to harm within data technology and suggest recommendations for future ethics codes.

Most of us know that DATA, the beloved android from Star Trek, The Next Generation, is an artificial intelligence (AI) life form from the distant future with a high capacity to problem solve and innovate. But, if DATA were present today and invented a new technology, could he be an inventor on a patent for his invention? The question of whether AI can legally be an inventor on a patent was recently addressed by the European Patent Office (EPO) and The United Kingdom Intellectual Property Office (UKIPO). The same question is still being evaluated by U.S. Patent and Trademark Office (USPTO) along with solicitation for comments to the patent community. A group from the University of Surrey, in the United Kingdom (UK), recently challenged the definition of "inventor" in Europe and the United States by filing two separate patent applications designating an AI entity as an inventor.

With coyote attacks on humans in cities and suburbs making headlines – coyotes injured two people in Chicago earlier this month – officials could tap into a data repository to get a better handle on what's bringing the area's animals into such close proximity to humans. Called eMammal, the tool has been around for several years in one form or another and has helped researchers manage camera-trapping projects. It uses a data pipeline that takes images and metadata from the field through a cloud-based review processes and into SIdora, a Smithsonian Institution data repository. To date, eMammal has data on more than 1 million detections of wildlife worldwide, including in cities. Smithsonian researchers collaborated with others at the North Carolina Museum of Natural Sciences, Conservation International and the Wildlife Conservation Society to develop an open standard for camera trap metadata -- the Camera Trap Metadata Standard -- as part of the eMammal project. Camera traps are ruggedized cameras that researchers place in forests, jungles, grasslands, cities and elsewhere to capture images of mammals.

The average time it took from filing to issuance was 850 days. The patent that took the longest time to issue was U.S. Patent No. 10410308, System, method, and device for personal medical care, intelligent analysis, and diagnosis.

Researchers associated with Google and the Partnership on AI have created a framework to help companies and their engineering teams audit AI systems before deploying them. The framework, intended to add a layer of quality assurance to businesses launching AI, translates into practice values often espoused in AI ethics principles and tackles an accountability gap authors say exists in AI today. The work, titled "Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing" is one of a handful of outstanding AI ethics research papers accepted for publication as part of the Fairness, Accountability, and Transparency (FAT) conference, which takes place this week in Barcelona, Spain. "The proposed auditing framework is intended to contribute to closing the development and deployment accountability gap of large-scale artificial intelligence systems by embedding a robust process to ensure audit integrity," the paper reads. "At a minimum, the internal audit process should enable critical reflections on the potential impact of a system, serving as internal education and training on ethical awareness in addition to leaving what we refer to as a'transparency trail' of documentation at each step of the development cycle." The framework is also intended to identify risks and reduce them to the lowest degree possible, as well as to map out how things that can be done differently in the future or how to respond to a failure after launch.

AI algorithms, and machine learning (ML) techniques in particular, are increasingly important to individuals' lives, but have caused a range of privacy concerns addressed by, e.g., the European GDPR. Using cryptographic techniques, it is possible to perform inference tasks remotely on sensitive client data in a privacy-preserving way: the server learns nothing about the input data and the model predictions, while the client learns nothing about the ML model (which is often considered intellectual property and might contain traces of sensitive data). While such privacy-preserving solutions are relatively efficient, they are mostly targeted at neural networks, can degrade the predictive accuracy, and usually reveal the network's topology. Furthermore, existing solutions are not readily accessible to ML experts, as prototype implementations are not well-integrated into ML frameworks and require extensive cryptographic knowledge. In this paper, we present CryptoSPN, a framework for privacy-preserving inference of sum-product networks (SPNs). SPNs are a tractable probabilistic graphical model that allows a range of exact inference queries in linear time. Specifically, we show how to efficiently perform SPN inference via secure multi-party computation (SMPC) without accuracy degradation while hiding sensitive client and training information with provable security guarantees. Next to foundations, CryptoSPN encompasses tools to easily transform existing SPNs into privacy-preserving executables. Our empirical results demonstrate that CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.

How should government and society come together to address the challenge of regulating artificial intelligence? What approaches and tools will promote innovation, protect society from harm and build public trust in AI? Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Algorithms are already being applied to improve predictions, optimize systems and drive productivity in many other sectors. However, early experience shows that AI can create serious challenges. Without proper oversight, AI may replicate or even exacerbate human bias and discrimination, cause potential job displacement, and lead to other unintended and harmful consequences.

While AI and machine learning are enabling definite advances in the digital world, these technologies are also raising privacy and ethical concerns. What does AI mean for personal privacy, and is it being exploited unethically? Are these concerns being addressed, or will AI spell disaster for society? Bernd Stahl is coordinator of the EU's SHERPA project, a consortium that investigates the impact of AI on ethics and human rights. Bernd stopped by for episode 34 of Cyber Security Sauna to discuss the delicate balance of AI – its advantages and disadvantages, potential misuses and how AI may improve life and create opportunity for some, while others may be hurt by algorithmic biases and unemployment. Listen, or read on for the transcript. And don't forget to subscribe, rate and review! Janne: So Bernd, how would you frame the work that the SHERPA project is doing? Bernd: SHERPA is trying to explore which ethical issues arise due to the use of AI. We're looking at human rights components in a variety of ways, and we are, as part of the overall work of the project, trying to explore which options of addressing possible ethical and human rights issues exist, which ones of those are important, and which ones of those need to be emphasized. Overall we hope to come up with a set of recommendations and proposals for the European Commission, but also for other stakeholders, that will help them deal with any issues that they may encounter.

Consumers must be safeguarded from artificial intelligence (AI) and automated decision-making (ADM) which is "advancing at a remarkable pace", says a European parliamentary committee. A draft resolution, approved by the European Parliament's Internal Market and Consumer Protection Committee (IMCO) on 23 January, proposes that humans should always be ultimately responsible for, and able to overrule, decisions, especially in medical, legal and accounting contexts. It also sets out how to make use of the technology more transparent and accountable. IMCO chair Petra De Sutter said the committee welcomed the potential of rapid advances in AI technology, but at the same time wanted to highlight important issues that needed to be addressed. The resolution makes clear that while free-flowing data will be essential for the creation of innovative services, the importance of protecting personal data under GDPR, and of using only high-quality and unbiased data sets is equally important.