Machine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human-computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we adopt a framework from conformal prediction sets to remove unfairness from risk algorithms themselves and the covariates used for forecasting. From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness that are effectively free any meaningful differences between Black and White offenders. We also produce fair forecasts for individual offenders coupled with valid probability guarantees that the forecasted outcome is the true outcome. We see our work as a demonstration of concept for application in a wide variety of criminal justice decisions. The procedures provided can be routinely implemented in jurisdictions with the usual criminal justice datasets used by administrators. The requisite procedures can be found in the scripting software R. However, whether stakeholders will accept our approach as a means to achieve risk assessment fairness is unknown. There also are legal issues that would need to be resolved although we offer a Pareto improvement.

Survival Analysis and Reliability Theory are concerned with the analysis of time-to-event data, in which observations correspond to waiting times until an event of interest such as death from a particular disease or failure of a component in a mechanical system. This type of data is unique due to the presence of censoring, a type of missing data that occurs when we do not observe the actual time of the event of interest but, instead, we have access to an approximation for it given by random interval in which the observation is known to belong. Most traditional methods are not designed to deal with censoring, and thus we need to adapt them to censored time-to-event data. In this paper, we focus on non-parametric goodness-of-fit testing procedures based on combining the Stein's method and kernelized discrepancies. While for uncensored data, there is a natural way of implementing a kernelized Stein discrepancy test, for censored data there are several options, each of them with different advantages and disadvantages. In this paper, we propose a collection of kernelized Stein discrepancy tests for time-to-event data, and we study each of them theoretically and empirically; our experimental results show that our proposed methods perform better than existing tests, including previous tests based on a kernelized maximum mean discrepancy.

Increasingly, organizations across many industries and geographies are building and deploying machine learning models and incorporating artificial intelligence into a variety of their different products and offerings. However, as they put AI capabilities into systems that we interact with on a daily basis, it becomes increasingly important to make sure these systems are behaving in a way that's beneficial to the public. When creating AI systems organizations should also consider the ethical and moral implications to make sure that AI is being created for good intentions. Policymakers that want to understand and leverage AI's potential and impact need to take a holistic view of the issues. This includes things like intentions behind AI systems, as well as potential unintended consequences and actions of AI systems.

The City of London in Canada is implementing an artificial intelligence (AI) tool it has developed internally to predict and prevent homelessness. The Chronic Homelessness Artificial Intelligence (CHAI) model uses machine learning to forecast the probability of an individual in the city's shelter system becoming chronically homeless within the next six months – that is, remaining in the shelter system for more than 180 days in a year. In July, 312 people in London were chronically homeless. The tool was developed in-house with support from a consultant, and could help other cities – particularly those in Canada – deploy similar systems quickly. The CHAI model grew out of London's adoption of the federal Homeless Individuals and Families Information System (HIFIS), which is designed to provide a clearer picture of homelessness in communities and support organisations to work collaboratively.

On Monday evening, a judge said she planned to rule quickly on a proposed restraining order against Apple in its battle with Epic, and US District Judge Yvonne Gonzalez Rogers came through on that promise. A few hours after a Zoom-streamed hearing, she made the call: Apple can continue to block Fortnite on iOS, but, at least for now, it can't drop the hammer on the Unreal Engine development kit. In a battle of titans over the App Store's rules, the hearing and ruling gave only a few hints about how this may roll out. Epic's decision to break the rules before suing doesn't seem to help its case, but Apple's arguments about competition in the market also fall a bit flat. It could be many months before a final decision comes down -- Fortnite players on iPhones should probably hope the two parties can negotiate an agreement before that happens.

In February, a lobbyist friend urged Erik Sartorius, the executive director of the Kansas League of Municipalities, to look at a newly introduced bill that would affect cities. The legislation involved "personal delivery devices"--robots that, as if in a sci-fi movie, might deliver a bag of groceries, a toolbox, or a prescription to your doorstep. It would have limited their weight to 150 pounds, not including the cargo inside. And it would have allowed them to operate on any sidewalk or crosswalk in Kansas at speeds up to 6 miles per hour, the pace of a quick human jog. Lawmakers and lobbyists say the bill was drafted with help from Amazon.

MIT SMR Connections is the custom content creation unit within MIT Sloan Management Review. In this Q&A, Michelle K. Lee, vice president of the Amazon Web Services (AWS) Machine Learning Solutions Lab, shares real-world examples of machine learning in action, describes four key implementation challenges, and offers other advice. This conversation has been condensed and edited for clarity, length, and editorial style. Q: Can you provide an overview of how artificial intelligence (AI) and machine learning (ML) are driving digital transformation? Lee: AI and machine learning went from being aspirational technology to mainstream extremely fast.

Orange launched its strategic plan for the next five years, Engage 2025, last December and AI-enabled innovation was one of the four pillars of the group's future success. This breaks into four parts, as shown below. The 2025 strategy states, "Our ambition is as strong as our social commitments are firm. And we will never think of one without the other" (see last section of article on inclusivity). Lugagne-Delpon said at the online briefing, "We believe that AI can bring value to almost every phase of the network lifecycle – so network planning and design to optimise the efficiency of investment, operations for advanced monitoring, smarter maintenance and better security, and also optimisation to populate a number of operation processes and also optimise the performance and the use of resources." He went on to describe a number of use cases.

Enterprises around the world are rapidly incorporating artificial intelligence (AI) into existing and new products and processes. This effort is not just to improve such offerings and services, but to achieve a qualitatively higher level of capability not possible before. It is clear that AI carries the potential for many new opportunities, across all industries, but it is also already recognized that it brings numerous risks as well. As with any technology, senior management and board directors need to be aware of both the opportunity and the risk in order to successfully and responsibly manage the enterprise. The opportunities are great--AI can assist in robotic process automation (RPA), machine learning, natural language processing, finding new drugs and therapies, and will be essential for driverless transportation--but if the risks are downplayed or overlooked, there can be serious reputational and/or legal consequences.