Among the unfortunate are a family, for example, Hector Hernandez-Garcia who, alongside his wife and newborn son, became temporarily homeless after being mistakenly profiled by such an algorithm. Hernandez-Garcia sued; the company settled. Another precedent was the Michigan Integrated Data Automated System, used by the state to monitor filing for unemployment benefits, which was also recently claimed to have falsely accused thousands of citizens of fraud. Class action lawsuits have been filed against the state, professing a myriad of problems with the system that's used and demonstrating how automated systems induce harm that are hard-to-detect. Furthermore, the recent lawsuit against Clearview AI, filed in Illinois near the end of May by the ACLU (American Civil Liberties Union) and a leading privacy class action law firm, alleging that the company's algorithms breached the state's Biometric Information Privacy Act.

Autonomous agents acting in the real-world often operate based on models that ignore certain aspects of the environment. The incompleteness of any given model---handcrafted or machine acquired---is inevitable due to practical limitations of any modeling technique for complex real-world settings. Due to the limited fidelity of its model, an agent's actions may have unexpected, undesirable consequences during execution. Learning to recognize and avoid such negative side effects of the agent's actions is critical to improving the safety and reliability of autonomous systems. This emerging research topic is attracting increased attention due to the increased deployment of AI systems and their broad societal impacts. This article provides a comprehensive overview of different forms of negative side effects and the recent research efforts to address them. We identify key characteristics of negative side effects, highlight the challenges in avoiding negative side effects, and discuss recently developed approaches, contrasting their benefits and limitations. We conclude with a discussion of open questions and suggestions for future research directions.

This is what I was able to construe from one of the statements made by the Institute for the Future and Omidyar Network in one of their AI ethics reports. Hence, it is pivotal for us as technology producers and consumers that we define certain guiding principles and doctrines to encourage ethical use of AI. It's great to have an optimistic disposition to the advancements made within the field of AI. However, in hindsight we should not forget all the havoc that it has perpetrated in the name of AI practices and to name a few; deep fakes, conversational chat bots and overly optimistic implementation of automation that poses a threat to the job security of millions across the globe. This brings us to the biggest question that we need to answer which is, if there is a way that we can define a checklist or a set of guidelines for risk mitigation and brainstorm as to how these guidelines can be implemented within our current schema of AI development? Below is an infographic that shows those risks or seepage points when mitigated or dealt with proper introspection can help us to produce ethically sound and low risk AI products.

Experts in Artificial Intelligence (AI) development predict that advances in the development of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance is not made prudently and critically-reflexively, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of AI for the preservation of humanity and the environment. Currently, several of the open problems in the field of AI research arise from the difficulty of avoiding unwanted behaviors of intelligent agents and systems, and at the same time specifying what we want such systems to do, especially when we look for the possibility of intelligent agents acting in several domains over the long term. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop human moral values simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic approach proposed by Dreyfus, whose phenomenological philosophy shows that the human experience of being-in-the-world in several aspects is not well represented by the symbolic or connectionist cognitive method, especially in regards to the question of learning values. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI.

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed datasets presents some significant challenges. This paper presents a case-study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models that are able to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.

The United States Patent and Trademark Office recently ruled that only flesh and blood humans can be granted patents, not artificial intelligence beings, thus ensuring that a Skynet scenario will play out (you didn't think I'd talk about AI without a Skynet reference, did you? Not it faces a lawsuit over its decision. What set this in motion is the filing of two patent applications in July of last year by Stephen Thaler, a physicist and AI researcher, on behalf of an AI "creative engine" called DABUS. One of the patents relates to an adjustable food container and the other one has to do with an emergency flashlight. On both applications, Thaler listed DABUS as the inventor.

Brian Patrick Green is the director of Technology Ethics at the Markkula Center for Applied Ethics. This article is an update of an earlier article which can be found here [1]. Artificial intelligence and machine learning technologies are rapidly transforming society and will continue to do so in the coming decades. This social transformation will have deep ethical impact, with these powerful new technologies both improving and disrupting human lives. AI, as the externalization of human intelligence, offers us in amplified form everything that humanity already is, both good and evil. At this crossroads in history we should think very carefully about how to make this transition, or we risk empowering the grimmer side of our nature, rather than the brighter. Why is AI ethics becoming a problem now?

Advances in Artificial Intelligence (AI) and Machine Learning (ML) that are being applied to legal efforts have raised controversial questions about the existent restrictions imposed on the practice-of-law. Generally, the legal field has sought to define Authorized Practices of Law (APL) versus Unauthorized Practices of Law (UPL), though the boundaries are at times amorphous and some contend capricious and self-serving, rather than being devised holistically for the benefit of society all told. A missing ingredient in these arguments is the realization that impending legal profession disruptions due to AI can be more robustly discerned by examining the matter through the lens of a framework utilizing the autonomous levels of AI Legal Reasoning (AILR). This paper explores a newly derived instrumental grid depicting the key characteristics underlying APL and UPL as they apply to the AILR autonomous levels and offers key insights for the furtherance of these crucial practice-of-law debates.

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.

Efforts furthering the advancement of Artificial Intelligence (AI) will increasingly encompass AI Legal Reasoning (AILR) as a crucial element in the practice of law. It is argued in this research paper that the infusion of AI into existing and future legal activities and the judicial structure needs to be undertaken by mindfully observing an alignment with the core principles of justice. As such, the adoption of AI has a profound twofold possibility of either usurping the principles of justice, doing so in a Dystopian manner, and yet also capable to bolster the principles of justice, doing so in a Utopian way. By examining the principles of justice across the Levels of Autonomy (LoA) of AI Legal Reasoning, the case is made that there is an ongoing tension underlying the efforts to develop and deploy AI that can demonstrably determine the impacts and sway upon each core principle of justice and the collective set.