DoNotPay, a bot-based platform that helps consumers fight for their rights, is rolling out a new email service that automatically applies for refunds, cancels subscriptions, fights spam, and more by scanning messages in people's inboxes. The launch comes in a year in which the San Francisco-based company has seen a surge in demand due to the global pandemic, with consumers contending with canceled flights, closed gyms, and monthly memberships to reconsider due to reduced income. As the world transitions to the "new normal" post-lockdown, many consumers will still be feeling the pinch, putting DoNotPay in a strong position to apply its "robo lawyer" to more industries and use-cases. The company recently secured $12 million in funding to help it do just that, with backing from big-name investors including Coatue Management, Andreessen Horowitz, and Peter Thiel's Founders Fund. DoNotPay first came to prominence back in 2015 when British entrepreneur Josh Browder launched a website to help Londoners appeal parking fines.

The Chinese board game Go is one of the oldest games in the world. Enthusiasts have spent more than 2,500 years developing strategies to beat their opponents, and with hundreds of options for every move, its complexity can test the upper limits of human thought. So when Google's AlphaGo, an artificial intelligence (AI) program, defeated Go's world champion in 2016, technologists took notice. Not only was the victory a testament to the power of AI, they argued, it was a bellwether of things to come, a demonstration that even highly skilled employees might soon be trampled in the unstoppable march toward an automated world. The jury is still out on whether pure AI--that is, a technology that mimics the full complexity of human thought--is inevitable or mere hype, but earnest work is well underway and already paying off.

The impact of digital technologies on those with mental health treatment histories is rarely addressed by sweeping reports and recommendations that focus on the impacts of technology on society. In a paper submitted to the Australian Human Rights Commission about promoting fair and equitable deployments of Artificial Intelligence, Piers Gooding addressed this gap. His report showed how infringements on privacy through data collection pose risks to people with disabilities and mental health service-users. Digital technologies and artificial intelligence-enabled changes to the provision of mental health services are ubiquitous today, facilitating'supported decision-making' in healthcare services, peer networking, face-to-face support, and crisis support. They are often instrumental in monitoring abuses in care provision too.

Two years ago, I wrote about a mysterious series of computer contacts between the Trump Organization and Alfa Bank, one of the most powerful financial institutions in Russia. For four months, during the summer before the 2016 Presidential election, two servers registered to the bank repeatedly looked up the address of another server, maintained by a mass-marketing firm for the Trump Organization. Thousands of these lookups, which typically precede communication, appeared in logs of the Domain Name System, a global database of online addresses; the traffic was noted by a group of prominent computer scientists with unusual access to those records. But the D.N.S. traces were inconclusive and required sophisticated analysis. The Trump Organization and Alfa Bank denied that they had been communicating at all, and the episode remained a mystery.

This summer, my peers marched and spoke out against blatant acts of racial injustice. Meanwhile, as a 17-year-old student who dabbles in computer programming, I've been stewing about a newfangled, less-overt threat that also relates to systemic racism. What I did not realize until this summer was that my generation is already experiencing bias from our most trusted ally: the computer. If you are a student, you may have already been the target of some sort of algorithmic bias, even if you don't know it. Consider one telling fact: for a good number of high schoolers like myself who take state standardized tests, written essays might not be graded not by an English teacher, but by a robot! My first reaction to learning this was simple surprise; I had never thought that my essays might be graded by inanimate objects.

Rapid developments in AI have triggered digital advancements in almost every industry. The technology is capable of construing data contextually to provide requested information, supply analysis, and push events based on findings. Simultaneously, businesses need to meet social, investor and regulatory requirements regarding how they use advanced technologies like AI. Significantly, it is also crucial that organizations must commit to using the technology with a purpose, which leads to the way of sustainable development. In its recent study, the Allen Institute for AI argued the prioritization of "Green AI" efforts that focus on the energy efficiency of AI systems. The study was based on many high-profile advances in AI that have wavered carbon footprints.

Unauthorized surveillance: It is well known that drones can be easily utilized for mass surveillance This is to be comprehended in setting of computerized advances that mean to reform our day by day lives, by having more point by point records about those lives. In the name of national security and fear based oppression, observation systems are used to track and profile the residents by the state too and private offices. By the ideals of their plan and size, drones can work undetected, permitting the client to screen individuals without their insight. For occurrence, there are drones with too high goals gigapixel cameras that can be utilized to follow individuals and vehicles from heights as high as 20,000 feet. They can convey gear for example, counterfeit towers, which can break Wi-Fi codes and block instant messages and mobile phone discussions without the information on either the correspondence supplier or the client.

Algorithmic fairness is a major concern in recent years as the influence of machine learning algorithms becomes more widespread. In this paper, we investigate the issue of algorithmic fairness from a network-centric perspective. Specifically, we introduce a novel yet intuitive function known as network-centric fairness perception and provide an axiomatic approach to analyze its properties. Using a peer-review network as case study, we also examine its utility in terms of assessing the perception of fairness in paper acceptance decisions. We show how the function can be extended to a group fairness metric known as fairness visibility and demonstrate its relationship to demographic parity. We also illustrate a potential pitfall of the fairness visibility measure that can be exploited to mislead individuals into perceiving that the algorithmic decisions are fair. We demonstrate how the problem can be alleviated by increasing the local neighborhood size of the fairness perception function.

Artificial Intelligence (AI) has rapidly emerged as a key disruptive technology in the 21st century. At the heart of modern AI lies Deep Learning (DL), an emerging class of algorithms that has enabled today's platforms and organizations to operate at unprecedented efficiency, effectiveness, and scale. Despite significant interest, IS contributions in DL have been limited, which we argue is in part due to issues with defining, positioning, and conducting DL research. Recognizing the tremendous opportunity here for the IS community, this work clarifies, streamlines, and presents approaches for IS scholars to make timely and high-impact contributions. Related to this broader goal, this paper makes five timely contributions. First, we systematically summarize the major components of DL in a novel Deep Learning for Information Systems Research (DL-ISR) schematic that illustrates how technical DL processes are driven by key factors from an application environment. Second, we present a novel Knowledge Contribution Framework (KCF) to help IS scholars position their DL contributions for maximum impact. Third, we provide ten guidelines to help IS scholars generate rigorous and relevant DL-ISR in a systematic, high-quality fashion. Fourth, we present a review of prevailing journal and conference venues to examine how IS scholars have leveraged DL for various research inquiries. Finally, we provide a unique perspective on how IS scholars can formulate DL-ISR inquiries by carefully considering the interplay of business function(s), application areas(s), and the KCF. This perspective intentionally emphasizes inter-disciplinary, intra-disciplinary, and cross-IS tradition perspectives. Taken together, these contributions provide IS scholars a timely framework to advance the scale, scope, and impact of deep learning research.

Over the next few years, society as a whole will need to address what core values it wishes to protect when dealing with technology. Anthropology, a field dedicated to the very notion of what it means to be human, can provide some interesting insights into how to cope and tackle these changes in our Western society and other areas of the world. It can be challenging for social science practitioners to grasp and keep up with the pace of technological innovation, with many being unfamiliar with the jargon of AI. This short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. It intends to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.