Machine learning models are often designed to maximize a primary goal, such as accuracy. However, as these models are increasingly used to inform decisions that affect people's lives or well-being, it is often unclear what the real-world repercussions of their deployment might be--making it crucial to understand and manage such repercussions effectively. Models maximizing user engagement on social media platforms, e.g., may inadvertently contribute to the spread of misinformation and content that deepens political polarization. This issue is not limited to social media--it extends to other applications where machine learning-informed decisions can have real-world repercussions, such as education, employment, and lending. Existing methods addressing this issue require prior knowledge or estimates of analytical models describing the relationship between a classifier's predictions and their corresponding repercussions. We introduce THEIA, a novel classification algorithm capable of optimizing a primary objective, such as accuracy, while providing high-confidence guarantees about its potential repercussions. Importantly, THEIA solves the open problem of providing such guarantees based solely on existing data with observations of previous repercussions. We prove that it satisfies constraints on a model's repercussions with high confidence and that it is guaranteed to identify a solution, if one exists, given sufficient data. We empirically demonstrate, using real-life data, that THEIA can identify models that achieve high accuracy while ensuring, with high confidence, that constraints on their repercussions are satisfied.

Machine learning models are often designed to maximize a primary goal, such as accuracy. However, as these models are increasingly used to inform decisions that affect people's lives or well-being, it is often unclear what the real-world repercussions of their deployment might be--making it crucial to understand and manage such repercussions effectively. Models maximizing user engagement on social media platforms, e.g., may inadvertently contribute to the spread of misinformation and content that deepens political polarization. This issue is not limited to social media--it extends to other applications where machine learning-informed decisions can have real-world repercussions, such as education, employment, and lending. Existing methods addressing this issue require prior knowledge or estimates of analytical models describing the relationship between a classifier's predictions and their corresponding repercussions. We introduce Theia, a novel classification algorithm capable of optimizing a primary objective, such as accuracy, while providing high-confidence guarantees about its potential repercussions. Importantly, Theia solves the open problem of providing such guarantees based solely on existing data with observations of previous repercussions. We prove that it satisfies constraints on a model's repercussions with high confidence and that it is guaranteed to identify a solution, if one exists, given sufficient data. We empirically demonstrate, using real-life data, that Theia can identify models that achieve high accuracy while ensuring, with high confidence, that constraints on their repercussions are satisfied.

Chipmaker Nvidia set a fresh sales record in the second quarter, surpassing Wall Street expectations for its artificial intelligence chips. But shares of the chip giant still dropped 2.3% in after hours trading, in a sign that investors' worries of an AI bubble and the repercussions of Donald Trump's trade wars are not quelled. Nvidia's financial report was the first test of investor appetite since last week's mass AI-stock selloff, when several tech stocks saw shares tumble last week amid growing questions over whether AI-driven companies are being overvalued. On Wednesday, Nvidia reported an adjusted earnings per share of 1.08 on 46.74bn in revenue, surpassing Wall Street's projection of 1.01 in earnings per share on 46.05bn in revenue, according to Fact Set data. But investors had high expectations for the company.

Tamara Kneese studies how people experience technology. She is a senior researcher at New York-based nonprofit Data & Society Research Institute. Her new book, Death Glitch, examines what happens to our digital belongings when we die, and argues that tech companies need to improve how they deal with death on their platforms for the sake of all our digital posterity. The posthumous fate of our digital belongings seems a morbid topic. Not many people think about their digital legacy, but our digital belongings are accumulating.

It's one thing to know whether an individual customer is intrigued by a new mattress or considering a replacement for their sofa's throw pillows; it's another to know to how to move these people to go ahead and make a purchase. When deployed strategically, artificial intelligence (AI) can be a marketer's trusted customer experience ally--transforming customer data into actionable insights and creating new opportunities for personalization at scale. On the other hand, when AI is viewed as merely a quick fix, its haphazard deployment at best can amount to a missed opportunity and at worse undermine trust with an organization's customers. This phenomenon is not unique to AI. In today's fast-moving digital economy, it's not uncommon for performance and results to lag behind expectations.

Given the global upheaval that COVID-19 has triggered, managing supply chain risk is top of mind for everyone. Improving forecast accuracy to lower risk is the sweet spot for machine learning (ML) applications in supply chains. ML is an AI application that looks for patterns, trends, and anomalies in data, the quality and accuracy of which automatically improves with experience of the system. Specifically, ML algorithms built into supply chain management platforms enable predictive risk management that accounts for unknown factors, which is critical to maintaining the continuous flow of goods through the supply chain. Strong risk management requires predicting and accommodating both known and unknown variables.

The discriminatory biases of the algorithms, the invasion of privacy, the risks of facial recognition and the regulation of human-machine relations are challenges that AI needs to face. However, the interests of governments and large companies often prevail over good practices. Artificial intelligence (AI) is no longer a science fiction thing, it is everywhere. Your bank uses it to know if it is going to give you a credit or not and the ads you see on your social networks come out of a classification carried out by an algorithm, which has microsegmented and'decided' if it shows you offers of wrinkle creams or high-end cars. Facial recognition systems, which use airports and security forces, are also based on this technology.

After all the hype, 2020 will be the year that automation and Artificial Intelligence (AI) technology starts moving out of experimentation mode and into more serious levels of adoption, believes Forrester Research. But the picture the market research firm paints is decidedly mixed for tech leaders and buyers. Here are four of the top forecasts from Laura Koetzle, the company's vice president, group director and head of research for Europe: The robotics process automation (RPA) services market has grown over the last few years, mainly because organisations have focused on tackling simple challenges and undertaken projects focused on'low-hanging fruit'. But to move to the next stage, it will be necessary to build "automation strike teams" and centres of excellence in order to put more structure around such initiatives, believes Koetzle. She also warns that in 2020, it would be "incumbent on all tech leaders" to "develop and promote a positive vision of the future of work". In other words, it will be up to them to clarify how technology, which includes RPA and AI, can be used not so much to automate jobs out, but rather to help employees undertake their jobs more effectively.

The ... [ ] biohacker and transhumanist wants to transform himself into a cyborg with the help of chips, implants and sensors. The tour with the digital conference re:publica, the Reeperbahn Festival and the media and marketing start-up promotion next media accelerator will take place on the occasion of the Germany Year USA. From October 2018 to the end of 2019, a German Year will take place in the USA under the motto "Wunderbar together. In a recent editorial, Chris Soley notes that AI can reduce bias humans that humans possess. But is bias the end-all of where AI and new technology will lead us while other writers note how AI infuses new forms of technology in human creativity in a world where chatbots are increasingly replacing an essential dimension of our culture: human interaction? While new tech is pommelled with various ways of evading human interaction, many consumers and businesses feel that tech need not do away entirely with offline interactions. Where companies like Starbucks are using AI more and more, there is great concern that new technology will replace human relationships. And many are responding to this concern by maintaining parts of their businesses within a real-life setting. Patrick Algrim, a human resources consultant at Algrim.co tells me, "Find where your candidates reside in terms of communities.

Science has always hinged on the idea that researchers must be able to prove and reproduce the results of their research. Simply put, that is what makes science...science. Yet in recent years, as computing power has increased, the cloud has taken shape, and data sets have grown, a problem has appeared: it has becoming increasingly difficult to generate the same results consistently--even when researchers include the same dataset. "One basic requirement of scientific results is reproducibility: shake an apple tree, and apples will fall downwards each and every time," observes Kai Zhang, an associate professor in the department of statistics and operations research at The University of North Carolina, Chapel Hill. "The problem today is that in many cases, researchers cannot replicate existing findings in the literature and they cannot produce the same conclusions. This is undermining the credibility of scientists and science. It is producing a crisis."