California's Senate last week advanced a bill that would force Amazon (AMZN) to reveal details behind the productivity-tracking algorithm used in its warehouses; meanwhile, Facebook (FB) this week faced criticism over a Wall Street Journal report finding it knows its Instagram feed makes some teenage girls feel worse about themselves. These developments make up a backlash not necessarily against big tech, so much as its algorithms, which use artificial intelligence (AI) to adapt performance for individual users or employees. In a new interview, AI expert Kai-Fu Lee -- who worked as an executive at Google (GOOG, GOOGL), Apple (AAPL), and Microsoft (MSFT) -- explained the top four dangers of burgeoning AI technology: externalities, personal data risks, inability to explain consequential choices, and warfare. "The single largest danger is autonomous weapons," he says. "That's when AI can be trained to kill, and more specifically trained to assassinate," adds Lee, the co-author of a new book entitled "AI 2041: Ten Visions for Our Future."
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society. The intention of developing AI is to benefit humans, by reducing human labor, bringing everyday convenience to human lives, and promoting social good. However, recent research and AI applications show that AI can cause unintentional harm to humans, such as making unreliable decisions in safety-critical scenarios or undermining fairness by inadvertently discriminating against one group. Thus, trustworthy AI has attracted immense attention recently, which requires careful consideration to avoid the adverse effects that AI may bring to humans, so that humans can fully trust and live in harmony with AI technologies. Recent years have witnessed a tremendous amount of research on trustworthy AI. In this survey, we present a comprehensive survey of trustworthy AI from a computational perspective, to help readers understand the latest technologies for achieving trustworthy AI. Trustworthy AI is a large and complex area, involving various dimensions. In this work, we focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being. For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems. We also discuss the accordant and conflicting interactions among different dimensions and discuss potential aspects for trustworthy AI to investigate in the future.
SAN FRANCISCO, CA - SEPTEMBER 07: Google AI Research Scientist Timnit Gebru speaks onstage during ... [ ] Day 3 of TechCrunch Disrupt SF 2018 at Moscone Center on September 7, 2018 in San Francisco, California. 'Taking On Tech is an informative series that explores artificial intelligence, data science, algorithms, and mass censorship. In this inaugural report, For(bes) The Culture kicks things off with Dr. Timnit Gebru, a former researcher and co-lead of Google's Ethical AI team. When Gebru was forced out of Google after refusing to retract a research paper that was already cleared by Google's internal review process, a conversation about the tech industry's inherent diversity problem resurfaced. The paper raised concerns on algorithmic bias in machine learning and the latent perils that AI presents for marginalized communities. Around 1,500 Google employees signed a letter in protest, calling for accountability and answers over her unethical firing.
Recent controversies related to topics such as fake news, privacy, and algorithmic bias have prompted increased public scrutiny of digital technologies and soul-searching among many of the people associated with their development. In response, the tech industry, academia, civil society, and governments have rapidly increased their attention to "ethics" in the design and use of digital technologies ("tech ethics"). Yet almost as quickly as ethics discourse has proliferated across the world of digital technologies, the limitations of these approaches have also become apparent: tech ethics is vague and toothless, is subsumed into corporate logics and incentives, and has a myopic focus on individual engineers and technology design rather than on the structures and cultures of technology production. As a result of these limitations, many have grown skeptical of tech ethics and its proponents, charging them with "ethics-washing": promoting ethics research and discourse to defuse criticism and government regulation without committing to ethical behavior. By looking at how ethics has been taken up in both science and business in superficial and depoliticizing ways, I recast tech ethics as a terrain of contestation where the central fault line is not whether it is desirable to be ethical, but what "ethics" entails and who gets to define it. This framing highlights the significant limits of current approaches to tech ethics and the importance of studying the formulation and real-world effects of tech ethics. In order to identify and develop more rigorous strategies for reforming digital technologies and the social relations that they mediate, I describe a sociotechnical approach to tech ethics, one that reflexively applies many of tech ethics' own lessons regarding digital technologies to tech ethics itself.
It was December 2020, and she was being invited into a pilot program providing guaranteed income--a direct cash transfer with no strings attached. For Softky, it was a lifeline. "For the first time in a long time, I felt like I could … take a deep breath, start saving, and see myself in the future," she says. The idea of "just giving people money" has been in and out of the news since becoming a favored cause for many high-profile Silicon Valley entrepreneurs, including Twitter's Jack Dorsey, Facebook cofounders Mark Zuckerberg and (separately) Chris Hughes, and Singularity University's Peter Diamandis. They proposed a universal basic income as a solution to the job losses and social conflict that would be wrought by automation and artificial intelligence--the very technologies their own companies create.
AI - Artificial Intelligence AGI - Artificial General Intelligence ANN - Artificial Neural Network ANOVA - Analysis of Variance ANT - Actor Network Theory API - Application Programming Interface APX - Amsterdam Power Exchange AVE - Average Variance Extracted BU - Business Unit CART - Classification and Regression Tree CBMV - Crowd-based Business Model Validation CR - Composite Reliability CT - Computed Tomography CVC - Corporate Venture Capital DR - Design Requirement DP - Design Principle DSR - Design Science Research DSS - Decision Support System EEX - European Energy Exchange FsQCA - Fuzzy-Set Qualitative Comparative Analysis GUI - Graphical User Interface HI-DSS - Hybrid Intelligence Decision Support System HIT - Human Intelligence Task IoT - Internet of Things IS - Information System IT - Information Technology MCC - Matthews Correlation Coefficient ML - Machine Learning OCT - Opportunity Creation Theory OGEMA 2.0 - Open Gateway Energy Management 2.0 OS - Operating System R&D - Research & Development RE - Renewable Energies RQ - Research Question SVM - Support Vector Machine SSD - Solid-State Drive SDK - Software Development Kit TCP/IP - Transmission Control Protocol/Internet Protocol TCT - Transaction Cost Theory UI - User Interface VaR - Value at Risk VC - Venture Capital VPP - Virtual Power Plant Chapter I
This week, we learned a lot more about the inner workings of AI fairness and ethics operations at Facebook and Google and how things have gone wrong. On Monday, a Google employee group wrote a letter asking Congress and state lawmakers to pass legislation to protect AI ethics whistleblowers. That letter cites VentureBeat reporting about the potential policy outcomes of Google firing former Ethical AI team co-lead Timnit Gebru. It also cites research by UC Berkeley law professor Sonia Katyal, who told VentureBeat, "What we should be concerned about is a world where all of the most talented researchers like [Gebru] get hired at these places and then effectively muzzled from speaking. And when that happens, whistleblower protections become essential."
Zhang, Daniel, Mishra, Saurabh, Brynjolfsson, Erik, Etchemendy, John, Ganguli, Deep, Grosz, Barbara, Lyons, Terah, Manyika, James, Niebles, Juan Carlos, Sellitto, Michael, Shoham, Yoav, Clark, Jack, Perrault, Raymond
Welcome to the fourth edition of the AI Index Report. This year we significantly expanded the amount of data available in the report, worked with a broader set of external organizations to calibrate our data, and deepened our connections with the Stanford Institute for Human-Centered Artificial Intelligence (HAI). The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Its mission is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. The report aims to be the most credible and authoritative source for data and insights about AI in the world.
We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to evolve to help people adapt to the challenges of remote and hybrid work.
Our mission to make business better is fueled by readers like you. To enjoy unlimited access to our journalism, subscribe today. Alex Spinelli, chief technologist for business software maker LivePerson, says the recent U.S. Capitol riot shows the potential dangers of a technology not usually associated with pro-Trump mobs: artificial intelligence. The same machine-learning tech that helps companies target people with online ads on Facebook and Twitter also helps bad actors distribute propaganda and misinformation. In 2016, for instance, people shared fake news articles on Facebook, whose A.I. systems then funneled them to users.