In the context of designing and implementing ethical Artificial Intelligence (AI), varying perspectives exist regarding developing trustworthy AI for autonomous cars. This study sheds light on the differences in perspectives and provides recommendations to minimize such divergences. By exploring the diverse viewpoints, we identify key factors contributing to the differences and propose strategies to bridge the gaps. This study goes beyond the trolley problem to visualize the complex challenges of trustworthy and ethical AI. Three pillars of trustworthy AI have been defined: transparency, reliability, and safety. This research contributes to the field of trustworthy AI for autonomous cars, providing practical recommendations to enhance the development of AI systems that prioritize both technological advancement and ethical principles.

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

Welcome to AI book reviews, a series of posts that explore the latest literature on artificial intelligence. There are two contrasting but equally disturbing images of artificial intelligence. One warns about a future in which runaway intelligence becomes smarter than humanity, creates mass unemployment, and enslaves humans in a Matrix-like world or destroys them a la Skynet. A more contemporary image is one in which dumb AI algorithms are entrusted with sensitive decisions that can cause severe harm when they do go wrong. What both visions have in common is the absence of human control.

The high expectations of AI have triggered worldwide interest and concern, generating 400 policy documents on responsible AI. Intense discussions over the ethical issues lay a helpful foundation, preparing researchers, managers, policy makers, and educators for constructive discussions that will lead to clear recommendations for building the reliable, safe, and trustworthy systems6 that will be commercial success. This Viewpoint focuses on four themes that lead to 15 recommendations for moving forward. The four themes combine AI thinking with human-centered User Experience Design (UXD). Ethical discussions are a vital foundation, but raising the edifice of responsible AI requires design decisions to guide software engineering teams, business managers, industry leaders, and government policymakers.

The new goal is to seek high levels of human control AND high levels of automation, which is more likely to produce computer applications that are Reliable, Safe & Trustworthy (RST). Achieving this goal, especially for complex poorly understood problems, will dramatically increase human performance, while supporting human self-efficacy, mastery, creativity, and responsibility. The traditional belief in computer autonomy is compelling for many artificial intelligence (AI) researchers, developers, journalists, and promoters. The goal of computer autonomy was central in Sheridan and Verplank's (1978) ten levels from human control to computer automation/autonomy (Table 1). Their widely cited one-dimensional list continues to guide much of the research and development, suggesting that increases in automation must come at the cost of lowering human control. Shifting to HCAI could liberate design thinking so as to produce computer applications that increase automation, while amplifying, augmenting, enhancing, and empowering people to innovatively apply systems and creatively refine them.

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.

Liz O'Sullivan says she struggled for months as she learned more about the military project her in which her employer, Clarifai, was participating. Liz O'Sullivan says she struggled for months as she learned more about the military project her in which her employer, Clarifai, was participating. On the night of Jan. 16, Liz O'Sullivan sent a letter she'd been working on for weeks. It was directed at her boss, Matt Zeiler, the founder and CEO of Clarifai, a tech company. "The moment before I hit send and then afterwards, my heart, I could just feel it racing," she says.

Artificial Intelligence (AI) systems are taking over a vast array of tasks that previously depended on human expertise and judgment. Often, however, the "reasoning" behind their actions is unclear, and can produce surprising errors or reinforce biased processes. One way to address this issue is to make AI "explainable" to humans--for example, designers who can improve it or let users better know when to trust it. Although the best styles of explanation for different purposes are still being studied, they will profoundly shape how future AI is used. Some explainable AI, or XAI, has long been familiar, as part of online recommender systems: book purchasers or movie viewers see suggestions for additional selections described as having certain similar attributes, or being chosen by similar users.