Digital Ethics

It’s difficult to see how artificial intelligence systems work, and to see whose interests they work for. Regulation could make AI more trustworthy. Until then, user beware.

A guide to support responsible investing in AI and other data technologies

Dear AI researchers, please put less effort into researching how to minimize the carbon footprint of training machine learning models. Instead, please focus…

GPT-4 surprised many people with its abilities at coding, creative brainstorming, letter-writing, and other skills. How can we be less surprised by developments in machine learning? In this post, I’ll forecast the properties of large pretrained ML systems in 2030.

Left unattended, leading AI firms that threaten to exercise near-total control of the technology have devoured troves of data, novels and art from creators without permission or payment.

By Jeremy Kirshbaum, Instructor at the Generative AI Masterclass, founder of LibraryofBabel.ai and IFTF Research Affiliate

Autonowashing[ aw-ton-uh-wosh-ing ] verb. The practice of making unverified or misleading claims which misrepresent the appropriate level of human...

What is the environmental footprint of AI, the consequential ethical implications, and the social cost borne by those who don't necessarily reap the benefits…

🔥 Are you developing AI? Investing in AI? Interested in AI? Listen to what Dr. Ann Cavoukian - the inventor of privacy by design - said about AI &… | 12 comments on LinkedIn

The human gig workers behind AI are often subjected to "unfair" working conditions, a new Oxford report says

Margaret Atwood, Viet Thanh Nguyen and 8,000 others have signed an open letter asking that permission is obtained and compensation given when a writer’s work is used by AI

A wave of advanced machines is coming to the company's facilities thanks to better AI and robots smart enough to work with—and without—humans.

I have already had people in my network asking for support on the matter of #sharenting issues like one parent LOVES to poster images of their children -even… | 20 comments on LinkedIn

Next-generation technologies are poised to cause society-shaking shifts at unprecedented speed and scale. Generative AI, quantum computing, blockchain, and other technologies present novel ethical problems that “business as usual” just can’t handle. To meet these challenges, leaders need to do something different: They must talk about ethics in direct, clear terms, and they must not only define their ethical nightmares but also explain how they’re going to prevent them. To prepare for the ethical challenges ahead, companies need to ensure their senior leaders understand these technologies and are aligned on the ethical risks, perform a gap and feasibility analysis, build a strategy, and implement it. All of this requires an important shift from thinking of our digital ethical nightmares as a technology problem to a leadership problem.

If there’s a 10% chance that human-level artificial intelligence (AI) would lead to existential risk or an “extremely negative outcome”, what would you do? I...

Artificial Intelligence has come a long way, but it might not quite be ready to give us immortality, even digitally. And that's probably a good thing.

Greg Rutkowski is among the artists calling for more protection from artificial intelligence tools.

Regardless of the input, AI image generators will have a tendency to return certain kinds of results. This is where the potential for bias arises.

ChatGPT's Groundbreaking Legislation to Govern AI in Costa Rica Sparks Debate among Experts. Here’s all you need to know!

"Let's wait 20 years, and if an AI wins an Oscar for Best Screenplay, I think we've got to take them seriously," he told Canada's CTV News.

Generative AI is headed towards in Napster moment. Many may not understand this, but that’s OK. It’s moving pretty clearly in that direction. I’ve seen a great…

The startup promises a fairly-distributed, cryptocurrency-based universal basic income. So far all it's done is build a biometric database from the bodies of the poor.

OpenAI is under FTC investigation, as of five days ago. Here are some highlights and reflections about the investigation. ➤ Background   👉The FTC opened…

An advanced version of ChatGPT can analyze images and is already helping the blind. But its ability to put a name to a face is one reason the public doesn’t have access to it.

As potential applications of artificial intelligence (AI) continue to expand, the question remains: will users want the technology and trust it? This blog post explores how to measure the trustworthiness of AI.

Can AI writing detectors be trusted? We dig into the theory behind them.

Fed up with A.I. companies consuming online content without consent, fan fiction writers, actors, social media companies and news organizations are among those rebelling.

The U.S. Federal Trade Commission has opened an investigation into OpenAI on claims it has run afoul of consumer protection laws by putting personal reputations and data at risk, the strongest regulatory threat to the Microsoft-backed startup yet.

Nora Roberts, Viet Thanh Nguyen, Michael Chabon and Margaret Atwood are among those signing an Authors Guild letter asking artificial intelligence companies to get permission or offer compensation.

Although high degrees of reliability have been found for many types of forecasts purportedly due to the existence of accountability, public forecasts of technology are rarely assessed and continue to have a poor reputation. This paper’s analysis of forecasts made by MIT’s Technology Review provides a rare assessment and thus a means to encourage accountability. It first shows that few of the predicted “breakthrough technologies” currently have large markets. Only four have sales greater than $10 billion while eight technologies not predicted by Technology Review have sales greater than $10 billion including three with greater than $100 billion and one other with greater than $50 billion. Second, possible reasons for these poor forecasts are then discussed including an over emphasis on the science-based process of technology change, sometimes called the linear model of innovation. Third, this paper describes a different model of technology change, one that is widely used by private companies and that explains the emergence of those technologies that have greater than $10 billion in sales. Fourth, technology change and forecasts are discussed in terms of cognitive biases and mental models.