Experts Argue Whether Computers Could Reason, and if They Should (Published 1977)

Computer world is in midst of fundamental dispute over question of computer intelligence since MIT Prof Joseph Weizenbaum wrote book arguing that machines can never be made to reason like people and should not be; Weizenbaum por (M)

The Unaccountability Machine: Why Big Systems Make Terrible Decisions—and How the World Lost Its Mind

Longlisted for the 2024 Financial Times Book of the Year. How life and the economy became a black box—a collection of systems no one understands, producing outcomes no one likes.   Passengers get bumped from flights. Phone menus disconnect. Automated financial trades produce market collapse. Of all the challenges in modern life, some of the most vexing come from our relationships with automation: a large system does us wrong, and there’s nothing we can do about it. The problem, economist Dan Davies shows, is accountability sinks: systems in which decisions are delegated to a complex rule book or set of standard procedures, making it impossible to identify the source of mistakes when they happen. In our increasingly unhuman world—lives dominated by algorithms, artificial intelligence, and large organizations—these accountability sinks produce more than just aggravation. They make life and economy unknowable—a black box for no reason. In The Unaccountability Machine, Davies lays bare how markets, institutions, and even governments systematically generate outcomes that no one—not even those involved in making them—seems to want. Since the earliest days of the computer age, theorists have foreseen the dangers of complex systems without personal accountability. In response, British business scholar Stafford Beer developed an accountability-first approach to management called “cybernetics,” which might have taken off had his biggest client (the Chilean government) not fallen to a bloody coup in 1973. With his signature blend of economic and journalistic rigor, Davies examines what’s gone wrong since Beer, including what might have been had the world embraced cybernetics when it had the chance. The Unaccountability Machine is a revelatory and resonant account of how modern life became predisposed to dysfunction.

Debates over AI consciousness are a trap

If AI systems are viewed as too advanced to control, the companies that build them can’t held liable for the harms they cause.

The System From Nowhere

When Accountability Goes Rogue 💡I recently spoke with Dr. Zena Assaad for her podcast, Responsible Bytes. Video is above, below is an essay adapted from the conversation. "The system from nowhere" is a way of talking about AI systems that excludes its origin as a consciously, human-designed product. It

Computer power and human reason : from judgment to calculation

xii, 300 pages : 24 cm; Includes bibliographical references and index

EDISON BROWSER

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On working machines

In part one, on thinking machines, I explored two facets of the philosophy of artificial intelligence: “intelligence”, and consciousness. That left an important topic to consider for this post: the...

Responsible AI Principles and Approach | Microsoft AI

Discover Microsoft AI tools, industry-specific governance solutions, and responsible AI practices to make smarter, more informed decisions about AI implementation.

Toward a theory of network gatekeeping: A framework for exploring information control

Abstract Gatekeeping theories have been a popular heuristic for describing information control for years, but none have attained a full theoretical status in the context of networks. This article aims to propose a theory of network gatekeeping comprised of two components: identification and salience . Network gatekeeping identification lays out vocabulary and naming foundations through the identification of gatekeepers, gatekeeping, and gatekeeping mechanisms. Network gatekeeping salience , which is built on the bases of the network identification theory, utilizes this infrastructure to understand relationships among gatekeepers and between gatekeepers and gated, the entity subjected to a gatekeeping process. Network gatekeeping salience 1 Salience refers to the degree to which gatekeepers give priority to competing gated claims. proposes identifying gated and their salience to gatekeepers by four attributes: (a) their political power in relation to the gatekeeper, (b) their information production ability , (c) their relationship with the gatekeeper, and (d) their alternatives in the context of gatekeeping.

Conscious or Not

For as long as I remember, people have been arguing about whether machines could be intelligent or not.

Governments Can’t Agree on What AI Actually Is

Without clear definitions, governance is impossible.