Open Access Book edited by Damian Okaibedi Eke, Kutoma Wakunuma, and Simisola Akintoye: “In the last few years, a growing and thriving AI ecosystem has emerged in Africa. Within this ecosystem, there are local tech spaces as well as a number of internationally driven technology hubs and centres established by big tech companies such as Twitter, Google, Facebook, Alibaba Group, Huawei, Amazon and Microsoft have significantly increased the development and deployment of AI systems in Africa. While these tech spaces and hubs are focused on using AI to meet local challenges (e.g. poverty, illiteracy, famine, corruption, environmental disasters, terrorism and health crisis), the ethical, legal and socio-cultural implications of AI in Africa have largely been ignored. To ensure that Africans benefit from the attendant gains of AI, ethical, legal and socio-cultural impacts of AI need to be robustly considered and mitigated…(More)”.
Human-AI Teaming
Report by the National Academies of Sciences, Engineering, and Medicine: “Although artificial intelligence (AI) has many potential benefits, it has also been shown to suffer from a number of challenges for successful performance in complex real-world environments such as military operations, including brittleness, perceptual limitations, hidden biases, and lack of a model of causation important for understanding and predicting future events. These limitations mean that AI will remain inadequate for operating on its own in many complex and novel situations for the foreseeable future, and that AI will need to be carefully managed by humans to achieve their desired utility.
Human-AI Teaming: State-of-the-Art and Research Needs examines the factors that are relevant to the design and implementation of AI systems with respect to human operations. This report provides an overview of the state of research on human-AI teaming to determine gaps and future research priorities and explores critical human-systems integration issues for achieving optimal performance…(More)”
The ethics of artificial intelligence, UNESCO and the African Ubuntu perspective
Paper by Dorine Eva van Norren: “This paper aims to demonstrate the relevance of worldviews of the global south to debates of artificial intelligence, enhancing the human rights debate on artificial intelligence (AI) and critically reviewing the paper of UNESCO Commission on the Ethics of Scientific Knowledge and Technology (COMEST) that preceded the drafting of the UNESCO guidelines on AI. Different value systems may lead to different choices in programming and application of AI. Programming languages may acerbate existing biases as a people’s worldview is captured in its language. What are the implications for AI when seen from a collective ontology? Ubuntu (I am a person through other persons) starts from collective morals rather than individual ethics…
Metaphysically, Ubuntu and its conception of social personhood (attained during one’s life) largely rejects transhumanism. When confronted with economic choices, Ubuntu favors sharing above competition and thus an anticapitalist logic of equitable distribution of AI benefits, humaneness and nonexploitation. When confronted with issues of privacy, Ubuntu emphasizes transparency to group members, rather than individual privacy, yet it calls for stronger (group privacy) protection. In democratic terms, it promotes consensus decision-making over representative democracy. Certain applications of AI may be more controversial in Africa than in other parts of the world, like care for the elderly, that deserve the utmost respect and attention, and which builds moral personhood. At the same time, AI may be helpful, as care from the home and community is encouraged from an Ubuntu perspective. The report on AI and ethics of the UNESCO World COMEST formulated principles as input, which are analyzed from the African ontological point of view. COMEST departs from “universal” concepts of individual human rights, sustainability and good governance which are not necessarily fully compatible with relatedness, including future and past generations. Next to rules based approaches, which may hamper diversity, bottom-up approaches are needed with intercultural deep learning algorithms…(More)”.
How the algorithm tipped the balance in Ukraine
David Ignatius at The Washington Post: “Two Ukrainian military officers peer at a laptop computer operated by a Ukrainian technician using software provided by the American technology company Palantir. On the screen are detailed digital maps of the battlefield at Bakhmut in eastern Ukraine, overlaid with other targeting intelligence — most of it obtained from commercial satellites.
As we lean closer, we see can jagged trenches on the Bakhmut front, where Russian and Ukrainian forces are separated by a few hundred yards in one of the bloodiest battles of the war. A click of the computer mouse displays thermal images of Russian and Ukrainian artillery fire; another click shows a Russian tank marked with a “Z,” seen through a picket fence, an image uploaded by a Ukrainian spy on the ground.
If this were a working combat operations center, rather than a demonstration for a visiting journalist, the Ukrainian officers could use a targeting program to select a missile, artillery piece or armed drone to attack the Russian positions displayed on the screen. Then drones could confirm the strike, and a damage assessment would be fed back into the system.
This is the “wizard war” in the Ukraine conflict — a secret digital campaign that has never been reported before in detail — and it’s a big reason David is beating Goliath here. The Ukrainians are fusing their courageous fighting spirit with the most advanced intelligence and battle-management software ever seen in combat.
“Tenacity, will and harnessing the latest technology give the Ukrainians a decisive advantage,” Gen. Mark A. Milley, chairman of the Joint Chiefs of Staff, told me last week. “We are witnessing the ways wars will be fought, and won, for years to come.”
I think Milley is right about the transformational effect of technology on the Ukraine battlefield. And for me, here’s the bottom line: With these systems aiding brave Ukrainian troops, the Russians probably cannot win this war…(More)” See also Part 2.
How to spot AI-generated text
Article by Melissa Heikkilä: “This sentence was written by an AI—or was it? OpenAI’s new chatbot, ChatGPT, presents us with a problem: How will we know whether what we read online is written by a human or a machine?
Since it was released in late November, ChatGPT has been used by over a million people. It has the AI community enthralled, and it is clear the internet is increasingly being flooded with AI-generated text. People are using it to come up with jokes, write children’s stories, and craft better emails.
ChatGPT is OpenAI’s spin-off of its large language model GPT-3, which generates remarkably human-sounding answers to questions that it’s asked. The magic—and danger—of these large language models lies in the illusion of correctness. The sentences they produce look right—they use the right kinds of words in the correct order. But the AI doesn’t know what any of it means. These models work by predicting the most likely next word in a sentence. They haven’t a clue whether something is correct or false, and they confidently present information as true even when it is not.
In an already polarized, politically fraught online world, these AI tools could further distort the information we consume. If they are rolled out into the real world in real products, the consequences could be devastating.
We’re in desperate need of ways to differentiate between human- and AI-written text in order to counter potential misuses of the technology, says Irene Solaiman, policy director at AI startup Hugging Face, who used to be an AI researcher at OpenAI and studied AI output detection for the release of GPT-3’s predecessor GPT-2.
New tools will also be crucial to enforcing bans on AI-generated text and code, like the one recently announced by Stack Overflow, a website where coders can ask for help. ChatGPT can confidently regurgitate answers to software problems, but it’s not foolproof. Getting code wrong can lead to buggy and broken software, which is expensive and potentially chaotic to fix…(More)”.
How AI That Powers Chatbots and Search Queries Could Discover New Drugs
Karen Hao at The Wall Street Journal: “In their search for new disease-fighting medicines, drug makers have long employed a laborious trial-and-error process to identify the right compounds. But what if artificial intelligence could predict the makeup of a new drug molecule the way Google figures out what you’re searching for, or email programs anticipate your replies—like “Got it, thanks”?
That’s the aim of a new approach that uses an AI technique known as natural language processing—the same technology that enables OpenAI’s ChatGPT to generate human-like responses—to analyze and synthesize proteins, which are the building blocks of life and of many drugs. The approach exploits the fact that biological codes have something in common with search queries and email texts: Both are represented by a series of letters.
Proteins are made up of dozens to thousands of small chemical subunits known as amino acids, and scientists use special notation to document the sequences. With each amino acid corresponding to a single letter of the alphabet, proteins are represented as long, sentence-like combinations.
Natural language algorithms, which quickly analyze language and predict the next step in a conversation, can also be applied to this biological data to create protein-language models. The models encode what might be called the grammar of proteins—the rules that govern which amino acid combinations yield specific therapeutic properties—to predict the sequences of letters that could become the basis of new drug molecules. As a result, the time required for the early stages of drug discovery could shrink from years to months.
“Nature has provided us with tons of examples of proteins that have been designed exquisitely with a variety of functions,” says Ali Madani, founder of ProFluent Bio, a Berkeley, Calif.-based startup focused on language-based protein design. “We’re learning the blueprint from nature.”…(More)”.
Smart OCR – Advancing the Use of Artificial Intelligence with Open Data
Article by Parth Jain, Abhinay Mannepalli, Raj Parikh, and Jim Samuel: “Optical character recognition (OCR) is growing at a projected compounded annual growth rate (CAGR) of 16%, and is expected to have a value of 39.7 billion USD by 2030, as estimated by Straits research. There has been a growing interest in OCR technologies over the past decade. Optical character recognition is the technological process for transforming images of typed, handwritten, scanned, or printed texts into machine-encoded and machine-readable texts (Tappert, et al., 1990). OCR can be used with a broad range of image or scan formats – for example, these could be in the form of a scanned document such as a .pdf file, a picture of a piece of paper in .png or .jpeg format, or images with embedded text, such as characters on a coffee cup, title on the cover page of a book, the license number on vehicular plates, and images of code on websites. OCR has proven to be a valuable technological process for tackling the important challenge of transforming non-machine-readable data into machine readable data. This enables the use of natural language processing and computational methods on information-rich data which were previously largely non-processable. Given the broad array of scanned and image documents in open government data and other open data sources, OCR holds tremendous promise for value generation with open data.
Open data has been defined as “being data that is made freely available for open consumption, at no direct cost to the public, which can be efficiently located, filtered, downloaded, processed, shared, and reused without any significant restrictions on associated derivatives, use, and reuse” (Chidipothu et al., 2022). Large segments of open data contain images, visuals, scans, and other non-machine-readable content. The size and complexity associated with the manual analysis of such content is prohibitive. The most efficient way would be to establish standardized processes for transforming documents into their OCR output versions. Such machine-readable text could then be analyzed using a range of NLP methods. Artificial Intelligence (AI) can be viewed as being a “set of technologies that mimic the functions and expressions of human intelligence, specifically cognition and logic” (Samuel, 2021). OCR was one of the earliest AI technologies implemented. The first ever optical reader to identify handwritten numerals was the advanced reading machine “IBM 1287,” presented at the World Fair in New York in 1965 (Mori, et al., 1990). The value of open data is well established – however, the extent of usefulness of open data is dependent on “accessibility, machine readability, quality” and the degree to which data can be processed by using analytical and NLP methods (data.gov, 2022; John, et al., 2022)…(More)”
What China’s Algorithm Registry Reveals about AI Governance
Article by Matt Sheehan, and Sharon Du: “For the past year, the Chinese government has been conducting some of the earliest experiments in building regulatory tools to govern artificial intelligence (AI). In that process, China is trying to tackle a problem that will soon face governments around the world: Can regulators gain meaningful insight into the functioning of algorithms, and ensure they perform within acceptable bounds?
One particular tool deserves attention both for its impact within China, and for the lessons technologists and policymakers in other countries can draw from it: a mandatory registration system created by China’s internet regulator for recommendation algorithms.
Although the full details of the registry are not public, by digging into its online instruction manual, we can reveal new insights into China’s emerging regulatory architecture for algorithms.
The algorithm registry was created by China’s 2022 regulation on recommendation algorithms (English translation), which came into effect in March of this year and was led by the Cyberspace Administration of China (CAC). China’s algorithm regulation has largely focused on the role recommendation algorithms play in disseminating information, requiring providers to ensure that they don’t “endanger national security or the social public interest” and to “give an explanation” when they harm the legitimate interests of users. Other provisions sought to address monopolistic behavior by platforms and hot-button social issues, such as the role that dispatching algorithms play in creating dangerous labor conditions for Chinese delivery drivers…(More)”
Language and the Rise of the Algorithm
Book by Jeffrey M. Binder: “Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians well before the computer age: How do you draw the line between computational rules and the complexities of making systems comprehensible to people? By attending to this question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day.
Here Jeffrey M. Binder offers a compelling tour of four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz’s calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole’s nineteenth-century logic system; and the early programming language ALGOL, short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. Machine learning, in its increasing dependence on words, erodes the line between technical and everyday language, revealing the urgent stakes underlying this boundary.
The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction…(More)”.
The Ethics of Automated Warfare and Artificial Intelligence
Essay series introduced by Bessma Momani, Aaron Shull and Jean-François Bélanger: “…begins with a piece written by Alex Wilner titled “AI and the Future of Deterrence: Promises and Pitfalls.” Wilner looks at the issue of deterrence and provides an account of the various ways AI may impact our understanding and framing of deterrence theory and its practice in the coming decades. He discusses how different countries have expressed diverging views over the degree of AI autonomy that should be permitted in a conflict situation — as those more willing to cut humans out of the decision-making loop could gain a strategic advantage. Wilner’s essay emphasizes that differences in states’ technological capability are large, and this will hinder interoperability among allies, while diverging views on regulation and ethical standards make global governance efforts even more challenging.
Looking to the future of non-state use of drones as an example, the weapon technology transfer from nation-state to non-state actors can help us to understand how next-generation technologies may also slip into the hands of unsavoury characters such as terrorists, criminal gangs or militant groups. The effectiveness of Ukrainian drone strikes against the much larger Russian army should serve as a warning to Western militaries, suggests James Rogers in his essay “The Third Drone Age: Visions Out to 2040.” This is a technology that can level the field by asymmetrically advantaging conventionally weaker forces. The increased diffusion of drone technology enhances the likelihood that future wars will also be drone wars, whether these drones are autonomous systems or not. This technology, in the hands of non-state actors, implies future Western missions against, say, insurgent or guerilla forces will be more difficult.
Data is the fuel that powers AI and the broader digital transformation of war. In her essay “Civilian Data in Cyber Conflict: Legal and Geostrategic Considerations,” Eleonore Pauwels discusses how offensive cyber operations are aiming to alter the very data sets of other actors to undermine adversaries — whether through targeting centralized biometric facilities or individuals’ DNA sequence in genomic analysis databases, or injecting fallacious data into satellite imagery used in situational awareness. Drawing on the implications of international humanitarian law, Pauwels argues that adversarial data manipulation constitutes another form of “grey zone” operation that falls below a threshold of armed conflict. She evaluates the challenges associated with adversarial data manipulation, given that there is no internationally agreed upon definition of what constitutes cyberattacks or cyber hostilities within international humanitarian law (IHL).
In “AI and the Actual International Humanitarian Law Accountability Gap,” Rebecca Crootoff argues that technologies can complicate legal analysis by introducing geographic, temporal and agency distance between a human’s decision and its effects. This makes it more difficult to hold an individual or state accountable for unlawful harmful acts. But in addition to this added complexity surrounding legal accountability, novel military technologies are bringing an existing accountability gap in IHL into sharper focus: the relative lack of legal accountability for unintended civilian harm. These unintentional acts can be catastrophic, but technically within the confines of international law, which highlights the need for new accountability mechanisms to better protect civilians.
Some assert that the deployment of autonomous weapon systems can strengthen compliance with IHL by limiting the kinetic devastation of collateral damage, but AI’s fragility and apparent capacity to behave in unexpected ways poses new and unexpected risks. In “Autonomous Weapons: The False Promise of Civilian Protection,” Branka Marijan opines that AI will likely not surpass human judgment for many decades, if ever, suggesting that there need to be regulations mandating a certain level of human control over weapon systems. The export of weapon systems to states willing to deploy them on a looser chain-of-command leash should be monitored…(More)”.