Dr Abeba Birhane

@abeba.blacksky.app

53495 Followers 899 Following

Founder & PI @aial.ie. Assistant Professor of AI, School of Computer Science & Statistics, @tcddublin.bsky.social AI accountability, AI audits & evaluation, critical data studies. Cognitive scientist by training. Ethiopian in Ireland. She/her

There is a well-known story about the origin of the Nobel Prize, although historians have been unable to verify it, and some dismiss the story as a myth.[26] In 1888, the death of his brother Ludvig supposedly caused several newspapers to publish obituaries of Alfred in error. One French newspaper[which?] condemned him for his invention of military explosives—in many versions of the story, dynamite is quoted, although this was mainly used for civilian applications—and this is said to have brought about his decision to leave a better legacy after his death.[6] The obituary supposedly stated, Le marchand de la mort est mort ("The merchant of death is dead"),[6] and according to the myth went on to say, "Dr. Alfred Nobel, who became rich by finding ways to kill more people faster than ever before, died yesterday."[27] Nobel read the obituary and was appalled at the idea that he would be remembered in this way. His decision to posthumously donate the majority of his wealth to found the Nobel Prize has been credited to him wanting to leave behind a better legacy.[28][6] However, it has been questioned whether or not the obituary in question actually existed.[28] An actual obituary (pictured) stated "A man who can only with the utmost difficulty be considered a benefactor of mankind has died yesterday at Cannes. It is Mr. Nobel, inventor of dynamite. Mr. Nobel was Swedish." It was published on the first page of the Figaro on the 15th of April 1888 and retracted the next day.
Investors are pouring billions into defence technology start-ups as wars in Ukraine and the Gulf turn drones, autonomous vessels and battlefield AI into one of this year’s hottest venture capital bets. Companies in the sector have raised $12.3bn from VC funds since the start of 2026, almost double the amount raised in the same period last year and more than the $9.95bn achieved in the whole of last year, according to data compiled for the Financial Times by PitchBook. The surge comes as conflicts expose demand for next-generation weapons systems that are cheaper and faster to produce. However, with some funds willing to pay increasing multiples as they bet heightened government defence spending will endure, it has also raised concerns that parts of the market are overheating.
Table 1. Taxonomy of capture mechanisms with brief descriptions. The highlighted concepts denote five broad, high-level categories, each further comprising a set of detailed mechanism categories and descriptions.
"The most consequential decision are made by systems that cannot explain themselves, supplied by companies that answer to no-one, in conflicts that generate no accountability and no reckoning"
a presentation slide. It reads "agi and the new luddism" image of Brian Merchant on the corner
The river liffey, Dublin. Dramatic morning clouds, calm waters showing reflection of buildings from both sides of the river
Nearly 9 in 10 kids age 9 to 17 (86%) use or interact with AI, including about one in four (24%) who do so on a daily basis. Teens use AI more often, but use is widespread even among younger adolescents, with 81% of 9- to 12-year-olds, 89% of 13- to 15-year-olds, and 92% of 16- to 17-year-olds saying they use or interact with AI. Nearly 3 in 10 teens (29% of 13- to 17-year-olds) and 17% of 9- to 12-year-olds use AI daily. AI-generated summaries in search engine results are the most widely used form of AI among 9- to 17-year-olds, with 75% having used or interacted with them. Just over two-thirds (67%) have used AI chatbots, including 58% of 9- to 12-year-olds, 71% of 13- to 15-year-olds, and 77% of 16- to 17-year-olds. However, when kids are asked which AI they use most often, ChatGPT is by far the most frequently mentioned (by 40% of all 9- to 17-year-olds), followed by Google’s AI offerings, including Gemini, AI summaries, or other Google AI tools (24%).
Facebook's artificial intelligence robots shut down after they start talking to each other in their own language 'you i i i everything else' Andrew Griffin Thursday 10 September 2020 15:38 BST
light pink peonies pictured from the top capturing the full blossom
Inside the unseen operation to turbocharge Claude Code Shubhangi Goel 01 June 2026 05:02 AM An Anthropic project is using feedback from about 1,000 human software engineers to improve the performance of Claude Code,the AI coding tool whose recent advancements have disrupted the vibe-coding industry.
Opt-In Surveillance Is Approaching AIs with access to all our data will soon be able to vouch for us to others. As people come to trust AI judgments of character, not sharing one will look suspicious. Steven Veld on Jun 3, 2026
Content guidelines The session should explicitly address how AI can be used to serve humanity. Content must be original and not infringe on any copyrights or trademarks. If you will be using visuals, audio, or video that is copyright protected, you must provide consent from the original owner for the use of the content for your use in the AI for Good Global Summit. Content must be compatible with the values, purposes, and principles of the United Nations, in both content and presentation. Sessions should ensure regional and gender balance. Content shall not be of a profit-generating or otherwise commercial nature. Speakers must avoid any political endorsements, partisan messaging, or divisive rhetoric. We encourage speakers to focus on meaningful, solution-oriented dialogue that respects the diversity of our global audience. Please give thoughtful consideration to content that could be interpreted negatively by segments of the diverse, global audience. We encourage inclusive, respectful communication that resonates constructively. Speakers are welcome to share their perspectives and beliefs, provided this is done respectfully and without undermining or disparaging differing viewpoints. Please note that the International Telecommunication Union (ITU) has final editorial review of session titles, blurbs, and slides.
a picture of a page from a book detailing historical eugenics but feels uncannily like present day
paper title: Reckoning with the Political Economy of AI: Avoiding Decoys in Pursuit of Accountability authors: JANET VERTESI, Princeton University, Princeton, NJ USA DANAH BOYD, Cornell University, Ithaca, NY, USA ALEX TAYLOR, University of Edinburgh, Edinburgh, UK BENJAMIN SHESTAKOFSKY, Cornell University, Ithaca, NY USA abstract: The Project of AI is a world-building endeavor, wherein those who fund and develop AI systems both operate through and seek to sustain networks of power and wealth. As they expand their access to resources and configure our sociotechnical conditions, they benefit from the ways in which a suite of decoys animate scholars, critics, policymakers, journalists, and the public into co-constructing industry-empowering AI futures. Regardless of who constructs or nurtures them, these decoys often create the illusion of accountability while both masking the emerging political economies that the Project of AI has set into motion, and also contributing to the network-making power that is at the heart of the Project’s extraction and exploitation. Drawing on literature at the intersection of communication, science and technology studies, and economic sociology, we examine how the Project of AI is constructed. We then explore five decoys that seemingly critique—but in actuality co-constitute—AI’s emergent power relations and material political economy. We argue that advancing meaningful fairness or accountability in AI requires: 1) recognizing when and how decoys serve as a distraction, and 2) grappling directly with the material political economy of the Project of AI. Doing so will enable us to attend to the networks of power that make “AI” possible, spurring new visions for how to realize a more just technologically entangled world.
Sick and wrong: Ontario auditors find doctors' AI note takers routinely blow basic facts 60% of evaluated AI Scribe systems mixed up prescribed drugs in patient notes, auditors say Brandon Vigliarolo
Lessons from adjacent movements. Many of the mechanisms of capture used by Big AI, that we have discussed in this paper, mirror strategies that have historically been applied by similar industries such as Big Tobacco, Big Pharma, and Big Oil. Civil society’s efforts to hold big corporations accountable in these sectors is ongoing and has been met with significant challenges and has often fallen short of meaningfully countering corporate power [45, 50, 118]. Yet, there are remain lesson to be learned from these efforts and the braoder scholarship on corporate capture. For example, the OECD report on preventing policy capture in public decision-making [85] recommends to (i) level the playing field by engaging diverse stakeholders, (ii) ensure transparency and access to information, (iii) promote accountability via external control, effective competition, and regulatory policies, and (iv) define clear institutional codes of conduct, promote cultures of integrity, and establish appropriate frameworks for risk-management. Similarly, in the context of Big Tobacco, Lee [63] calls for appropriate separation between public and private interests, binding rules for government-industry interactions to manage conflicts-of-interests, enforcement of transparency and accountability practices, and safeguarding academic knowledge production from undue industry influence. A 2024 report [5] further calls for applying transferable lessons from the US Food and Drug Administration on how to regulate and hold Big AI accountable
Prior work has typically adopted a dichotomous theoretical lens – distinguishing information capture from influence on policymaking [108], whereas the most recurring mechanisms in our evidence also include the Elusion of law. These recurring violations and contentious interpretations of antitrust, privacy, copyright and labour laws call into question the effectiveness of enforcement. Such pressures, along with weakening the mandates of regulatory agencies, risk the normalisation of a de facto law in which Big AI operates outside the bounds of regulatory scrutiny. Furthermore, the stochastic nature of the underlying technology–where the technology cannot be reliably tested–coupled with the economic power of Big AI corporations enables a normalisation of “algorithmic states of exception” at scale. Defined by McQuillan [71], this indicates the application of algorithms as the de facto authority in contexts where their behaviour has not been or cannot be tested, creating conditions akin to martial law. The conjunction of law-flouting practice and technological affordances thus raises urgent concerns regarding the contemporary integrity of lawmaking institutions over their respective jurisdictions. Our annotation of narratives employed provide another qualitative avenue for insight into capture strategies, which can be further studied to understand the causal or system impact of different narratives on discourse, public conceptions of AI, and knowledge production. Our finding that there is substantial growth over time in the Discourse & Epistemic Influence (see Figure 2b) indicates that how AI is framed is becoming increasingly important. The consistent co-occurrence of each D&EI mechanism with approximately one corresponding mechanism from other categories indicates the significant role that public-facing campaigns play.
a table describing Taxonomy of capture mechanisms. highlighted concepts denote five broad, high-level categories, each further comprising a set of detailed mechanism categories and descriptions.
We use the term ‘Big AI’ to refer to the handful of companies that develop and mass deploy large-scale AI technologies – such as large pre-trained models – built on massive datasets collected through vast, centralised infrastructures, which, with increased integration into societal infrastructure, continue to exert outsized epistemic, economic, political, and societal influence. The term ‘Big AI ’ also encapsulates the structural consolidation of AI technologies by Big Tech as their core value proposition, central to their infrastructure, resources, and strategic investments [121]. While new entities such as OpenAI, Anthropic, DeepSeek and xAI are included in our definition due to their significant geo-political influence, ‘Big AI’ also marks the shift of existing Big Tech companies – Alphabet, Meta, Amazon, Microsoft, Apple, NVIDIA – towards becoming “AI-first” companies, further expanding their unprecedented power and influence across all economic sectors and aspects of public life.
Title: Big AI’s Regulatory Capture: Mapping Industry Interference and Government Complicity abstract: Over the past decade, the AI industry has come to exert an unprecedented economic, political and societal power and influence. The well-functioning of regulatory and oversight structures and processes that govern the industry thus have paramount ramifications for everything from fostering public trust in systems marketed as AI, the credibility of scientific knowledge, educational and healthcare services and products, information ecosystems, the environment, rule of law and integrity of democratic process. In this paper, we first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem. Grounded in design science research (DSR) methodologies and extensive scoping review of existing literature and media reports, our taxonomy of capture consists of 27 mechanisms across five categories. We then develop an annotation template incorporating our taxonomy, and manually annotate and analyse 100 news articles. The purpose behind this analysis is twofold: validate our taxonomy and provide a novel quantification of capture mechanisms and dominant narratives. Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture. We find that the most recurring categories of mechanisms are Discourse & Epistemic Influence, concerning narrative framing, and Elusion of law, related to violations and contentious interpretations of antitrust, privacy, copyright and labour laws. We further find that Regulation stifles innovation, Red tape and National Interest are the most frequently invoked narratives used to rationalise capture. We emphasize the extent and breadth of regulatory capture by coalescing forces — Big AI and governments — as something policy makers and the public ought to treat as an emergency.
email reads: Dear Dr. Abeba Birhane, Thank you so much for sharing your time and knowledge of artificial intelligence with me. I learned a lot from you, including the facial recognition mistake and the certain biases in artificial intelligence's system. Because of you, I will be better able to share information about AI when I present my project to my class soon. Thank you again,
reads: Designed to Be the Safest Truck on the Road With a low center of gravity, impact-absorbing castings and the lowest probability of rollover for any truck tested by NHTSA, Cybertruck has earned a five-star overall safety rating
This prompt is then used to direct a language model in judging or rating an output. Yet how valid can the findings be when the artefact being studied is also the instrument used? What gaps and feedback loops occur when language models are used to evaluate language models? Without an evaluation of the ‘judge’ LLM itself, how can we know if it will correctly evaluate the relevant concept? It is simply assumed that any mainstream LLM has enough ‘knowledge’ to measure any arbitrary concept and will be able to accurately and reliably apply that knowledge given sufficient prompting – assumptions which in many cases have been proven incorrect. Even for concepts where LLMs do have this function, what are the implications of basing the field’s sensemaking about its own progress on a small number of models developed behind closed doors?
an image of the liffey river, Dublin. calm waters, blue sky. reflection of the building visible on the river
Image of Ta-Nehisi Coates with a caption that reads "understandable, but i guess was silence not an option"
Screenshot reads: Jackie Berté, a spokesperson for Google, said the company complies with the law and that the audit was “based on a fundamental misunderstanding of how our products work.” A spokesperson for Meta didn’t immediately respond to a request for comment. “The idea that I misunderstand anything is a demonstrable falsehood,” Libert said, pointing out his work on cookie policy at Google. “I would assert that, when I was there, I knew more about it than anybody else,” he added.