Paper by Marion Oswald, Luke Chambers, Ellen P. Goodman, Pam Ugwudike, and Miri Zilka: “1. The UK Government’s draft ‘Algorithmic Transparency Standard’ is intended to provide a standardised way for public bodies and government departments to provide information about how algorithmic tools are being used to support decisions. The research discussed in this report was conducted in parallel to the piloting of the Standard by the Cabinet Office and the Centre for Data Ethics and Innovation.
2. We conducted semi-structured interviews with respondents from across UK policing and commercial bodies involved in policing technologies. Our aim was to explore the implications for police forces of participation in the Standard, to identify rewards, risks, challenges for the police, and areas where the Standard could be improved, and therefore to contribute to the exploration of policy options for expansion of participation in the Standard.
3. Algorithmic transparency is both achievable for policing and could bring significant rewards. A key reward of police participation in the Standard is that it provides the opportunity to demonstrate proficient implementation of technology-driven policing, thus enhancing earned trust. Research participants highlighted the public good that could result from the considered use of algorithms.
4. Participants noted, however, a risk of misperception of the dangers of policing technology, especially if use of algorithmic tools was not appropriately compared to the status quo and current methods…(More)”.
Artificial Intelligence and Democracy
Open Access Book by Jérôme Duberry on “Risks and Promises of AI-Mediated Citizen–Government Relations….What role does artificial intelligence (AI) play in the citizen–government rela-tions? Who is using this technology and for what purpose? How does the use of AI influence power relations in policy-making, and the trust of citizens in democratic institutions? These questions led to the writing of this book. While the early developments of e-democracy and e-participation can be traced back to the end of the 20th century, the growing adoption of smartphones and mobile applications by citizens, and the increased capacity of public adminis-trations to analyze big data, have enabled the emergence of new approaches. Online voting, online opinion polls, online town hall meetings, and online dis-cussion lists of the 1990s and early 2000s have evolved into new generations of policy-making tactics and tools, enabled by the most recent developments in information and communication technologies (ICTs) (Janssen & Helbig, 2018). Online platforms, advanced simulation websites, and serious gaming tools are progressively used on a larger scale to engage citizens, collect their opinions, and involve them in policy processes…(More)”.
First regulatory sandbox on Artificial Intelligence presented
European Commission: “The sandbox aims to bring competent authorities close to companies that develop AI to define best practices that will guide the implementation of the future European Commission’s AI Regulation (Artificial Intelligence Act). This would also ensure that the legistlation can be implemented in two years.
The regulatory sandbox is a way to connect innovators and regulators and provide a controlled environment for them to cooperate. Such a collaboration between regulators and innovators should facilitates the development, testing and validation of innovative AI systems with a view to ensuring compliance with the requirements of the AI Regulation.
While the entire ecosystem is preparing for the AI Act, this sandbox initiative is expected to generate easy-to-follow, future-proof best practice guidelines and other supporting materials. Such outputs are expected to facilitate the implementation of rules by companies, in particular SMEs and start-ups.
This sandbox pilot initiated by the Spanish government will look at operationalising the requirements of the future AI regulation as well as other features such as conformity assessments or post-market activities.
Thanks to this pilot experience, obligations and how to implement them will be documented, for AI system providers (participants of the sandbox) and systematised in a good practice and lessons learnt implementation guidelines. The deliverables will also include methods to control and follow up that are useful for supervising national authorities in charge of implementing the supervisory mechanisms that the regulation stablishes.
In order to strengthen the cooperation of all possible actors at the European level, this exercise will remain open to other Member States that will be able to follow or join the pilot in what could potentially become a pan-European AI regulatory sandbox. Cooperation at EU level with other Member States will be pursued within the framework of the Expert Group on AI and Digitalisation of Businesses set up by the Commission.
The financing of this sandbox is drawn from the Recovery and Resilience Funds assigned to the Spanish Government, through the Spanish Recovery, Transformation and Resilience Plan, and in particular through the Spanish National AI Strategy (Component 16 of the Plan). The overall budget for the pilot will be approximately 4.3M EUR for approximately three years…(More)”.
The Model Is The Message
Essay by Benjamin Bratton and Blaise Agüera y Arcas: “An odd controversy appeared in the news cycle last month when a Google engineer, Blake Lemoine, was placed on leave after publicly releasing transcripts of conversations with LaMDA, a chatbot based on a Large Language Model (LLM) that he claims is conscious, sentient and a person.
Like most other observers, we do not conclude that LaMDA is conscious in the ways that Lemoine believes it to be. His inference is clearly based in motivated anthropomorphic projection. At the same time, it is also possible that these kinds of artificial intelligence (AI) are “intelligent” — and even “conscious” in some way — depending on how those terms are defined.
Still, neither of these terms can be very useful if they are defined in strongly anthropocentric ways. An AI may also be one and not the other, and it may be useful to distinguish sentience from both intelligence and consciousness. For example, an AI may be genuinely intelligent in some way but only sentient in the restrictive sense of sensing and acting deliberately on external information. Perhaps the real lesson for philosophy of AI is that reality has outpaced the available language to parse what is already at hand. A more precise vocabulary is essential.
AI and the philosophy of AI have deeply intertwined histories, each bending the other in uneven ways. Just like core AI research, the philosophy of AI goes through phases. Sometimes it is content to apply philosophy (“what would Kant say about driverless cars?”) and sometimes it is energized to invent new concepts and terms to make sense of technologies before, during and after their emergence. Today, we need more of the latter.
We need more specific and creative language that can cut the knots around terms like “sentience,” “ethics,” “intelligence,” and even “artificial,” in order to name and measure what is already here and orient what is to come. Without this, confusion ensues — for example, the cultural split between those eager to speculate on the sentience of rocks and rivers yet dismiss AI as corporate PR vs. those who think their chatbots are persons because all possible intelligence is humanlike in form and appearance. This is a poor substitute for viable, creative foresight. The curious case of synthetic language — language intelligently produced or interpreted by machines — is exemplary of what is wrong with present approaches, but also demonstrative of what alternatives are possible…(More)”.
Artificial Intelligence in the City: Building Civic Engagement and Public Trust
Collection of essays edited by Ana Brandusescu, Ana, and Jess Reia: “After navigating various challenging policy and regulatory contexts over the years, in different regions, we joined efforts to create a space that offers possibilities for engagement focused on the expertise, experiences and hopes to shape the future of technology in urban areas. The AI in the City project emerged as an opportunity to connect people, organizations, and resources in the networks we built over the last decade of work on research and advocacy in tech policy. Sharing non-Western and Western perspectives from five continents, the contributors questioned, challenged, and envisioned ways public trust and meaningful civic engagement can flourish and persist as data and AI become increasingly pervasive in our lives. This collection of essays brings together a group of multidisciplinary scholars, activists, and practitioners working on a diverse range of initiatives to map strategies going forward. Divided into five parts, the collection brings into focus: 1) Meaningful engagement and public participation; 2) Addressing inequalities and building trust; 3) Public and private boundaries in tech policy; 4) Legal perspectives and mechanisms for accountability; and 5) New directions for local and urban governance. The focus on civil society and academia was deliberate: a way to listen to and learn with people who have dedicated many years to public interest advocacy, governance and policy that represents the interests of their communities…(More)”.
Your Boss Is an Algorithm: Artificial Intelligence, Platform Work and Labour
Book by Antonio Aloisi and Valerio De Stefano: “What effect do robots, algorithms, and online platforms have on the world of work? Using case studies and examples from across the EU, the UK, and the US, this book provides a compass to navigate this technological transformation as well as the regulatory options available, and proposes a new map for the era of radical digital advancements.
From platform work to the gig-economy and the impact of artificial intelligence, algorithmic management, and digital surveillance on workplaces, technology has overwhelming consequences for everyone’s lives, reshaping the labour market and straining social institutions. Contrary to preliminary analyses forecasting the threat of human work obsolescence, the book demonstrates that digital tools are more likely to replace managerial roles and intensify organisational processes in workplaces, rather than opening the way for mass job displacement.
Can flexibility and protection be reconciled so that legal frameworks uphold innovation? How can we address the pervasive power of AI-enabled monitoring? How likely is it that the gig-economy model will emerge as a new organisational paradigm across sectors? And what can social partners and political players do to adopt effective regulation?
Technology is never neutral. It can and must be governed, to ensure that progress favours the many. Digital transformation can be an essential ally, from the warehouse to the office, but it must be tested in terms of social and political sustainability, not only through the lenses of economic convenience. Your Boss Is an Algorithm offers a guide to explore these new scenarios, their promises, and perils…(More)”
Human-centred mechanism design with Democratic AI
Paper by Raphael Koster et al: “Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that humans prefer by majority. A large group of humans played an online investment game that involved deciding whether to keep a monetary endowment or to share it with others for collective benefit. Shared revenue was returned to players under two different redistribution mechanisms, one designed by the AI and the other by humans. The AI discovered a mechanism that redressed initial wealth imbalance, sanctioned free riders and successfully won the majority vote. By optimizing for human preferences, Democratic AI offers a proof of concept for value-aligned policy innovation…(More)”.
Crime Prediction Keeps Society Stuck in the Past
Article by Chris Gilliard: “…All of these policing systems operate on the assumption that the past determines the future. In Discriminating Data: Correlation, Neighborhoods, and the New Politics of Recognition, digital media scholar Wendy Hui Kyong Chun argues that the most common methods used by technologies such as PredPol and Chicago’s heat list to make predictions do nothing of the sort. Rather than anticipating what might happen out of the myriad and unknowable possibilities on which the very idea of a future depends, machine learning and other AI-based methods of statistical correlation “restrict the future to the past.” In other words, these systems prevent the future in order to “predict” it—they ensure that the future will be just the same as the past was.
“If the captured and curated past is racist and sexist,” Chun writes, “these algorithms and models will only be verified as correct if they make sexist and racist predictions.” This is partly a description of the familiar garbage-in/garbage-out problem with all data analytics, but it’s something more: Ironically, the putatively “unbiased” technology sold to us by promoters is said to “work” precisely when it tells us that what is contingent in history is in fact inevitable and immutable. Rather than helping us to manage social problems like racism as we move forward, as the McDaniel case shows in microcosm, these systems demand that society not change, that things that we should try to fix instead must stay exactly as they are.
It’s a rather glaring observation that predictive policing tools are rarely if ever (with the possible exception of the parody “White Collar Crime Risk Zone” project) focused on wage theft or various white collar crimes, even though the dollar amounts of those types of offenses far outstrip property crimes in terms of dollar value by several orders of magnitude. This gap exists because of how crime exists in the popular imagination. For instance, news reports in recent weeks bludgeoned readers with reports of a so-called “crime wave” of shoplifting at high-end stores. Yet just this past February, Amazon agreed to pay regulators a whopping $61.7 million, the amount the FTC says the company shorted drivers in a two-and-a-half-year period. That story received a fraction of the coverage, and aside from the fine, there will be no additional charges.
The algorithmic crystal ball that promises to predict and forestall future crimes works from a fixed notion of what a criminal is, where crimes occur, and how they are prosecuted (if at all). Those parameters depend entirely on the power structure empowered to formulate them—and very often the explicit goal of those structures is to maintain existing racial and wealth hierarchies. This is the same set of carceral logics that allow the placement of children into gang databases, or the development of a computational tool to forecast which children will become criminals. The process of predicting the lives of children is about cementing existing realities rather than changing them. Entering children into a carceral ranking system is in itself an act of violence, but as in the case of McDaniel, it also nearly guarantees that the system that sees them as potential criminals will continue to enact violence on them throughout their lifetimes…(More)”.
How Does the Public Sector Identify Problems It Tries to Solve with AI?
Article by Maia Levy Daniel: “A correct analysis of the implementation of AI in a particular field or process needs to start by identifying if there actually is a problem to be solved. For instance, in the case of job matching, the problem would be related to the levels of unemployment in the country, and presumably addressing imbalances in specific fields. Then, would AI be the best way to address this specific problem? Are there any alternatives? Is there any evidence that shows that AI would be a better tool? Building AI systems is expensive and the funds being used by the public sector come from taxpayers. Are there any alternatives that could be less expensive?
Moreover, governments must understand from the outset that these systems could involve potential risks for civil and human rights. Thus, it should be justified in detail why the government might be choosing a more expensive or riskier option. A potential guide to follow is the one developed by the UK’s Office for Artificial Intelligence on how to use AI in the public sector. This guide includes a section specifically devoted to how to assess whether AI is the right solution to a problem.
AI is such a buzzword that it has become appealing for governments to use as a solution to any public problem, without even starting to look for available alternatives. Although automation could accelerate decision-making processes, speed should not be prioritized over quality or over human rights protection. As Daniel Susser argues in his recent paper, the speed at which automated decisions are reached has normative implications. By incorporating digital technologies in decision-making processes, temporal norms and values that govern them are impacted, disrupting prior norms, re-calibrating balanced trade-offs, or displacing automation’s costs. As Susser suggests, speed is not necessarily bad; however, “using computational tools to speed up (or slow down) certain decisions is not a ‘neutral’ adjustment without further explanations.”
So, conducting a thorough diagnosis including the identification of the specific problem to address and the best way to address it is key to protecting citizens’ rights. And this is why transparency must be mandatory. As citizens, we have a right to know how these processes are being conceived and designed, the reasons governments choose to implement technologies, as well as the risks involved.
In addition, maybe a good way to ultimately approach the systemic problem and change the structure of incentives is to stop using the pretentious terms “artificial intelligence”, “AI”, and “machine learning”, as Emily Tucker, the Executive Director of the Center on Privacy & Technology at Georgetown Law Center announced the Center would do. As Tucker explained, these terms are confusing for the average person, and the way they are typically employed makes us think it’s a machine rather than human beings making the decisions. By removing marketing terms from the equation and giving more visibility to the humans involved, these technologies may not ultimately seem so exotic…(More)”.
What AI Can Tell Us About Intelligence
Essay by Yann LeCun and Jacob Browning: “If there is one constant in the field of artificial intelligence it is exaggeration: there is always breathless hype and scornful naysaying. It is helpful to occasionally take stock of where we stand.
The dominant technique in contemporary AI is deep learning (DL) neural networks, massive self-learning algorithms which excel at discerning and utilizing patterns in data. Since their inception, critics have prematurely argued that neural networks had run into an insurmountable wall — and every time, it proved a temporary hurdle. In the 1960s, they could not solve non-linear functions. That changed in the 1980s with backpropagation, but the new wall was how difficult it was to train the systems. The 1990s saw a rise of simplifying programs and standardized architectures which made training more reliable, but the new problem was the lack of training data and computing power.
In 2012, when contemporary graphics cards could be trained on the massive ImageNet dataset, DL went mainstream, handily besting all competitors. But then critics spied a new problem: DL required too much hand-labelled data for training. The last few years have rendered this criticism moot, as self-supervised learning has resulted in incredibly impressive systems, such as GPT-3, which do not require labeled data.
Today’s seemingly insurmountable wall is symbolic reasoning, the capacity to manipulate symbols in the ways familiar from algebra or logic. As we learned as children, solving math problems involves a step-by-step manipulation of symbols according to strict rules (e.g., multiply the furthest right column, carry the extra value to the column to the left, etc.). Gary Marcus, author of “The Algebraic Mind”and co-author (with Ernie Davis) of “Rebooting AI,”recently argued that DL is incapable of further progress because neural networks struggle with this kind of symbol manipulation. By contrast, many DL researchers are convinced that DL is already engaging in symbolic reasoning and will continue to improve at it.
At the heart of this debate are two different visions of the role of symbols in intelligence, both biological and mechanical: one holds that symbolic reasoning must be hard-coded from the outset and the other holds it can be learned through experience, by machines and humans alike. As such, the stakes are not just about the most practical way forward, but also how we should understand human intelligence — and, thus, how we should pursue human-level artificial intelligence…(More)”.