Nature-rich nations push for biodata payout

Curated on Posted on by Stefaan Verhulst

Article by Lee Harris: “Before the current generation of weight-loss drugs, there was hoodia, a cactus that grows in southern Africa’s Kalahari Desert, and which members of the region’s San tribe have long used to stave off hunger. UK-based Phytopharm licensed the active ingredient in the cactus in 1996, and made numerous attempts to commercialise weight-loss products derived from it.

The company won licensing deals with Pfizer and Unilever, but drew outrage from campaigners who argued that the country was ripping off indigenous groups that had made the discovery. Indignation grew after the chief executive said it could not compensate local tribes because “the people who discovered the plant have disappeared”. (They had not).

This is just one example of companies using biological resources discovered in other countries for financial gain. The UN has attempted to set fairer terms with treaties such as the 1992 Convention on Biological Diversity, which deals with the sharing of genetic resources. But this approach has been seen by many developing countries as unsatisfactory. And earlier tools governing trade in plants and microbes may become less useful as biological data is now frequently transmitted in the form of so-called digital sequence information — the genetic code derived from those physical resources.

Now, the UN is working on a fund to pay stewards of biodiversity — notably communities in lower-income countries — for discoveries made with genetic data from their ecosystems. The mechanism was established in 2022 as part of the Conference of Parties to the UN Convention on Biological Diversity, a sister process to the climate “COP” initiative. But the question of how it will be governed and funded will be on the table at the October COP16 summit in Cali, Colombia.

If such a fund comes to fruition — a big “if” — it could raise billions for biodiversity goals. The sectors that depend on this genetic data — notably, pharmaceuticals, biotech and agribusiness — generate revenues exceeding $1tn annually, and African countries plan to push for these sectors to contribute 1 per cent of all global retail sales to the fund, according to Bloomberg.

There’s reason to temper expectations, however. Such a fund would lack the power to compel national governments or industries to pay up. Instead, the strategy is focused around raising ambition — and public pressure — for key industries to make voluntary contributions…(More)”.

The New Artificial Intelligentsia

Curated on Posted on by Stefaan Verhulst

Essay by Ruha Benjamin: “In the Fall of 2016, I gave a talk at the Institute for Advanced Study in Princeton titled “Are Robots Racist?” Headlines such as “Can Computers Be Racist? The Human-Like Bias of Algorithms,” “Artificial Intelligence’s White Guy Problem,” and “Is an Algorithm Any Less Racist Than a Human?” had captured my attention in the months before. What better venue to discuss the growing concerns about emerging technologies, I thought, than an institution established during the early rise of fascism in Europe, which once housed intellectual giants like J. Robert Oppenheimer and Albert Einstein, and prides itself on “protecting and promoting independent inquiry.”

My initial remarks focused on how emerging technologies reflect and reproduce social inequities, using specific examples of what some termed “algorithmic discrimination” and “machine bias.” A lively discussion ensued. The most memorable exchange was with a mathematician who politely acknowledged the importance of the issues I raised but then assured me that “as AI advances, it will eventually show us how to address these problems.” Struck by his earnest faith in technology as a force for good, I wanted to sputter, “But what about those already being harmed by the deployment of experimental AI in healthcare, education, criminal justice, and more—are they expected to wait for a mythical future where sentient systems act as sage stewards of humanity?”

Fast-forward almost 10 years, and we are living in the imagination of AI evangelists racing to build artificial general intelligence (AGI), even as they warn of its potential to destroy us. This gospel of love and fear insists on “aligning” AI with human values to rein in these digital deities. OpenAI, the company behind ChatGPT, echoed the sentiment of my IAS colleague: “We are improving our AI systems’ ability to learn from human feedback and to assist humans at evaluating AI. Our goal is to build a sufficiently aligned AI system that can help us solve all other alignment problems.” They envision a time when, eventually, “our AI systems can take over more and more of our alignment work and ultimately conceive, implement, study, and develop better alignment techniques than we have now. They will work together with humans to ensure that their own successors are more aligned with humans.” For many, this is not reassuring…(More)”.

Long-term validation of inner-urban mobility metrics derived from Twitter/X

Curated on Posted on by Stefaan Verhulst

Paper by Steffen Knoblauch et al: “Urban mobility analysis using Twitter as a proxy has gained significant attention in various application fields; however, long-term validation studies are scarce. This paper addresses this gap by assessing the reliability of Twitter data for modeling inner-urban mobility dynamics over a 27-month period in the. metropolitan area of Rio de Janeiro, Brazil. The evaluation involves the validation of Twitter-derived mobility estimates at both temporal and spatial scales, employing over 1.6 × 1011 mobile phone records of around three million users during the non-stationary mobility period from April 2020 to. June 2022, which coincided with the COVID-19 pandemic. The results highlight the need for caution when using Twitter for short-term modeling of urban mobility flows. Short-term inference can be influenced by Twitter policy changes and the availability of publicly accessible tweets. On the other hand, this long-term study demonstrates that employing multiple mobility metrics simultaneously, analyzing dynamic and static mobility changes concurrently, and employing robust preprocessing techniques such as rolling window downsampling can enhance the inference capabilities of Twitter data. These novel insights gained from a long-term perspective are vital, as Twitter – rebranded to X in 2023 – is extensively used by researchers worldwide to infer human movement patterns. Since conclusions drawn from studies using Twitter could be used to inform public policy, emergency response, and urban planning, evaluating the reliability of this data is of utmost importance…(More)”.

Veridical Data Science

Curated on Posted on by Stefaan Verhulst

Book by Bin Yu and Rebecca L. Barter: “Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs.
Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science…(More)”.

Contractual Freedom and Fairness in EU Data Sharing Agreements

Curated on Posted on by Stefaan Verhulst

Paper by Thomas Margoni and Alain M. Strowel: “This chapter analyzes the evolving landscape of EU data-sharing agreements, particularly focusing on the balance between contractual freedom and fairness in the context of non-personal data. The discussion highlights the complexities introduced by recent EU legislation, such as the Data Act, Data Governance Act, and Open Data Directive, which collectively aim to regulate data markets and enhance data sharing. The chapter emphasizes how these laws impose obligations that limit contractual freedom to ensure fairness, particularly in business-to-business (B2B) and Internet of Things (IoT) data transactions. It also explores the tension between private ordering and public governance, suggesting that the EU’s approach marks a shift from property-based models to governance-based models in data regulation. This chapter underscores the significant impact these regulations will have on data contracts and the broader EU data economy…(More)”.

Cross-border data flows in Africa: Continental ambitions and political realities

Curated on Posted on by Stefaan Verhulst

Paper by Melody Musoni, Poorva Karkare and Chloe Teevan: “Africa must prioritise data usage and cross-border data sharing to realise the goals of the African Continental Free Trade Area and to drive innovation and AI development. Accessible and shareable data is essential for the growth and success of the digital economy, enabling innovations and economic opportunities, especially in a rapidly evolving landscape.

African countries, through the African Union (AU), have a common vision of sharing data across borders to boost economic growth. However, the adopted continental digital policies are often inconsistently applied at the national level, where some member states implement restrictive measures like data localisation that limit the free flow of data.

The paper looks at national policies that often prioritise domestic interests and how those conflict with continental goals. This is due to differences in political ideologies, socio-economic conditions, security concerns and economic priorities. This misalignment between national agendas and the broader AU strategy is shaped by each country’s unique context, as seen in the examples of Senegal, Nigeria and Mozambique, which face distinct challenges in implementing the continental vision.

The paper concludes with actionable recommendations for the AU, member states and the partnership with the European Union. It suggests that the AU enhances support for data-sharing initiatives and urges member states to focus on policy alignment, address data deficiencies, build data infrastructure and find new ways to use data. It also highlights how the EU can strengthen its support for Africa’s datasharing goals…(More)”.

Lifecycles, pipelines, and value chains: toward a focus on events in responsible artificial intelligence for health

Curated on Posted on by Stefaan Verhulst

Paper by Joseph Donia et al: “Process-oriented approaches to the responsible development, implementation, and oversight of artificial intelligence (AI) systems have proliferated in recent years. Variously referred to as lifecycles, pipelines, or value chains, these approaches demonstrate a common focus on systematically mapping key activities and normative considerations throughout the development and use of AI systems. At the same time, these approaches risk focusing on proximal activities of development and use at the expense of a focus on the events and value conflicts that shape how key decisions are made in practice. In this article we report on the results of an ‘embedded’ ethics research study focused on SPOTT– a ‘Smart Physiotherapy Tracking Technology’ employing AI and undergoing development and commercialization at an academic health sciences centre. Through interviews and focus groups with the development and commercialization team, patients, and policy and ethics experts, we suggest that a more expansive design and development lifecycle shaped by key events offers a more robust approach to normative analysis of digital health technologies, especially where those technologies’ actual uses are underspecified or in flux. We introduce five of these key events, outlining their implications for responsible design and governance of AI for health, and present a set of critical questions intended for others doing applied ethics and policy work. We briefly conclude with a reflection on the value of this approach for engaging with health AI ecosystems more broadly…(More)”.

A shared destiny for public sector data

Curated on Posted on by Stefaan Verhulst

Blog post by Shona Nicol: “As a data professional, it can sometime feel hard to get others interested in data. Perhaps like many in this profession, I can often express the importance and value of data for good in an overly technical way. However when our biggest challenges in Scotland include eradicating child poverty, growing the economy and tackling the climate emergency, I would argue that we should all take an interest in data because it’s going to be foundational in helping us solve these problems.

Data is already intrinsic to shaping our society and how services are delivered. And public sector data is a vital component in making sure that services for the people of Scotland are being delivered efficiently and effectively. Despite an ever growing awareness of the transformative power of data to improve the design and delivery of services, feedback from public sector staff shows that they can face difficulties when trying to influence colleagues and senior leaders around the need to invest in data.

A vision gap

In the Scottish Government’s data maturity programme and more widely, we regularly hear about the challenges data professionals encounter when trying to enact change. This community tell us that a long-term vision for public sector data for Scotland could help them by providing the context for what they are trying to achieve locally.

Earlier this year we started to scope how we might do this. We recognised that organisations are already working to deliver local and national strategies and policies that relate to data, so any vision had to be able to sit alongside those, be meaningful in different settings, agnostic of technology and relevant to any public sector organisation. We wanted to offer opportunities for alignment, not enforce an instruction manual…(More)”.

Understanding local government responsible AI strategy: An international municipal policy document analysis

Curated on Posted on by Stefaan Verhulst

Paper by Anne David et al: “The burgeoning capabilities of artificial intelligence (AI) have prompted numerous local governments worldwide to consider its integration into their operations. Nevertheless, instances of notable AI failures have heightened ethical concerns, emphasising the imperative for local governments to approach the adoption of AI technologies in a responsible manner. While local government AI guidelines endeavour to incorporate characteristics of responsible innovation and technology (RIT), it remains essential to assess the extent to which these characteristics have been integrated into policy guidelines to facilitate more effective AI governance in the future. This study closely examines local government policy documents (n = 26) through the lens of RIT, employing directed content analysis with thematic data analysis software. The results reveal that: (a) Not all RIT characteristics have been given equal consideration in these policy documents; (b) Participatory and deliberate considerations were the most frequently mentioned responsible AI characteristics in policy documents; (c) Adaptable, explainable, sustainable, and accountable considerations were the least present responsible AI characteristics in policy documents; (d) Many of the considerations overlapped with each other as local governments were at the early stages of identifying them. Furthermore, the paper summarised strategies aimed at assisting local authorities in identifying their strengths and weaknesses in responsible AI characteristics, thereby facilitating their transformation into governing entities with responsible AI practices. The study informs local government policymakers, practitioners, and researchers on the critical aspects of responsible AI policymaking…(More)” See also: AI Localism

AI helped Uncle Sam catch $1 billion of fraud in one year. And it’s just getting started

Curated on Posted on by Stefaan Verhulst

Article by Matt Egan: “The federal government’s bet on using artificial intelligence to fight financial crime appears to be paying off.

Machine learning AI helped the US Treasury Department to sift through massive amounts of data and recover $1 billion worth of check fraud in fiscal 2024 alone, according to new estimates shared first with CNN. That’s nearly triple what the Treasury recovered in the prior fiscal year.

“It’s really been transformative,” Renata Miskell, a top Treasury official, told CNN in a phone interview.

“Leveraging data has upped our game in fraud detection and prevention,” Miskell said.

The Treasury Department credited AI with helping officials prevent and recover more than $4 billion worth of fraud overall in fiscal 2024, a six-fold spike from the year before.

US officials quietly started using AI to detect financial crime in late 2022, taking a page out of what many banks and credit card companies already do to stop bad guys.

The goal is to protect taxpayer money against fraud, which spiked during the Covid-19 pandemic as the federal government scrambled to disburse emergency aid to consumers and businesses.

To be sure, Treasury is not using generative AI, the kind that has captivated users of OpenAI’s ChatGPT and Google’s Gemini by generating images, crafting song lyrics and answering complex questions (even though it still sometimes struggles with simple queries)…(More)”.