Accelerating Medicines Partnership (AMP): Improving Drug Research Efficiency through Biomarker Data Sharing

Curated on Posted on by Stefaan Verhulst

Data Collaborative Case Study by Michelle Winowatan, Andrew Young, and Stefaan Verhulst: “Accelerating Medicines Partnership (AMP) is a cross-sector data-sharing partnership in the United States between the National Institutes of Health (NIH), the Food and Drug Administration (FDA), multiple biopharmaceutical and life science companies, as well as non-profit organizations that seeks to improve the efficiency of developing new diagnostics and treatments for several types of disease. To achieve this goal, the partnership created a pre-competitive collaborative ecosystem where the biomedical community can pool data and resources that are relevant to the prioritized disease areas. A key component of the partnership is to make biomarkers data available to the medical research community through online portals.

Data Collaboratives Model: Based on our typology of data collaborative models, AMP is an example of the data pooling model of data collaboration, specifically a public data pool. Public data pools co-mingle data assets from multiple data holders — in this case pharmaceutical companies — and make those shared assets available on the web. Pools often limit contributions to approved partners (as public data pools are not crowdsourcing efforts), but access to the shared assets is open, enabling independent re-uses.

Data Stewardship Approach: Data stewardship is built into the partnership through the establishment of an executive committee, which governs the entire partnership, and a steering committee for each disease area, which governs each of the sub-projects within AMP. These committees consist of representatives from the institutional partners involved in AMP and perform data stewards function including enabling inter-institutional engagement as well as intra-institutional coordination, data audit and assessment of value and risk, communication of findings, and nurture the collaboration to sustainability….(Full Case Study)”.

Federal Sources of Entrepreneurship Data: A Compendium

Curated on Posted on by Stefaan Verhulst

Compendium developed by Andrew Reamer: “The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy (GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development. The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.


Achieving these ends first requires the identification and detailed description of available federal datasets, as provided in this compendium. Its contents include:

  • An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
  • Tables providing information on each dataset, including:
    • scope of coverage of self-employed, entrepreneurs, and businesses;
    • data collection methods (nature of data source, periodicity, sampling frame, sample size);
    • dataset variables (owner characteristics, business characteristics and operations, geographic areas);
    • Data release schedule; and
    • Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).

For each dataset, examples of studies, if any, that use the data source to describe and explain trends in entrepreneurship.
The author’s aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More)”

Industry and Public Sector Leaders Partner to Launch the Mobility Data Collaborative

Curated on Posted on by Stefaan Verhulst

Press Release: “The Mobility Data Collaborative (the Collaborative), a multi-sector forum with the goal of creating a framework to improve mobility through data, launches today…

New mobility services, such as shared cars, bikes, and scooters, are emerging and integrating into the urban transportation landscape across the globe. Data generated by these new mobility services offers an exciting opportunity to inform local policies and infrastructure planning. The Collaborative brings together key members from the public and private sectors to develop best practices to harness the potential of this valuable data to support safe, equitable, and livable streets.

The Collaborative will leverage the knowledge of its current and future members to solve the complex challenges facing shared mobility operators and the public agencies who manage access to infrastructure that these new services require. A critical component of this collaboration is providing an open and impartial forum for sharing information and developing best practices. 

Membership is open to public agencies, nonprofits, academic institutions and private companies….(More)”.

New Orleans has declared a state of emergency after a cyberattack

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “The city told its employees to shut down their computers as a precaution this weekend after an attempted cyberattack on Friday.

The news: New Orleans spotted suspicious activity in its networks at around 5 a.m. on Friday, with a spike in the attempted attacks at 8 a.m. It detected phishing attempts and ransomware, Kim LaGrue, the city’s head of IT, later told reporters. Once they were confident the city was under attack, the team shut down its servers and computers. City authorities then filed a declaration of a state of emergency with the Civil District Court, and pulled local, state, and federal authorities into a (still pending) investigation of the incident. The city is still working to recover data from the attack but will be open as usual from this morning, Mayor LaToya Cantrell said on Twitter.

Was it ransomware? The nature of the attack is still something of a mystery. Cantrell confirmed that ransomware had been detected, but the city hasn’t received any demands for ransom money.

The positives: New Orleans was at least fairly well prepared for this attack, thanks to training for this scenario and its ability to operate many of its services without internet access, officials told reporters.

A familiar story: New Orleans is just the latest government to face ransomware attacks, after nearly two dozen cities in Texas were targeted in August, plus Louisiana in November (causing the governor to declare a state of emergency). The phenomenon goes beyond the US, too: in October Johannesburg became the biggest city yet to face a ransomware attack.…(More)”.

What is the Difference between a Conclusion and a Fact?

Curated on Posted on by Stefaan Verhulst

Paper by Howard M. Erichson: “In Ashcroft v. Iqbal, building on Bell Atlantic v. Twombly, the Supreme Court instructed district courts to treat a complaint’s conclusions differently from allegations of fact. Facts, but not conclusions, are assumed true for purposes of a motion to dismiss. The Court did little to help judges or lawyers understand the elusive distinction, and, indeed, obscured the distinction with its language. The Court said it was distinguishing “legal conclusions” from factual allegations. The application in Twombly and Iqbal, however, shows that the relevant distinction is not between law and fact, but rather between different types of factual assertions. This essay, written for a symposium on the tenth anniversary of Ashcroft v. Iqbal, explores the definitional problem with the conclusion-fact distinction and examines how district courts have applied the distinction in recent cases….(More)”.

Assessing employer intent when AI hiring tools are biased

Curated on Posted on by Stefaan Verhulst

Report by Caitlin Chin at Brookings: “When it comes to gender stereotypes in occupational roles, artificial intelligence (AI) has the potential to either mitigate historical bias or heighten it. In the case of the Word2vec model, AI appears to do both.

Word2vec is a publicly available algorithmic model built on millions of words scraped from online Google News articles, which computer scientists commonly use to analyze word associations. In 2016, Microsoft and Boston University researchers revealed that the model picked up gender stereotypes existing in online news sources—and furthermore, that these biased word associations were overwhelmingly job related. Upon discovering this problem, the researchers neutralized the biased word correlations in their specific algorithm, writing that “in a small way debiased word embeddings can hopefully contribute to reducing gender bias in society.”

Their study draws attention to a broader issue with artificial intelligence: Because algorithms often emulate the training datasets that they are built upon, biased input datasets could generate flawed outputs. Because many contemporary employers utilize predictive algorithms to scan resumes, direct targeted advertising, or even conduct face- or voice-recognition-based interviews, it is crucial to consider whether popular hiring tools might be susceptible to the same cultural biases that the researchers discovered in Word2vec.

In this paper, I discuss how hiring is a multi-layered and opaque process and how it will become more difficult to assess employer intent as recruitment processes move online. Because intent is a critical aspect of employment discrimination law, I ultimately suggest four ways upon which to include it in the discussion surrounding algorithmic bias….(More)”

This report from The Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “AI and Bias,” a series that explores ways to mitigate possible biases and create a pathway toward greater fairness in AI and emerging technologies.

Engaging citizens in determining the appropriate conditions and purposes for re-using Health Data

Curated on Posted on by Stefaan Verhulst

Beth Noveck at The GovLab: “…The term, big health data, refers to the ability to gather and analyze vast quantities of online information about health, wellness and lifestyle. It includes not only our medical records but data from apps that track what we buy, how often we exercise and how well we sleep, among many other things. It provides an ocean of information about how healthy or ill we are, and unsurprisingly, doctors, medical researchers, healthcare organizations, insurance companies and governments are keen to get access to it. Should they be allowed to?

It’s a huge question, and AARP is partnering with GovLab to learn what older Americans think about it. AARP is a non-profit organization — the largest in the nation and the world — dedicated to empowering Americans to choose how they live as they age. In 2018 it had more than 38 million members. It is a key voice in policymaking in the United States, because it represents the views of people aged over 50 in this country.

From today, AARP and the GovLab are using the Internet to capture what AARP members feel are the most urgent issues confronting them to try to discover what worries people most: the use of big health data or the failure to use it.

The answers are not simple. On the one hand, increasing the use and sharing of data could enable doctors to make better diagnoses and interventions to prevent disease and make us healthier. It could lead medical researchers to find cures faster, while the creation of health data businesses could strengthen the economy.

On the other hand, the collection, sharing, and use of big health data could reveal sensitive personal information over which we have little control. This data could be sold without our consent, and be used by entities for surveillance or discrimination, rather than to promote well-being….(More)”.

A World With a Billion Cameras Watching You Is Just Around the Corner

Curated on Posted on by Stefaan Verhulst

Liza Lin and Newley Purnell at the Wall Street Journal: “As governments and companies invest more in security networks, hundreds of millions more surveillance cameras will be watching the world in 2021, mostly in China, according to a new report.

The report, from industry researcher IHS Markit, to be released Thursday, said the number of cameras used for surveillance would climb above 1 billion by the end of 2021. That would represent an almost 30% increase from the 770 million cameras today. China would continue to account for a little over half the total.

Fast-growing, populous nations such as India, Brazil and Indonesia would also help drive growth in the sector, the report said. The number of surveillance cameras in the U.S. would grow to 85 million by 2021, from 70 million last year, as American schools, malls and offices seek to tighten security on their premises, IHS analyst Oliver Philippou said.

Mr. Philippou said government programs to implement widespread video surveillance to monitor the public would be the biggest catalyst for the growth in China. City surveillance also was driving demand elsewhere.

“It’s a public-safety issue,” Mr. Philippou said in an interview. “There is a big focus on crime and terrorism in recent years.”

The global security-camera industry has been energized by breakthroughs in image quality and artificial intelligence. These allow better and faster facial recognition and video analytics, which governments are using to do everything from managing traffic to predicting crimes.

China leads the world in the rollout of this kind of technology. It is home to the world’s largest camera makers, with its cameras on street corners, along busy roads and in residential neighborhoods….(More)”.

Public Entrepreneurship and Policy Engineering

Curated on Posted on by Stefaan Verhulst

Essay by Beth Noveck at Communications of the ACM: “Science and technology have progressed exponentially, making it possible for humans to live longer, healthier, more creative lives. The explosion of Internet and mobile phone technologies have increased trade, literacy, and mobility. At the same time, life expectancy for the poor has not increased and is declining.

As science fiction writer William Gibson famously quipped, the future is here, but unevenly distributed. With urgent problems from inequality to climate change, we must train more passionate and innovative people—what I call public entrepreneurs—to learn how to leverage new technology to tackle public problems. Public problems are those compelling and important challenges where neither the problem is well understood nor the solution agreed upon, yet we must devise and implement approaches, often from different disciplines, in an effort to improve people’s lives….(More)”.

Regulating Artificial Intelligence

Curated on Posted on by Stefaan Verhulst

Book by Thomas Wischmeyer and Timo Rademacher: “This book assesses the normative and practical challenges for artificial intelligence (AI) regulation, offers comprehensive information on the laws that currently shape or restrict the design or use of AI, and develops policy recommendations for those areas in which regulation is most urgently needed. By gathering contributions from scholars who are experts in their respective fields of legal research, it demonstrates that AI regulation is not a specialized sub-discipline, but affects the entire legal system and thus concerns all lawyers. 

Machine learning-based technology, which lies at the heart of what is commonly referred to as AI, is increasingly being employed to make policy and business decisions with broad social impacts, and therefore runs the risk of causing wide-scale damage. At the same time, AI technology is becoming more and more complex and difficult to understand, making it harder to determine whether or not it is being used in accordance with the law. In light of this situation, even tech enthusiasts are calling for stricter regulation of AI. Legislators, too, are stepping in and have begun to pass AI laws, including the prohibition of automated decision-making systems in Article 22 of the General Data Protection Regulation, the New York City AI transparency bill, and the 2017 amendments to the German Cartel Act and German Administrative Procedure Act. While the belief that something needs to be done is widely shared, there is far less clarity about what exactly can or should be done, or what effective regulation might look like. 

The book is divided into two major parts, the first of which focuses on features common to most AI systems, and explores how they relate to the legal framework for data-driven technologies, which already exists in the form of (national and supra-national) constitutional law, EU data protection and competition law, and anti-discrimination law. In the second part, the book examines in detail a number of relevant sectors in which AI is increasingly shaping decision-making processes, ranging from the notorious social media and the legal, financial and healthcare industries, to fields like law enforcement and tax law, in which we can observe how regulation by AI is becoming a reality….(More)”.