Paper by Deborah N. Archer: “Legal education purports to prepare the next generation of lawyers capable of tackling the urgent and complex social justice challenges of our time. But law schools are failing in that public promise. Clinical education offers the best opportunity to overcome those failings by teaching the skills lawyers need to tackle systemic and interlocking legal and social problems. But too often even clinical education falls short: it adheres to conventional pedagogical methodologies that are overly narrow and, in the end, limit students’ abilities to manage today’s complex racial and social justice issues. This article contends that clinical education needs to embrace and reimagine political lawyering for the 21st century in order to prepare aspiring lawyers to tackle both new and chronic issues of injustice through a broad array of advocacy strategies….(More)”.
DNA databases are too white. This man aims to fix that.
Interview of Carlos D. Bustamante by David Rotman: “In the 15 years since the Human Genome Project first exposed our DNA blueprint, vast amounts of genetic data have been collected from millions of people in many different parts of the world. Carlos D. Bustamante’s job is to search that genetic data for clues to everything from ancient history and human migration patterns to the reasons people with different ancestries are so varied in their response to common diseases.
Bustamante’s career has roughly spanned the period since the Human Genome Project was completed. A professor of genetics and biomedical data science at Stanford and 2010 winner of a MacArthur genius award, he has helped to tease out the complex genetic variation across different populations. These variants mean that the causes of diseases can vary greatly between groups. Part of the motivation for Bustamante, who was born in Venezuela and moved to the US when he was seven, is to use those insights to lessen the medical disparities that still plague us.
But while it’s an area ripe with potential for improving medicine, it’s also fraught with controversies over how to interpret genetic differences between human populations. In an era still obsessed with race and ethnicity—and marred by the frequent misuse of science in defining the characteristics of different groups—Bustamante remains undaunted in searching for the nuanced genetic differences that these groups display.
Perhaps his optimism is due to his personality—few sentences go by without a “fantastic” or “extraordinarily exciting.” But it is also his recognition as a population geneticist of the incredible opportunity that understanding differences in human genomes presents for improving health and fighting disease.
David Rotman, MIT Technology Review’s editor at large, discussed with Bustamante why it’s so important to include more people in genetic studies and understand the genetics of different populations.
How good are we at making sure that the genomic data we’re collecting is inclusive?
I’m optimistic, but it’s not there yet.
In our 2011 paper, the statistic we had was that more than 96% of participants in genome-wide association studies were of European descent. In the follow-up in 2016, the number went from 96% to around 80%. So that’s getting better. Unfortunately, or perhaps fortunately, a lot of that is due to the entry of China into genetics. A lot of that was due to large-scale studies in Chinese and East Asian populations. Hispanics, for example, make up less than 1% of genome-wide association studies. So we need to do better. Ultimately, we want precision medicine to benefit everybody.
Aside from a fairness issue, why is diversity in genomic data important? What do we miss without it?
First of all, it has nothing to do with political correctness. It has everything to do with human biology and the fact that human populations and the great diaspora of human migrations have left their mark on the human genome. The genetic underpinnings of health and disease have shared components across human populations and things that are unique to different populations….(More)”.
Crowdsourcing the vote: New horizons in citizen forecasting
Article by Mickael Temporão, Yannick Dufresne, Justin Savoie and Clifton van der Linden in International Journal of Forecasting: “People do not know much about politics. This is one of the most robust findings in political science and is backed by decades of research. Most of this research has focused on people’s ability to know about political issues and party positions on these issues. But can people predict elections? Our research uses a very large dataset (n>2,000,000) collected during ten provincial and federal elections in Canada to test whether people can predict the electoral victor and the closeness of the race in their district throughout the campaign. The results show that they can. This paper also contributes to the emerging literature on citizen forecasting by developing a scaling method that allows us to compare the closeness of races and that can be applied to multiparty contexts with varying numbers of parties. Finally, we assess the accuracy of citizen forecasting in Canada when compared to voter expectations weighted by past votes and political competency….(More)”.
A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI
Paper by Sandra Wachter and Brent Mittelstadt: “Big Data analytics and artificial intelligence (AI) draw non-intuitive and unverifiable inferences and predictions about the behaviors, preferences, and private lives of individuals. These inferences draw on highly diverse and feature-rich data of unpredictable value, and create new opportunities for discriminatory, biased, and invasive decision-making. Concerns about algorithmic accountability are often actually concerns about the way in which these technologies draw privacy invasive and non-verifiable inferences about us that we cannot predict, understand, or refute.
Data protection law is meant to protect people’s privacy, identity, reputation, and autonomy, but is currently failing to protect data subjects from the novel risks of inferential analytics. The broad concept of personal datain Europe could be interpreted to include inferences, predictions, and assumptions that refer to or impact on an individual. If seen as personal data, individuals are granted numerous rights under data protection law. However, the legal status of inferences is heavily disputed in legal scholarship, and marked by inconsistencies and contradictions within and between the views of the Article 29 Working Party and the European Court of Justice.
As we show in this paper, individuals are granted little control and oversight over how their personal data is used to draw inferences about them. Compared to other types of personal data, inferences are effectively ‘economy class’ personal data in the General Data Protection Regulation (GDPR). Data subjects’ rights to know about (Art 13-15), rectify (Art 16), delete (Art 17), object to (Art 21), or port (Art 20) personal data are significantly curtailed when it comes to inferences, often requiring a greater balance with controller’s interests (e.g. trade secrets, intellectual property) than would otherwise be the case. Similarly, the GDPR provides insufficient protection against sensitive inferences (Art 9) or remedies to challenge inferences or important decisions based on them (Art 22(3))….
In this paper we argue that a new data protection right, the ‘right to reasonable inferences’, is needed to help close the accountability gap currently posed ‘high risk inferences’ , meaning inferences that are privacy invasive or reputation damaging and have low verifiability in the sense of being predictive or opinion-based. In cases where algorithms draw ‘high risk inferences’ about individuals, this right would require ex-ante justification to be given by the data controller to establish whether an inference is reasonable. This disclosure would address (1) why certain data is a relevant basis to draw inferences; (2) why these inferences are relevant for the chosen processing purpose or type of automated decision; and (3) whether the data and methods used to draw the inferences are accurate and statistically reliable. The ex-ante justification is bolstered by an additional ex-post mechanism enabling unreasonable inferences to be challenged. A right to reasonable inferences must, however, be reconciled with EU jurisprudence and counterbalanced with IP and trade secrets law as well as freedom of expression and Article 16 of the EU Charter of Fundamental Rights: the freedom to conduct a business….(More)”.
A Doctor’s Prescription: Data May Finally Be Good for Your Health
Interview by Art Kleiner: “In 2015, Robert Wachter published The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age, a skeptical account of digitization in hospitals. Despite the promise offered by the digital transformation of healthcare, electronic health records had not delivered better care and greater efficiency. The cumbersome design, legacy procedures, and resistance from staff were frustrating everyone — administrators, nurses, consultants, and patients. Costs continued to rise, and preventable medical mistakes were not spotted. One patient at Wachter’s own hospital, one of the nation’s finest, was given 39 times the correct dose of antibiotics by an automated system that nobody questioned. The teenager survived, but it was clear that there needed to be a new approach to the management and use of data.
Wachter has for decades considered the delivery of healthcare through a lens focused on patient safety and quality. In 1996, he coauthored a paper in the New England Journal of Medicine that coined the term hospitalist in describing and promoting a new way of managing patients in hospitals: having one doctor — the hospitalist — “own” the patient journey from admission to discharge. The primary goal was to improve outcomes and save lives. Wachter argued it would also reduce costs and increase efficiency, making the business case for better healthcare. And he was right. Today there are more than 50,000 hospitalists, and it took just two years from the article’s publication to have the first data proving his point. In 2016, Wachter was named chair of the Department of Medicine at the University of California, San Francisco (UCSF), where he has worked since 1990.
Today, Wachter is, to paraphrase the title of a recent talk, less grumpy than he used to be about health tech. The hope part of his book’s title has materialized in some areas faster than he predicted. AI’s advances in imaging are already helping the detection of cancers become more accurate. As data collection has become better systematized, big technology firms such as Google, Amazon, and Apple are entering (in Google’s case, reentering) the field and having more success focusing their problem-solving skills on healthcare issues. In his San Francisco office, Wachter sat down with strategy+businessto discuss why the healthcare system may finally be about to change….
Systems for Fresh Thinking
S+B: The changes you appreciate seem to have less to do with technological design and more to do with people getting used to the new systems, building their own variations, and making them work.
WACHTER: The original electronic health record was just a platform play to get the data in digital form. It didn’t do anything particularly helpful in terms of helping the physicians make better decisions or helping to connect one kind of doctor with another kind of doctor. But it was a start.
I remember that when we were starting to develop our electronic health record at UCSF, 12 or 13 years ago, I hired a physician who is now in charge of our health computer system. I said to him, “We don’t have our electronic health record in yet, but I’m pretty sure we will in seven or eight years. What will your job be when that’s done?” I actually thought once the system was fully implemented, we’d be done with the need to innovate and evolve in health IT. That, of course, was asinine.
S+B: That’s like saying to an auto mechanic, “What will your job be when we have automatic transmissions?”
WACHTER: Right, but even more so, because many of us saw electronic health records as the be-all and end-all of digitally facilitated medicine. But putting in the electronic health record is just step one of 10. Then you need to start connecting all the pieces, and then you add analytics that make sense of the data and make predictions. Then you build tools and apps to fit into the workflow and change the way you work.
One of my biggest epiphanies was this: When you digitize, in any industry, nobody is clever enough to actually change anything. All they know how to do is digitize the old practice. You only start seeing real progress when smart people come in, begin using the new system, and say, “Why the hell do we do it that way?” And then you start thinking freshly about the work. That’s when you have a chance to reimagine the work in a digital environment…(More)”.
Human Rights in the Big Data World
Paper by Francis Kuriakose and Deepa Iyer: “Ethical approach to human rights conceives and evaluates law through the underlying value concerns. This paper examines human rights after the introduction of big data using an ethical approach to rights. First, the central value concerns such as equity, equality, sustainability and security are derived from the history of digital technological revolution. Then, the properties and characteristics of big data are analyzed to understand emerging value concerns such as accountability, transparency, tracability, explainability and disprovability.
Using these value points, this paper argues that big data calls for two types of evaluations regarding human rights. The first is the reassessment of existing human rights in the digital sphere predominantly through right to equality and right to work. The second is the conceptualization of new digital rights such as right to privacy and right against propensity-based discrimination. The paper concludes that as we increasingly share the world with intelligence systems, these new values expand and modify the existing human rights paradigm….(More)”.
Text Analysis Systems Mine Workplace Emails to Measure Staff Sentiments
Alan Rothman at LLRX: “…For all of these good, bad or indifferent workplaces, a key question is whether any of the actions of management to engage the staff and listen to their concerns ever resulted in improved working conditions and higher levels of job satisfaction?
The answer is most often “yes”. Just having a say in, and some sense of control over, our jobs and workflows can indeed have a demonstrable impact on morale, camaraderie and the bottom line. As posited in the Hawthorne Effect, also termed the “Observer Effect”, this was first discovered during studies in the 1920’s and 1930’s when the management of a factory made improvements to the lighting and work schedules. In turn, worker satisfaction and productivity temporarily increased. This was not so much because there was more light, but rather, that the workers sensed that management was paying attention to, and then acting upon, their concerns. The workers perceived they were no longer just cogs in a machine.
Perhaps, too, the Hawthorne Effect is in some ways the workplace equivalent of the Heisenberg’s Uncertainty Principle in physics. To vastly oversimplify this slippery concept, the mere act of observing a subatomic particle can change its position.¹
Giving the processes of observation, analysis and change at the enterprise level a modern (but non-quantum) spin, is a fascinating new article in the September 2018 issue of The Atlantic entitled What Your Boss Could Learn by Reading the Whole Company’s Emails, by Frank Partnoy. I highly recommend a click-through and full read if you have an opportunity. I will summarize and annotate it, and then, considering my own thorough lack of understanding of the basics of y=f(x), pose some of my own physics-free questions….
Today the text analytics business, like the work done by KeenCorp, is thriving. It has been long-established as the processing behind email spam filters. Now it is finding other applications including monitoring corporate reputations on social media and other sites.²
The finance industry is another growth sector, as investment banks and hedge funds scan a wide variety of information sources to locate “slight changes in language” that may point towards pending increases or decreases in share prices. Financial research providers are using artificial intelligence to mine “insights” from their own selections of news and analytical sources.
But is this technology effective?
In a paper entitled Lazy Prices, by Lauren Cohen (Harvard Business School and NBER), Christopher Malloy (Harvard Business School and NBER), and Quoc Nguyen (University of Illinois at Chicago), in a draft dated February 22, 2018, these researchers found that the share price of company, in this case NetApp in their 2010 annual report, measurably went down after the firm “subtly changes” its reporting “descriptions of certain risks”. Algorithms can detect such changes more quickly and effectively than humans. The company subsequently clarified in its 2011 annual report their “failure to comply” with reporting requirements in 2010. A highly skilled stock analyst “might have missed that phrase”, but once again its was captured by “researcher’s algorithms”.
In the hands of a “skeptical investor”, this information might well have resulted in them questioning the differences in the 2010 and 2011 annual reports and, in turn, saved him or her a great deal of money. This detection was an early signal of a looming decline in NetApp’s stock. Half a year after the 2011 report’s publication, it was reported that the Syrian government has bought the company and “used that equipment to spy on its citizen”, causing further declines.
Now text analytics is being deployed at a new target: The composition of employees’ communications. Although it has been found that workers have no expectations of privacy in their workplaces, some companies remain reluctant to do so because of privacy concerns. Thus, companies are finding it more challenging to resist the “urge to mine employee information”, especially as text analysis systems continue to improve.
Among the evolving enterprise applications are the human resources departments in assessing overall employee morale. For example, Vibe is such an app that scans through communications on Slack, a widely used enterprise platform. Vibe’s algorithm, in real-time reporting, measures the positive and negative emotions of a work team….(More)”.
Craft metrics to value co-production
Liz Richardson and Beth Perry at Nature: “Advocates of co-production encourage collaboration between professional researchers and those affected by that research, to ensure that the resulting science is relevant and useful. Opening up science beyond scientists is essential, particularly where problems are complex, solutions are uncertain and values are salient. For example, patients should have input into research on their conditions, and first-hand experience of local residents should shape research on environmental-health issues.
But what constitutes success on these terms? Without a better understanding of this, it is harder to incentivize co-production in research. A key way to support co-production is reconfiguring that much-derided feature of academic careers: metrics.
Current indicators of research output (such as paper counts or the h-index) conceptualize the value of research narrowly. They are already roundly criticized as poor measures of quality or usefulness. Less appreciated is the fact that these metrics also leave out the societal relevance of research and omit diverse approaches to creating knowledge about social problems.
Peer review also has trouble assessing the value of research that sits at disciplinary boundaries or that addresses complex social challenges. It denies broader social accountability by giving scientists a monopoly on determining what is legitimate knowledge1. Relying on academic peer review as a means of valuing research can discourage broader engagement.
This privileges abstract and theoretical research over work that is localized and applied. For example, research on climate-change adaptation, conducted in the global south by researchers embedded in affected communities, can make real differences to people’s lives. Yet it is likely to be valued less highly by conventional evaluation than research that is generalized from afar and then published in a high-impact English-language journal….(More)”.
Whither large International Non-Governmental Organisations?
Working Paper by Penny Lawrence: “Large international non-government organisations (INGOs) seem to be in an existential crisis in their role in the fight for social justice. Many, such as Save the Children or Oxfam, have become big well-known brands with compliance expectations similar to big businesses. Yet the public still imagine them to be run by volunteers. Their context is changing so fast, and so unpredictably, that they are struggling to keep up. It is a time of extraordinary disruptive change including the digital transformation, changing societal norms and engagement expectations and political upheaval and challenge. Fifteen years ago the political centre-ground in the UK seemed firm, with expanding space for civil society organisations to operate. Space for civil society voice now seems more threatened and challenged (Kenny 2015).
There has been a decline in trust in large charities in particular, partly as a result of their own complacency, acting as if the argument for aid has been won. Partly as a result of questioned practices e.g. the fundraising scandal of 2016/17 (where repeated mail drops to individuals requesting funds caused public backlash) and the safeguarding scandal of 2018 (where historic cases of sexual abuse by INGO staff, including Oxfam, were revisited by media in the wake of the #me too movement). This is also partly as a result of political challenge on INGOs’ advocacy and influencing role, their bias and their voice:
‘Some government ministers regard the charity sector with suspicion because it largely employs senior people with a left-wing perspective on life and because of other unfair criticisms of government it means there is regularly a tension between big charities and the conservative party’ Richard Wilson (Former Minister for Civil Society) 2018
On the other hand many feel that charities who have taken significant contracts to deliver services for the state have forfeited their independent voice and lost their way:
‘The voluntary sector risks declining over the next ten years into a mere instrument of a shrunken state, voiceless and toothless, unless it seizes the agenda and creates its own vision.’ Professor Nicholas Deakin 2014
It’s a tough context to be leading an INGO through, but INGOs have appeared ill prepared and slow to respond to the threats and opportunities, not realising how much they may need to change to respond to the fast evolving context and expectations. Large INGOs spend most of their energy exploiting present grant and contract business models, rather than exploring the opportunities to overcome poverty offered by such disruptive change. Their size and structures do not enable agility. They are too internally focused and self-referencing at a time when the world around them is changing so fast, and when political sands have shifted. Focussing on the internationalisation of structures and decision-making means large INGOs are ‘defeated by our own complexity’, as one INGO interviewee put it.
The purpose of this paper is to stimulate thinking amongst large INGOs at a time of such extraordinary disruptive change. The paper explores options for large INGOs, in terms of function and structure. After outlining large INGOs’ history, changing context, value and current thinking, it explores learning from others outside the development sector before suggesting the emerging options. It reflects on what’s encouraging and what’s stopping change and offers possible choices and pathways forwards….(More)”.
Translating science into business innovation: The case of open food and nutrition data hackathons
Paper by Christopher Tucci, Gianluigi Viscusi and Heidi Gautschi: “In this article, we explore the use of hackathons and open data in corporations’ open innovation portfolios, addressing a new way for companies to tap into the creativity and innovation of early-stage startup culture, in this case applied to the food and nutrition sector. We study the first Open Food Data Hackdays, held on 10-11 February 2017 in Lausanne and Zurich. The aim of the overall project that the Hackdays event was part of was to use open food and nutrition data as a driver for business innovation. We see hackathons as a new tool in the innovation manager’s toolkit, a kind of live crowdsourcing exercise that goes beyond traditional ideation and develops a variety of prototypes and new ideas for business innovation. Companies then have the option of working with entrepreneurs and taking some of the ideas forward….(More)”.