Digital self-determination/Digital Health and Well-Being
In this module, learners will approach and learn about the chances and challenges of digital self-determination in health from a multidisciplinary angle – technology, medicine, law and policy. From a technical perspective, learners will explore which problems in medicine can and cannot be solved with artificial intelligence (AI). Using the COVID tracing app as an example, the clinical utility of AI shall be explored from a medical perspective. Within a legal and policy analysis, learners will consider how individual and public interests can be balanced adequately in order to foster innovation while also protecting individual rights.
Learning Materials[edit | edit source]
- “What Artificial Intelligence Can and Can’t Do Right Now”, Harvard Business Review, by Andrew Ng
- “AI Recognizes COVID-19 in the Sound of a Cough”, IEEE Spectrum, by Megan Scudellari
- “What can machine learning do? Workforce implications”, Science, Vo. 358 (3670), by Erik Brynjolfsson and Tom Mitchell
- "Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid", The Lancet Digital Health Vo. 2, August 2020, by Urs Gasser et al
Video Sparks[edit | edit source]
For this session, videos were not recorded but we share with you the summation of the speakers' talks and would encourage folks to use these as starting points to find additional video or audio content that explores these issues. A good example are the writings, videos, and other materials by the Berkman Klein Center on Internet and Society's Digital Pandemic Response program.
|Speaker||Stefan Feuerriegel||Satchit Balsari||Kerstin N Vokinger|
|Video Summary||Stefan Feuerriegel focuses on the possible applications of AI in the context of health and well-being. Although various digital solutions have already been implemented, deployments requiring higher levels of complexity and customization still pose a significant challenge for machine learning. In novel circumstances where modest quantities of data are available, AI fails. It appears that navigating in the meanders of multi-dimensional discussions and deciphering varieties of inconclusive feedback is still a human-centered domain. (Summarized by Karolina Alama-Maruta)||Satchit Balsari illustrates the clinical utility of AI through the emergence of COVID-19 tracking apps in the initial phase of the pandemic. He draws out a comparison between the three contact tracing apps which are deployed in different geographies - Germany, Singapore and India - and examines the tension between epidemiological utility and the degree of privacy invasion by discussing the role of decentralization. He then sheds light into the design choices and the context in which the apps are implemented by taking into consideration the fact that real life conditions often debunk the assumption of ‘technology always works’. Pointing out the private companies’ eagerness to be involved in the development processes for Covid-19 tracking apps and potential misuses, he argues the public healthcare sector shouldn’t be a safe haven for both start-ups with a bunch of opportunities to exploit data and governments to satisfy their surveillance needs on communities. (Summarized by İdil Kula)||Kerstin N Vokinger addresses the legal challenges of AI and self-determination emphasizing that while AI-based medical apps have increased, the definition of health data remains murky. “What’s most important is the context,” e.g. a person's mobility record could also be seen as health data. Such unclear parameters of health data complicate the legal discussions about privacy regulation and data access, particularly during a crisis. One example would be the evolving tension between individual interest for privacy and public interest in contact tracing during the COVID-19 pandemic. (Summarized by Carmen Ng)|
Learning Artifacts[edit | edit source]
For this week’s artifacts, we asked participants to find a current example that illustrate some of the questions, challenges, or elements discussed in the session as relates to digital self-determination and the intersection of technology, medicine, policy and law in their country, region, state, or city and write a brief case study.
|Christian Thönnes||An analysis of what can be learned from the German debate around contact tracing apps|
|Ana Margarida Coelho||An audio recording explaining what the Portuguese app STAYAWAYCOVID is and how it played a role on the public health context.|
|Constanza M. Vidal Bustamante||Case study on CoronApp, the Chilean government's official COVID-19 app|
|Eraldo||In April 2018, BuzzFeed published an article denouncing the ways in which Grindr, a dating app for LGBT+ people, has shared its users' HIV information with third parties. This "five-minute case study" shows how the LGBT+ community has sought to protect its health data either by boycotting the app, filing complaints or lawsuits against the companies behind it, or calling for legal change.|
|Hillary McLauchlin||This artifact, a poster with graphics entitled "Direct-to-Consumer: The Future of Mental Healthcare?" explores the challenges and opportunities presented by consumer mobile applications claiming to improve or treat mental health concerns. This artifact touches upon limitations in the current oversight of such apps and their quality, raising questions about the degree to which such technologies both empower––and undermine––patients' digital self-determination.|
|Leonid Demidov||This short essay explores the question of data ownership from the digital agriculture point of veiw.|
|Rachid Benharrousse||Wiqaytna as an app which has attempted to reduce COVID-19 cases in Morocco, but has been a means of collecting data, similar to the app on which it was based.|
|Rory Torres||This brief case study on reach52's Online Health Check that uses Facebook Chatbot explores issues and asks questions on the app's privacy, quality, and how it can enhance digital self-determination in the health sector.|
|Samreen Mushtaq||Brings forth the intersection of medicine, tropes of care, and surveillance through the case of Aarogya Setu app in India|
|Tomás Guarna||A data ecosystem of a COVID test, repesenting the data flows and the tensions that underlie it.|
|Kyle Chan||A mind map on the HK government COVID tracking app: Leave Home Safe|
Activity[edit | edit source]
For this module's activity, learners looked for an interesting current example of some of the questions, challenges, or elements discussed in this module in relation to digital self-determination at the intersection of technology, medicine, policy and law in their country, region, state, or city.
Goal: Conduct some research on the intersection of technology, medicine, and policy in your country or region, and produce an artifact that addresses or raises some of the questions and concerns from this module in relation to digital self-determination. Ideally, these will be case studies that will highlight what is happening, what are some of the challenges and solutions possible in the situation, and what are some of the additional questions raised through the lens of digital self-determination. Ideally, the artifact would include a set of questions to push the audience to think more deeply about the example or to compare it/connect it to other examples (e.g. “What could be changed to enhance the peoples/communities’ abilities to take more control of their health data?”)
Format: This activity can be written, audio recording, video, or some other multimedia concept (animation, branching scenarios, collage, Prezi, etc). We encourage the use of visuals to conceptualize it but those visuals can be simple (hand-drawn on notebook paper that you capture with your phone) or more complex--and obviously, no visuals if you are doing something written or in audio solely.
We encourage you to be as creative as you want with this but we also encourage that if you are making things, particularly visuals or integrating video/audio content from elsewhere that you make sure you have the legal permission to do so. Along those lines, we encourage the use of Creative Common licensed materials or even materials from places like the Internet Archive. Additionally, we ask that you do not use logos of Digital Asia Hub or Berkman Klein Center in the creation of your artifacts.
If you are exploring this course on your own, we encourage you to create artifacts to share on Twitter or other social media platforms using the following hashtag: #DigitalSelfDetermination
Speaker Bios[edit | edit source]
Dr. Satchit Balsari is a Professor in emergency medicine at Harvard Medical School and Beth Israel Deaconess Medical Center. Since 2009, he has been affiliated with the FXB Center for Health and Human Rights at Harvard University, where his research has contributed to advocacy on behalf of vulnerable populations affected by disasters and humanitarian crises. His interdisciplinary interests in mobile technology, disaster response, and population health have been informed by his clinical practice in the United States and his field work around the world. His research has resulted in innovative applications of mobile, cloud-based technology to address public health challenges in mass gatherings, disasters, and humanitarian crises. Dr. Balsari received his medical degree from Grant Medical College in Mumbai, India and his public health degree from Harvard; he completed his emergency medicine residency at Columbia and Cornell’s New York-Presbyterian Hospital. In March 2017, he was awarded the Dr B.C. Roy National Award by the President of India, for “outstanding services in the field of sociomedical relief.” Dr. Balsari is an Aspen Ideas Scholar, and Asia 21 Young Leader at the Asia Society.
Stefan is a Professor of management information systems in the Department of Management, Technology, and Economics at ETH Zurich. His group develops, implements, and evaluates new Artificial Intelligence (AI) tools to solve real-world challenges and to make a profound impact in our daily lives. Examples include AI tools for monitoring the COVID-19 epidemic, for managing global development aid flows, for identifying and mitigating fake news, and for effectively detecting health risks in diabetes patients. In his research, Stefan partnered with global industry players such as Foursquare, Thomson Reuters, ABB Hitachi, or Siemens to bring AI into practice. Stefan has further been an advisor of the SDG Financing Lab of the OECD, and a member of a COVID-19 working group of the World Health Organization (WHO). After obtaining a PhD from the University of Freiburg, he was a strategy consultant with McKinsey, and a visitor at Carnegie Mellon University, Pittsburgh; the University of Texas at Austin; the University of New South Wales, Sydney; and the National Institute of Informatics, Tokyo.
Kerstin N Vokinger
Kerstin N Vokinger is a Professor at the University of Zurich, Faculty Associate at the Berkman Klein Center (Harvard University), and Affiliated Faculty at Harvard Medical School (Program on Regulation, Therapeutics, and Law). In her research, she focuses with her team on interdisciplinary questions at the intersection of law, medicine, and technology, with the goal to improve access to medicine and technology. In her areas of expertise, she also advises governmental authorities and international organizations. Kerstin studied in parallel law (JD) and medicine (MD), and conducted a PhD at the University of Zurich. She also completed a Masters of Law (LLM) at Harvard Law School, was a Visiting Fellow at the Berkman Klein Center, and a Postdoc Fellow at Harvard Medical School.