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Author: Block Center for Technology and Society at Carnegie Mellon University

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Consequential is a podcast that looks at the human side of technological change and develops meaningful plans of action for policymakers, technologists and everyday people to build the kind of future that reduces inequality, improves quality of life and considers humanity. Over the course of the first season, we will unpack important topics like industry disruption, algorithmic bias, human-AI collaboration, re-skilling and the future of work, as well as discuss policy interventions for using emerging technologies for social good.

Consequential is hosted at Carnegie Mellon University's Block Center for Technology and Society. For information on upcoming episodes, visit:
27 Episodes
Open source software is the infrastructure of the Internet, but it is less diverse than the tech industry overall. In this deep-dive on gender in open source, we speak to CMU’s Laura Dabbish and Anita Williams Woolley about what’s keeping women from participating in open source software development and how increased participation benefits society as a whole.
From helping to identify tumors to guiding trading decisions on Wall Street, artificial intelligence has begun to inform important decision-making, but always with the input of a human. However, not all humans respond the same way to algorithmic advice. This episode of Consequential looks at human-in-the-loop AI, with guests Sumeet Chabria, David Danks, and Maria De-Arteaga.
The Enron emails helped give us spam filters, and many natural language processing and fact-checking algorithms rely on data from Wikipedia. While these data resources are plentiful and easily accessible, they are also highly biased. This week, we speak to guests Amanda Levendowski and Katie Willingham about how low-friction data sources contribute to algorithmic bias and the role of copyright law in accessing less troublesome sources of knowledge and data.
Peer review is the backbone of research, upholding the standards of accuracy, relevance and originality. However, as innovation in the fields of AI and machine learning has reached new heights of productivity, it has become more difficult to perform peer review in a fast and fair manner. Our hosts are joined by Nihar Shah to unpack the question of automation in the scientific publication process: could it help, is it happening already, and what does it have in common with the job application process?
We're taking a day off today from our episode and will be back in December. Have a great holiday weekend!
Traditional scientific research has a data diversity problem. Online platforms, such as Mechanical Turk, give researchers access to a wider variety and greater volume of subjects, but they are not without their issues. Our hosts are joined by experts David S. Jones, Ilka Gleibs, and Jeffrey Bigham to discuss the pros and cons of knowledge production using crowdsourced data.
Disinformation is as old as the printing press, if not older. So what has accelerated its spread now, and what can be done to stop it? On this special bonus episode of Consequential, we speak to the experts about disinformation, the election, and COVID-19.
In the first episode of Season 3 of Consequential, hosts Eugene and Lauren look at how underlying biases in the development of the EEG have impacted healthcare, medical technology, and scientific research, with guests Ben Amaba, Arnelle Etienne, Pulkit Grover, and Shawn Kelly.
In Season 3 of Consequential, hosts Eugene and Lauren will be exploring knowledge production in the Information Age. Beginning on October 21, this season will examine how AI and machine learning will impact research practices and data collection, as well as the development and dissemination of knowledge. Topics will include combatting disinformation, the ethics of crowdsourced research, and representation in open source software development.
Today we're asking our experts: how do you coordinate a crisis response to an issue like COVID-19, where every public health decision has economic ramifications, and every economic decision has a direct impact on public health? To answer these questions, we speak to Dean Ramayya Krishnan of Heinz College; Professor of Machine Learning and Public Policy, Rayid Ghani; and Distinguished Service Professor Richard Stafford.
With consideration to the events of the past week and in order to hold space for the voices that are boldly challenging systemic racism and injustice, we have decided to postpone the release of our new episode. We would also like to echo the sentiment expressed by Carnegie Mellon's President Farnam Jahanian, that it is up to each one of us – no matter our background – to confront and dismantle racism and injustice wherever they exist.
Can teams still be effective when working together remotely? Is working from home the future of work? In this week’s episode, hosts Eugene and Lauren talk to Professor Anita Williams Woolley of Carnegie Mellon’s Tepper School of Business to learn about how communication and collaboration change once teams are no longer face-to-face, and we hear from people in a variety of fields about their experience working remotely.
In the span of just two weeks, the entire American higher education system moved online due to COVID-19. While this is often considered a temporary measure, the truth is that higher ed may never fully go back to normal. And in some regards, we may not want it to. In this week’s episode, hosts Eugene and Lauren talk to professors across the United States about the future of higher education.
How will certain new standards for data sharing and surveillance during the COVID-19 pandemic impact the future of healthcare? In episode two of Consequential's two-part deep-dive on pandemics, public health and privacy, hosts Eugene and Lauren talk to David S. Jones of Harvard University and Henry Kautz of the National Science Foundation about the impact of big data on health and privacy.
Mobile data records, tracking devices and government-mandated selfies have played a large role in both enforcing quarantines and providing data to better understand the coronavirus. In this week’s episode of Consequential, hosts Eugene and Lauren talk to Wilbert Van Panhuis, an infectious disease epidemiologist at the University of Pittsburgh; Tom Mitchell, the Lead Technologist of the Block Center and a computer scientist at Carnegie Mellon University; and Scott Andes, the Executive Director of the Block Center, about the importance of collecting and using data for public health, the individual privacy concerns that arise as a result of this data collection, and the challenges of striking a balance between societal benefit and personal privacy. This episode is part one of a two-episode look on large-scale public health data analytics.
In light of recent developments related to COVID-19, we have decided to push back our second season to focus instead on what we can learn from the coronavirus in terms of technology and society. In our mini-season, we will cover the use of large-scale public health data, remote education, and the future of work. 
A Policy Roadmap

A Policy Roadmap


Over the last 9 episodes, we’ve presented a variety of questions and concerns relating to the impacts of technology, specifically focusing on artificial intelligence. To end season 1, we want to take a step back and lay out a policy roadmap that came together from the interviews and research we conducted. We will outline over 20 different steps and actions that policymakers can take, starting with laying the necessary foundations to applying regulatory frameworks from other industries to novel approaches.  
Paging Dr. Robot

Paging Dr. Robot


Don’t worry, your next doctor probably isn’t going to be a robot. But as healthcare tech finds its way into both the operating room and your living room, we’re going to have to answer the kinds of difficult ethical questions that will also determine how these technologies could be used in other sectors. We will also discuss the importance of more robust data-sharing practices and policies to drive innovation in the healthcare sector.
The Future of Work

The Future of Work


If artificial intelligence can do certain tasks better than we can, what does that mean for the concept of work as we know it? We will cover human-AI collaboration in the workplace: what it might look like, what it could accomplish and what policy needs to be put in place to protect the interests of workers.  
The World Economic Forum has found that while automation could eliminate 75 million jobs by 2022, it could also create 133 million new jobs. In this episode, we will look at how to prepare potentially displaced workers for these new opportunities. We will also discuss the “overqualification trap” and how the Fourth Industrial Revolution is changing hiring and credentialing processes.
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