Consequential

While infrastructure may have taken center stage in this year's policy discussions, the United States has been trying to figure out what to do about our infrastructure for a long time. This week's episode looks at the current state of our infrastructure, as well as past and future infrastructure reform, with guests Price V. Fishback, Jodi Sandfort, and Ramayya Krishnan.

What are the long-term impacts of targeted investments in our physical and human infrastructure? Beginning November 24, Season 4 of the Consequential Podcast will examine how policymaking in such areas as public transportation, energy, and workforce development will affect our future.

In the age of the Internet, a lot of information is at our fingertips. But is it accessible, reliable and up-to-date? In the season finale of Consequential, we're discussing information inequality with guests Asia Biega, Stephen Caines, and Myeong Lee.

Natural language processing is the branch of artificial intelligence that allows computers to recognize, analyze and replicate human language. But when it's hard enough for humans to say what they mean most of the time, it's even harder for computers to get it right. Even when they do, we might not like what we hear. In this week's episode looks at sentiment analysis, search engine prediction, and what AI and human language can teach us about each other, with guests Alvin Grissom II of Haverford College and Alexandra Olteanu of Microsoft Research.

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.