Podcast (7)

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Find narratives by ethical themes or by technologies.

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Themes
  • Privacy
  • Accountability
  • Transparency and Explainability
  • Human Control of Technology
  • Professional Responsibility
  • Promotion of Human Values
  • Fairness and Non-discrimination
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Technologies
  • AI
  • Big Data
  • Bioinformatics
  • Blockchain
  • Immersive Technology
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  • Year
    • 1916 - 1966
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    • 2019 - 2069
  • Duration
  • 27 min
  • Cornell Tech
  • 2019
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Quantifying Workers

Podcast about worker quantification in factors such as hiring, productivity and more. Dives into the discussion on why we should attempt a fair making of algorithms. Warns specifically about how algorithms can find “proxy variables” to approximate for cultural fits like race or gender even when the algorithms is supposedly controlled for these factors.

  • Cornell Tech
  • 2019
  • 28 min
  • Cornell Tech
  • 2019
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Algorithms in the Courtroom

Pre-trial risk assessment is part of an attempted answer to mass incarceration. Data sometimes answers a different question than the ones we’re trying to answer (data based on riskiness before incarceration, not how dangerous they are later). Essentially, technologies and algorithms which end up in contexts of social power differentials can often be abused to further cause injustice against people accused of a crime, for example. Numbers are not neutral and can even be a “moral anesthetic,” especially if the sampled data has confounding variables that collectors ignore. Engineers designing technology do not always envisage ethical questions when making decisions that ought to be political.

  • Cornell Tech
  • 2019
  • 27 min
  • Cornell Tech
  • 2019
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Teaching Ethics in Data Science

Solon Barocas discusses his relatively new course on ethics in data science, following a larger trend of developing ethical sensibility in this field. He shares ideas of spreading out lessons across courses, promoting dialogue, and making sure we are really analyzing problems while learning to stand up for the right thing. Offers a case study of technological ethical sensibilities through questions raised by predictive policing algorithms.

  • Cornell Tech
  • 2019
  • 41 min
  • The New York Times
  • 2021
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Sexism and Racism in Silicon Valley

In this podcast episode, Ellen Pao, an early whistleblower on gender bias and racial discrimination in the tech industy, tells the story of her experience suing the venture capital firm Kleiner Perkins for gender discrimination. The episode then moves into a discussion of how Silicon Valley, and the tech industry more broadly, is dominated by white men who do not try to deeply understand or move toward racial or gender equity; instead, they focus on PR moves. Specifically, she reveals that social media companies and CEOs can be particularly performative when it comes to addressing racial or gender inequality, focusing on case studies rather than breeding a new, more fair culture.

  • The New York Times
  • 2021
  • 51 min
  • TechCrunch
  • 2020
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Artificial Intelligence and Disability

In this podcast, several disability experts discuss the evolving relationship between disabled people, society, and technology. The main point of discussion is the difference between the medical and societal models of disability, and how the medical lens tends to spur technologies with an individual focus on remedying disability, whereas the societal lens could spur technologies that lead to a more accessible world. Artificial Intelligence and machine learning is labelled as inherently “normative” since it is trained on data that comes from a biased society, and therefore is less likely to work in favor of a social group as varied as disabled people. There is a clear need for institutional change in the technology industry to address these problems.

  • TechCrunch
  • 2020
  • 35 min
  • Wired
  • 2021
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How Tech Transformed How We Hook Up—and Break Up

In this podcast, interviewees share several narratives which discuss how certain technologies, especially digital photo albums, social media sites, and dating apps, can change the nature of relationships and memories. Once algorithms for certain sites have an idea of what a certain user may want to see, it can be hard for the user to change that idea, as the Pinterest wedding example demonstrates. When it comes to photos, emotional reactions can be hard or nearly impossible for a machine to predict. While dating apps do not necessarily make a profit by mining data, the Match monopoly of creating different types of dating niches through a variety of apps is cause for some concern.

  • Wired
  • 2021
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