Mihir Kshirsagar

Mihir Kshirsagar
  • Lecturer

Contact Information

  • office Address:

    600 Jon M. Huntsman Hall
    3730 Walnut Street
    Philadelphia, PA 19104

Overview

I work with a group of interdisciplinary scholars at Princeton’s Center for Information Technology Policy helping build the intellectual infrastructure for a healthy relationship between digital technology and society.

At CITP, I run our tech policy clinic that trains the next-generation of public-spirited technologists. My research focuses on how we can use technology ethically to promote accountability and empower users.

In particular, I study the following broad questions with the goal of developing pragmatic responses to these difficult challenges:

  • how do we protect consumers from being exploited by online services?
  • how do we promote the responsible use of machine learning?
  • how should we design products and services that treat individuals with dignity?
  • how do we ensure that the power of online gatekeepers are held in check?
  • how do we create online spaces that respect democratic values?

I welcome opportunities to talk to students about their interest in tech policy and potential career options.

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Teaching

Current Courses

  • LGST242 - Big Data, Big Resp.

    Significant technologies always have unintended consequences, and their effects are never neutral. A World of ubiquitous data, subject to ever more sophisticated collection, aggregation, and analysis, creates massive opportunities for both financial gain and social good. It also creates dangers in areas such as privacy, security, discrimination, exploitation, and inequality, as well as simple hubris about the effectiveness of management by algorithm. Firms that anticipate the risks of these new practices will be best positioned to avoid missteps. This course introduces students to the legal, policy, and ethical dimensions of big data, predictive analytics, and related techniques. It then examines responses-both private and governmental-that may be employed to address these concerns.

    LGST242001 ( Syllabus )

  • LGST642 - Big Data, Big Resp.

    Significant technologies always have unintended consequences, and their effects are never neutral. A world of ubiquitous data, subject to ever more sophisticated collection, aggregation, and analysis, creates massive opportunities for both financial gain and social good. It also creates dangers in areas such as privacy, security, discrimination, exploitation, and inequality, as well as simple hubris about the effectiveness of management by algorithm. Firms that anticipate the risks of these new practices will be best positioned to avoid missteps. This course introduces students to the legal, policy, and ethical dimensions of big data, predictive analytics, and related techniques. It then examines responses-both private and governmental-that may be employed to address these concerns.

    LGST642001 ( Syllabus )

Past Courses

  • LGST242 - BIG DATA, BIG RESP.

    Significant technologies always have unintended consequences, and their effects are never neutral. A World of ubiquitous data, subject to ever more sophisticated collection, aggregation, and analysis, creates massive opportunities for both financial gain and social good. It also creates dangers in areas such as privacy, security, discrimination, exploitation, and inequality, as well as simple hubris about the effectiveness of management by algorithm. Firms that anticipate the risks of these new practices will be best positioned to avoid missteps. This course introduces students to the legal, policy, and ethical dimensions of big data, predictive analytics, and related techniques. It then examines responses-both private and governmental-that may be employed to address these concerns.

  • LGST642 - BIG DATA, BIG RESP.

    Significant technologies always have unintended consequences, and their effects are never neutral. A world of ubiquitous data, subject to ever more sophisticated collection, aggregation, and analysis, creates massive opportunities for both financial gain and social good. It also creates dangers in areas such as privacy, security, discrimination, exploitation, and inequality, as well as simple hubris about the effectiveness of management by algorithm. Firms that anticipate the risks of these new practices will be best positioned to avoid missteps. This course introduces students to the legal, policy, and ethical dimensions of big data, predictive analytics, and related techniques. It then examines responses-both private and governmental-that may be employed to address these concerns.

Knowledge@Wharton

Why U.S. Multinationals May Increase Profit-shifting

Tax reforms proposed as part of the budget reconciliation process would have important consequences for U.S. multinationals’ profit-shifting incentives, and also their competitiveness, according to an analysis by the Penn Wharton Budget Model.

Knowledge @ Wharton - 10/19/2021
Leadership 2.0: Gain New Skills to Meet New Challenges

Learning to lead is not a “one and done” assignment. In this Nano Tool for Leaders, Wharton’s Mike Useem explains how to gain new skills to keep pace with change.

Knowledge @ Wharton - 10/19/2021
What Will It Take to Curb Insider Trading?

Wharton's Daniel Taylor believes legislative changes are needed to get insider trading under control and reform Wall Street’s image.

Knowledge @ Wharton - 10/18/2021