Machine Learning and The Social and Ethical Responsibilities of Computing


Machine Learning (ML) is a huge field of study and not all of it will be applicable to you if your only focus is to solve a problem. Let us start by explaining ML in basic terms.

What is Machine Learning

Machine Learning (ML) is an area within computer science concerned with programs that can learn. It is the ability of a machine to mimic intelligent human behavior by learning from past data without being programmed.

What is Artificial Intelligence?

Machine Learning is a subset or a branch of Artificial Intelligence (AI). AI overlaps with Machine Learning.

AI is also a part of computer science, but it is concerned with creating and building programs that are intelligent or can perform intelligent actions. It is a larger concept to build intelligent machines that simulate human behavior and thinking capability.

Social and Ethical Responsibilities of Computing (SERC) framework

The Social and Ethical Responsibilities of Computing (SERC) enables the growth of accountable “habits of action and mind” for those who build and install computing technologies and nurtures the development of technologies in the public interest.

With help from the Social and Ethical Responsibilities of Computing (SERC) framework, students can now think about the consequences of these Artificial Intelligence tools, which have their share of adverse impacts.

By utilizing a teaching, study, and engagement framework, SERC is working towards the following:

  • Training students.
  • Encouraging research to inspect the wide range of opportunities and challenges associated with computing.
  • Improving policies, designs, implementation, and impacts.

SERC is led by Julie Shah, professor of astronautics and aeronautics and head of the Interactive Robotics Group at CSAIL and by associate deans David Kaiser, the Germeshausen Professor of physics and Professor of the History of Science.

Last winter, a team of SERC Scholars led by Leslie Kaelbling, the Panasonic Professor of Engineering and Computer Science, and the MIT course 6.036 teaching assistants worked to permeate weekly labs with material comprising ethical computing, model, data bias, and fairness in ML. Jacob Andreas, the X Consortium Assistant Professor in the Department of Electrical Engineering and Computer Science initiated the process in the fall of 2019.

SERC Scholars work together in multidisciplinary teams to help the faculty create new course material.

SERC Framework

  • Teaching

    • Collaborative curriculum: Create academic materials that can be included in existing classes across all levels of instruction.
    • Case studies: Publishing a series of peer-reviewed MIT Case Studies in Social and Ethical Responsibilities of Computing.
    • Active learning projects: In-class demonstrations and original homework assignments specially designed by interdisciplinary teams.
  • Research

    • Community of Research: The SERC Scholars program is open to MIT undergraduates, graduate students, and postdocs from across the Institute.
    • Research Catalyst: SERC helps to connect researchers across MIT.
    • Infrastructure: Building on references from the SERC Action Group on Data, and Anti-racism and including experience and insights from the ethical, legal, and equity committee for MIT Campus Planning.
  • Broader Engagements

    • Policy Task Forces
    • Public Forums
    • Legal, Ethical, and Equity Committee for MIT Campus Planning


SERC believes that it is our responsibility to make sure that things do not go out of hand and that we should all be able to do it. So, let us start asking questions and be accountable for our actions.