skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Chockler, Hana"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; (, International Joint Conferences on Artificial Intelligence Organization)
    We focus on explaining image classifiers, taking the work ofMothilal et al. 2021 (MMTS) as our point of departure. We observe that, although MMTS claim to be using the definition of explanation proposed by Halpern 2016, they do not quite do so. Roughly speaking, Halpern’s definition has a necessity clause and a sufficiency clause. MMTS replace the necessity clause by a requirement that, as we show, implies it. Halpern’s definition also allows agents to restrict the set of options considered.While these difference may seem minor, as we show, they can have a nontrivial impact on explanations.We also show that, essentially without change, Halpern’s definition can handle two issues that have proved difficultfor other approaches: explanations of absence (when, for example, an image classifier for tumors outputs “no tumor”) and explanations of rare events (such as tumors). 
    more » « less
    Free, publicly-accessible full text available November 1, 2025
  2. ; (, Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-21))
    Consider a bank that uses an AI system to decide which loan applications to approve. We want to ensure that the system is fair, that is, it does not discriminate against applicants based on a predefined list of sensitive attributes, such as gender and ethnicity. We expect there to be a regulator whose job it is to certify the bank's system as fair or unfair. We consider issues that the regulator will have to confront when making such a decision, including the precise definition of fairness, dealing with proxy variables, and dealing with what we call allowed variables, that is, variables such as salary on which the decision is allowed to depend, despite being correlated with sensitive variables. We show (among other things) that the problem of deciding fairness as we have defined it is co-NP-complete, but then argue that, despite that, in practice the problem should be manageable. 
    more » « less
    Accepted Manuscript Full Text Available