---
title: "Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems"
authors:
  - "Pulkit Verma"
doi: "10.24963/ijcai.2021/693"
url: "https://ijcai.org/proceedings/2021/693"
pdf: "https://ijcai.org/proceedings/2021/0693.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4923-4924"
track: "Doctoral Consortium"
keywords:
  - "Knowledge Representation and Reasoning: Action, Change and Causality"
  - "Planning and Scheduling: Model-Based Reasoning"
---

# Data Efficient Algorithms and Interpretability Requirements for Personalized Assessment of Taskable AI Systems

Pulkit Verma

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4923-4924. DOI: [10.24963/ijcai.2021/693](https://doi.org/10.24963/ijcai.2021/693)

## Abstract

The vast diversity of internal designs of black-box AI systems and their nuanced zones of safe functionality make it difficult for a layperson to use them without unintended side effects. The focus of my dissertation is to develop algorithms and requirements of interpretability that would enable a user to assess and understand the limits of an AI system's safe operability. We develop an assessment module that lets an AI system execute high-level instruction sequences in simulators and answer the user queries about its execution of sequences of actions. Our results show that such a primitive query-response capability is sufficient to efficiently derive a user-interpretable model of the system in stationary, fully observable, and deterministic settings.