Algorithmic Bias in Autonomous Systems
Authors: David Danks, Alex John London
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In this paper, we have developed a taxonomy of different kinds and sources of algorithmic bias in an attempt to isolate possible reasons or causes. |
| Researcher Affiliation | Academia | David Danks1,2 and Alex John London1,3 1-Department of Philosophy; 2-Department of Psychology; 3-Center for Ethics and Policy Carnegie Mellon University, Pittsburgh, USA {ddanks, ajlondon}@andrew.cmu.edu |
| Pseudocode | No | The paper is a conceptual work presenting a taxonomy and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not describe or release any source code for a methodology. |
| Open Datasets | No | The paper discusses 'training data bias' as a conceptual source of algorithmic bias within autonomous systems. However, the authors did not use or provide access to any dataset for training within their own research. For example: 'If the vehicle s training data and information come entirely or mostly from one location or city...then the resulting models will undoubtedly be biased...' |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers for its own research. |
| Experiment Setup | No | The paper is theoretical and does not provide details on experimental setup or hyperparameters. |