=Paper=
{{Paper
|id=Vol-2786/Paper21
|storemode=property
|title=Knowledge Representation for Algorithmic Auditing to Detangle Systemic Bias
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper21.pdf
|volume=Vol-2786
|authors=Paola Di Maio
|dblpUrl=https://dblp.org/rec/conf/isic2/Maio21
}}
==Knowledge Representation for Algorithmic Auditing to Detangle Systemic Bias==
<pdf width="1500px">https://ceur-ws.org/Vol-2786/Paper21.pdf</pdf>
<pre>
                                                                                                                                          149-157




Knowledge Representa on for Algorithmic Audi ng to Detangle
Systemic Bias

Paola DiMaio
Center for Systems, Knowledge Representation and Neuroscience

          Abstract: Knowledge is central to cognition, and adequate knowledge representation is necessary to ensure the
          correct functioning of intelligent systems leveraging knowledge. Knowledge auditing methodologies in use,
          however do not typically address the requirements for evaluating the consistency of logical and functional
          knowledge representation constructs, as used in AI, that support the correct execution of system processes. This
          paper puts forward the notion that algorithmic bias results from the lack of adequate knowledge representation
          mechanisms, and is part of systemic bias. The notion of systemic bias is identified, characterized, and described
          as an emergent relation resulting from the combination of other factors between knowledge representation (KR)
          and bias in AI. These factors are here named conceptualised, related to each other and visualised. The resulting
          artefacts are then incorporated in the KAF (Knowledge Audit Framework) methodology (first published in 2012)
          and adopted as a reference model for understanding and representing bias as a systemic emergent phenomenon.


Table of Contents
1. Introduction and Motivation
                                                                                            5. Knowledge Representation For Algorithmic Audits
2. Semantics and Knowledge Representation
                                                                                                    5.1. Criteria for Auditing KR
3. Accountability And Algorithmic Bias
                                                                                                    5.2. Levels of Representation
           3.1. Disentangling Systemic Bias
                                                                                                    5.3. Adequacy
           3.2. Knowledge Representation
                                                                                                    5.4 Algorithmic Auditability
           3.3. Deep fakes As Knowledge
           Misrepresentation                                                                6. Adding KR To KAF

4. Auditing: Data, Knowledge, Algorithms                                                    7. Conclusions and Future Work

            4.1. Data Auditing                                                               The full research paper is available as an open access
                                                                                            resource via Open access repositories or by contacting
            4.2. Knowledge Auditing                                                         the author.

            4.3. The Knowledge Audit Framework                                              Paola.dimaio@gmail.com

            4.4. Algorithm Auditing

______________________________
ISIC’21:International Semantic Intelligence Conference, February
25–27, 2021, New Delhi, India
✉ : paola.dimaio@gmail.com (P.D. Maio)

                 Copyright © 2021 for this paper by the authors. Use permitted
                 underCreative Commons License Attribution 4.0 International (CC BY 4.0).
                 CEUR Workshop Proceedings (CEUR-WS.org)

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